Volatility Cycles of Output and Inflation: A Good Shock ... - CiteSeerX

Volatility Cycles of Output and In‡ation: A Good Shock, Bad Shock Story Szilárd Benk Magyar Nemzeti Bank and Central European University Max Gillmany Cardi¤ Business School, and Institute of Economics HAS Michal Kejak CERGE-EI February 27, 2008

Abstract We explain the close correlation of volatilities of GDP growth and in‡ation over the 1919-2004 period, using credit and money shocks that have "bad" and "good" e¤ects that are de…ned in terms of their e¤ects on the spectral variation in GDP. With these shocks, plus standard TFP productivity shocks, we identify, characterize and contrast the two great volatility cycles over the historical period, within an endogenous growth monetary business cycle with micro-based banking production. The Great Moderation post-1983 coincided with good credit shocks from deregulation, which allowed money velocity to diverge from GDP and in‡ation volatilities, while the Great Depression was faced with bad money and credit shocks that tied together velocity volatility with GDP and in‡ation volatility. Keywords: Volatility, business cycle, credit shocks, velocity. We thank Daniel Levy, Patrick Minford, Casper de Vries, seminar participants at Ben Gurion University, Cardi¤ University, and Reading University, participants at the 2006 EMF conference, Amsterdam, the 2007 CGBCR Manchester conference, and the 2007 Bank of England Macro-Stability conference; the authors gratefully acknowledge joint support for the project from a World Bank GDN RRC IV grant. y Corresponding author: Cardi¤ Business School, Aberconway Drive, Cardi¤ CF11 3EU, UK; [email protected].

1

JEL: E13, E32, E44 Preliminary Draft: Incomplete

2

1

Introduction

US GDP and in‡ation rate volatility rose to higher levels from the 1960s through the mid-1980s. The decrease in GDP volatility at the end of this period is know now as the Great Moderation. However historical data shows that there was a much larger rise and decline in these two volatilities from 1927 to 1940, which …nally dropped to low levels in the 1950s. This much larger cycle encompassed the Great Depression and WWII. This paper shows a new perspective on the signi…cant US macroeconomic stability experienced since 1983, in terms of the lower GDP and in‡ation volatility. It does this by identifying, analyzing and comparing the two major rises and declines of these volatilities since 1919 using a stochastic endogenous growth monetary economy. We show what percent of each the GDP growth rate and the in‡ation rate variation is due to each of three di¤erent shocks, on goods productivity (TFP), money supply growth rates, and credit sector productivity. We decompose this variation across spectral frequencies of the short run, business cycle and long run as in Levy and Dezhbakhsh (2003) (Appendix A.2). And this variance decomposition is done for the entire 1919-2004 period, for various subperiod divisions, and for exogenous growth to provide a comparison to our endogenous growth baseline model. And we similarly explain money velocity variation as it is a determinate of in‡ation, and it helps corroborate features of the major volatility swings of GDP growth and in‡ation rates. To present a cohesive story, …rst we set out the historical trends that we use in the analysis (Section 2), and the endogenous growth monetary economy, as found in Benk, Gillman, and Kejak (2007)1 (Section 3). We compare the model’s simulated behavior to facts, for example as in Cooley and Hansen (1995) (Section 4). Then, we construct, or "back out", the shocks from the equilibrium solutions of our model as combined with time series data, as does Parkin (1988) and Ingram, Kocherlakota, and Savin (1994) (Referee Appendix B.1, with actual shocks displayed in Appendix B.2). The analysis focuses on money and credit shocks, as in Bernanke and Blinder (1988). We demonstrate how these shocks a¤ect the level and variance of 1

Benk, Gillman, and Kejak (2007) explains only US velocity in the post-1972 quarterly period. Relatedly, Harrison and Weder (2008) …nd support for an exogenous growth version of this model, as presented in Benk, Gillman, and Kejak (2005), but with only the credit shocks allowed, in terms of its ability to replicate the US 1920s era, at business cycle frequencies.

1

GDP growth, in‡ation and velocity (Section 5). The short run money e¤ects are described in terms of transition dynamics with Phillips-like behavior, and the long run money e¤ects are described in terms of endogenous growth e¤ects from the in‡ation tax. (Section 6). We …nd that money and credit shocks are important in explaining GDP growth, in‡ation, and velocity volatility, along with TFP shocks. Further, the money and credit shocks a¤ect di¤erent parts of the spectrum of GDP volatility during the pre-war versus the post war periods. This source of volatility from money is due more to short run, Phillips-like, transition dynamics during 1919-1939, and more from longer run in‡ation-tax factors during 1986-2004. The contrasting short and long run e¤ects provide the basis for a bad and good shock characterization. Short run GDP e¤ects result from transition dynamics that become prominent when repeatedly embarked upon. This creates greater GDP volatility, and in this sense are bad. Long run e¤ects involve more of the equilibrium e¤ects of persistent changes in productivity or money supply growth, and less transition dynamics; these are associated with less volatility and, in this sense, are good. Credit shocks likewise behave with di¤ering short and long run e¤ects across the periods. Credit shocks combine relatively low credit shock volatility with greater short run GDP volatility e¤ects, when credit is constrained during 1919-1939; credit shocks combine relatively high credit shock volatility with greater long run sectoral productivity e¤ects and lower GDP volatility, when credit is liberalized post-1983. The prewar "bad" credit shocks, with a relatively greater short run impact, appear to have contributed to greater GDP volatility during the Depression, when constraints on exchange credit allowed the money supply volatility to translate into in‡ation and GDP volatilities. The post-1983 "good" credit shocks, with a greater long run impact on GDP at a time when GDP exhibits less volatility, are consistent with …nancial liberalization facilitating less GDP volatility, as in Jermann and Quadrini (2006). Here the interpretation is that, with money and credit as substitutes, the …nancial deregulation allowed monetary aggregate volatility to be manifested through higher credit volatility rather than becoming higher in‡ation and GDP volatility (Sections 5 and 6).

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15.0%

8.0% 6.0%

10.0%

4.0% 5.0%

2.0%

0.0%

0.0% -2.0%

-5.0%

-4.0% -10.0%

-6.0%

Inflation

2003

1999

1995

1991

1987

1983

1979

1975

1971

1967

1963

1959

1955

1951

1947

1943

1939

1935

1931

1927

1923

-8.0% 1919

-15.0%

Volatility(Inflation)

Figure 1: In‡ation Rate Level and its Volatility 1919-2004

2

Data Description: Historical Trends

The data is described in Appendix A.1. There are six historical stylistic trends that we identify here and use in the analysis. 1) The in‡ation rate level and its volatility move together, as shown in Figure 1. This gives a sense in which discussion of in‡ation volatility is synonymous with discussion of in‡ation rate levels.2 2) In‡ation volatility and GDP volatility each had a large up and down movement in the pre-War period that lasted that troughed after WWII, that exist during 1919 to 1939 even if the war period 1940-1950 is dropped, and that were much larger than the rise and fall of these volatilities from the 1960s to the 1990s; see Figure 2. 3) Also in Figure 2, money velocity volatility and M1 growth rate volatility moved together and broadly followed GDP and in‡ation volatilities up until 1983, when they together sharply diverged from the other two. 4) GDP and the normalized in‡ation volatilities move together throughout the entire historical period except WWII (0.82 correlation), as shown in Figure 3. Here the in‡ation volatility is scaled by multiplying it by the average proportion di¤erence between it and the GDP volatility for the 1919-2004 2

Volatility is calculated as the standard deviation of the variable over a certain window. For annual data it is a 7 year window; the formula is volatility(xt ) = SD(xt

k ; xt k+1 ; :::xt ; :::; xt+k 1; xt+k );

where k = 3 for annual data.

3

14.0% corr(V(velocity),V(GDP)) = 0.87 (1919-1939 & 1951-1983) corr(V(velocity),V(GDP)) = -0.16 (1984-2004) corr(V(velocity),V(grM1)) = 0.90 (1940-1950 excl)

12.0% 10.0% 8.0% 6.0% 4.0% 2.0%

Volatility(GDP) Volatility(Inflation)

2003

1999

1995

1991

1987

1983

1979

1975

1971

1967

1963

1959

1955

1951

1947

1943

1939

1935

1931

1927

1923

1919

0.0%

Volatility(Velocity) Volatility(grM1)

Figure 2: Volatilities of GDP, In‡ation, Velocity and M1 Growth 1919-2004

period. 20% 18%

corr(V(GDP),V(Inflation)) = 0.73 corr(V(GDP),V(Inflation)) = 0.82

16%

(1940-1950 excl)

14% 12% 10% 8% 6% 4% 2%

Volatility(GDP)

2003

1999

1995

1991

1987

1983

1979

1975

1971

1967

1963

1959

1955

1951

1947

1943

1939

1935

1931

1927

1923

1919

0%

Volatility(Inflation) [scaled]

Figure 3: Scaled In‡ation Volatility Versus GDP Volatility, 1919-2004

5) The GDP level volatility and the GDP growth rate volatility are very similar (0.90 correlation), allowing us to discuss them with some interchangeability. Figure 4 shows that both move with the in‡ation rate volatility, and that the GDP growth rate volatility is a bit more highly correlated with the in‡ation rate volatility (0.79 in growth rates and 0.73 in levels for the whole period). 6). Finally, Figure 5 shows that the GDP growth rate and the in‡ation rate level moved together from 1919-1955 (except 1945-1949), with a 0.65 4

20% 18% corr(V(grGDP),V(GDP)) = 0.90 corr(V(grGDP),V(Inflation)) = 0.79

16% 14% 12% 10% 8% 6% 4% 2%

Volatility(GDP)

Volatility(Inflation) [scaled]

2003

1999

1995

1991

1987

1983

1979

1975

1971

1967

1963

1959

1955

1951

1947

1943

1939

1935

1931

1927

1923

1919

0%

Volatility(grGDP)

Figure 4: Volatilites of GDP, the GDP Growth and Scaled In‡ation, 19192004

correlation from 1919-1939, and often in opposite directions post-1955, with a -0.25 correlation from 1951-2004.

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The Endogenous Growth Economy with Credit

The representative agent economy is as in Benk, Gillman, and Kejak (2007), an endogenous growth extension, with human capital investment causing growth as in Lucas (1988), of the monetary business cycle with exchange credit that appears in Benk, Gillman, and Kejak (2005). By combining the business cycle with endogenous growth, stationary in‡ation lowers the output growth rate as in Gillman and Kejak (2005) over the stochastic secular trend, and as supported empirically for example in Gillman, Harris, and Matyas (2004) and Karanasos and Zeng (2006). Over the business cycle, shocks cause changes in growth rates and in stationary ratios. The shocks to the goods sector productivity and the money supply growth rate are standard, while the third shock to the credit sector productivity exists by virtue of the model’s endogeneity of money velocity via a micro-based production of exchange credit. The shocks occur at the beginning of the period, observed by the consumer before the decision process, and follow a vector …rst-order autoregressive process. For goods sector productivity, zt ; the money supply growth

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20% 15% 10% 5% 0% -5%

Corr(grGDP, inflation) Full sample: 0.38 1919-1939: 0.65 1940-1950: 0.23 1951-2004: -0.25

-10% -15%

grGDP

2003

1999

1995

1991

1987

1983

1979

1975

1971

1967

1963

1959

1955

1951

1947

1943

1939

1935

1931

1927

1923

1919

-20%

Inflation

Figure 5: GDP Growth Rate Level and the In‡ation Rate Level, 1919-2004

rate, ut ; and credit sector productivity, vt : Zt =

Z Zt 1

(1)

+ "Zt ;

where the shocks are Zt = [zt ut vt ]0 , the autocorrelation matrix is Z = diag f'z ; 'u ; 'v g and 'z ; 'u ; 'v 2 (0; 1) are autocorrelation parameters, and the shock innovations are "Zt = [ zt ut vt ]0 N (0; ) : The general structure of the second-order moments is assumed to be given by the variancecovariance matrix . These shocks a¤ect the economy as described below.

3.1

Consumer Problem

Consider a representative agent that maximizes over an in…nite horizon its expected lifetime utility over consumption (ct ) and leisure (xt ). Utility is given by: U = E0

1 X

t

u(ct ; xt )

0
0 and 2 (0; 1); ct (1

at ) = qt = AF evt (ft ht ) dt1 ;

(10)

where AF evt is the stochastic factor productivity.3 Here physical capital is excluded for simplicity of computation, but more generally as in Clark (1984) this factor can enter this function. The separate factor of deposited funds goes back to Clark (1984) and Hancock (1985) and, including physical capital as well, this speci…cation is the still the standard in the intermediation services literature, with empirical veri…cation of this three-factor CRS functional form starting with Hancock. Note that without capital, this gives a functional form of the exchange constraint that is identical to a special case of the shopping time model, but in this case is a "banking time" model.4 And the resulting money demand function has an interest elasticity that rises in magnitude with the nominal interest rate, as in the Cagan (1956) model, rather than being constant as in a typical shopping time speci…cation. Gillman and Kejak (2005) show that this Cagan feature is essential in order to explain the long run negative e¤ects of the in‡ation tax on output growth. Since all means of exchange, both money and credit, are ultimately drawn +qt = dt : Substituting in ct = dt from the deposits at the bank, then ct = MPt+1 t 3

Gillman, Harris, and Kejak (2006) lay out a fully decentralized version of …nancial intermediation using the same exchange credit production function. 4 Solve for at is equation (11) and use the solution to substitute for at in equation (cia) to get a single exchange constraint that is a special case of the shopping time constraint as extended to endogenous growth. Functionally, it can be derived that banking time ft ht depends on real money Mt =Pt mt and ct ; with the same derivatives signs (negative and positive respectively) as in the shopping time model: ft ht = g(mt ; ct ); with g1 < 0 and g2 > 0: But also note that solving for ft = g^(mt ; ct ; ht ); in order to use the allocation of time constraint (4) to substitute for xt in the utility function, it is clear that Feenstra’s (1985) equivalence result between money-in-the-utility function and shopping time does not hold here because ht would then inappropriately enter the utility function along with ct and mt (as well as lt and nt !):; this immediate non-equivalence is a result of the endogenous growth setting.

8

into equation (10), and dividing by ct ; gives the share of credit in the total exchange for consumption:5 qt = (1 ct

3.2

ft ht ct

at ) = AF evt

(11)

:

Producer Problem

The …rm maximizes pro…t given by yt wt lt ht rt sGt kt ; subject to a standard Cobb-Douglas production function in e¤ective labor and capital. This is given as yt = G(sGt kt ; lt ht ; zt ) = AG ezt (sGt kt )1

(lt ht ) :

(12)

The …rst order conditions for the …rm’s problem yield the following expressions for the wage rate and the rental rate of capital: wt = AG e

rt = (1

3.3

1

sGt kt lt ht

zt

)AG ezt

(13)

;

sGt kt lt ht

(14)

:

Government Money Supply

It is assumed that the government policy includes sequences of nominal transfers which satisfy: Tt =

t Mt

=(

+ eut

1)Mt ;

t

where t is the growth rate of money and of money.

= [Mt

Mt 1 ]=Mt 1 :

(15)

is the stationary growth rate

5 Solving from equations (9) and (11) for ft ht in terms of mt and ct ; this e¤ective "banking time" is seen as a special case of the general shopping time function of ft ht = g(mt ; ct ); with g1 < 0 and g2 > 0: But solving for ft = g^(mt ; ct ; ht ); in order to use the allocation of time constraint (4) and substitute for xt in the utility function, it is clear that Feenstra’s (1985) equivalence result between money-in-the-utility function and shopping time does not hold here because ht would then inappropriately enter the utility function along with ct and mt (as well as lt and nt !); this immediate unequivalence is a result of the endogenous growth setting.

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3.4

De…nition of Competitive Equilibrium

Denote the state of the economy by s = (k; h; M; z; u; v) and by a prime (’) the next-period values. A competitive equilibrium consists of a set of policy functions c(s), x(s), l(s), n(s), f (s), sG (s), a(s), k 0 (s), h0 (s), M 0 (s), pricing functions P (s), w(s), r(s) and a value function V (s), such that: (i) households maximize utility: given the pricing functions and the policy functions, V (s) solves the functional equation (16). (ii) …rms maximize pro…ts, the functions w and r being given by (13) and (14). (iii) goods and money markets clear, in equations (8) and (15). The representative agent’s optimization problem can be written in a recursive form as: V (s) =

max

c; x; l; n; f;sG; a;k0 ;h0 ;M 0

fu(c; x) + EV (s0 )g

(16)

subject to the conditions (3)-(9) and (11).

3.5

E¤ect of Shocks on Balanced-Growth Path

Equilibrium conditions along the balanced-growth path are given here to describe how the shocks a¤ect the equilibrium. Rt =

+ +(

1) gt =

t

(17)

+ rt ;

~t 1+R xt = ; ct wt ht ~ t = at Rt + Rt (1 R rHt 1 + gt =

"AH

sHt kt lHt ht

1 + rHt 1+

H

(18) (19)

at ) ;

(1 ")

(1 =

(20)

xt );

1 + rt 1+

K

;

(21)

At the Friedman optimum, the nominal interest R of equation (17) equals zero and no credit is used. But as in‡ation rises, the agent substitutes from goods towards leisure while equalizing of the ratio of the shadow h the margin i ~ price of goods to leisure, xt =( ct ) = 1 + Rt = (wt ht ) ; in equation (18). Here ~ t ; as given in equation (19), is the average exchange cost per unit of output; R 10

this equals the average cost of using cash, Rt ; weighted by (at ) and the average cost of using credit, Rt ; weighted by (1 at ) : That Rt is an average cost can be veri…ed by dividing the total cost of credit production by the total output of credit production. And this total exchange cost determines how much substitution there is from money to credit, and from goods to leisure. Substitution towards leisure causes a fall in the human capital return of rHt "AH (sHt kt =lHt ht )(1 ") (1 xt ) , given in equation (20). The marginal product of physical capital rt , in equation (14), also falls, while the real wage wt in equation (13) rises, while there is a Tobin (1965)-type substitution from labor to capital across all sectors in response to the higher real wage to real interest rate ratio. The Tobin (1965) like rise in sHt kt =lHt ht mitigates but does not reverse the fall in the return to human capital rHt caused by the increase in leisure. The growth rate, in equation (21), falls as Rt rises since both rHt and rt fall. As the in‡ation rate continues to rise, the credit substitution channel allows the growth rate to decline at a decreasing rate, as more credit and less leisure are used as the substitute for the in‡ation-taxed good (Gillman and Kejak 2005). 3.5.1

E¤ects of Shocks on Growth Rate

A positive money shock increases Rt and the goods shadow price [1 + at Rt + (1 at ) Rt ] relative to the leisure shadow price wt ; induces substitution from goods (ct =ht ) towards leisure (xt ); and decreases the growth rate. A positive credit shock in reverse decreases the cost of exchange, induces substitution from xt towards ct =ht , increases the employment rate (1 xt ) and gt . A goods productivity shock directly increases rt and gt . 3.5.2

E¤ects of Shocks on In‡ation

From equations (9) and (15), write ln With 1 + that

t

Pt Pt 1

Mt+1 Mt

= ln

and 1 + gvt t

'

Pt ct vct 1 + ln + ln : Pt 1 ct 1 vct vct ; vct 1

t

(22)

then it follows from equation (22)

gt + gvc t :

(23)

Equation (23) is an identity, and can be used to account for in‡ation rate changes. A positive shock to the money supply growth rate t raises 11

directly, and has secondary e¤ects on gt and gvc t : Since this shock causes velocity to rise, and consumption growth to fall, these other factors both go in the same direction and can be thought of as further increasing the extent of the in‡ation rate increase. Credit shocks act only through the last two terms of equation (23), with a positive shock causing both velocity and consumption growth to rise, resulting in opposing e¤ects. A random increase in the goods productivity causes consumption velocity to be only slightly a¤ected (see Benk, Gillman and Kejak, 2007), while having a direct positive e¤ect on the growth rate gt . This results in a decrease in the in‡ation rate. t

4

Calibration

Parameters are chosen so that certain features of the nonstochastic steady state of the model match average values from US annual time series between 1919-2004, as much as possible, and with particular attention to points raised by Gomme and Rupert (2007) study of the two sector real business cycle model. Our human capital sector is a second sector that suggests comparison to the household sector in Gomme and Rupert. Tables 2 and 3 present the parameter values of the calibration and the target values of certain endogenous variables, respectively. The calibration sets the capital share parameter in the goods sector, 1 , equal to 0.36 as in Jones, Manuelli, and Siu (2005), the annual discount factor equal to 0.96 and log-utility is assumed, or = 1: The US average annual output growth rate, g; and the measured physical capital investment share of output for the period 1919-2004 are 1.77% and 23.8%, respectively. This implies the annual depreciation rate of capital K equal to 0.0316 . Knowing it we can determine the capital-labor ratio in the good sector, (sG k)=(lh); which in turn produces a net real interest rate of 6.7%. We choose the rate of depreciation of human capital, H ; equal to 0.025 as in Jones, Manuelli and Siu (2005), 7 and Jorgenson and Fraumeni (1989). 6

The implied value of the depreciation rate is smaller under the presence of the use of physical capital in the human capital sector since the implied relevant physical capitaloutput ratio is smaller than the overall physical capital-output ratio. 7 Gomme (1993) uses a higher value of 0.05 but sets the same depreciation rates in both sectors (i.e. 0.0128 for the quarterly value).

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Preferences 1

Relative risk aversion parameter

1.84

Leisure weight

0.96

Discount factor

Goods Production 0.64 K

AG

Labor share in goods production

0.031

Depreciation rate of goods sector

1

Goods productivity parameter

Human Capital Production

"

0.83

Labor share in human capital production

H

0.025

Depreciation rate of human capital sector

AH

0.21

Human capital productivity parameter

Banking Sector

AF

0.11

Labor share in credit production

1.1

Banking productivity parameter

0.05

Money growth rate

Government

Table 1: Parameters of Calibration The allocation of time is similar to Gomme and Rupert (2007), with the working time, l; being 0.24 and leisure, x; 0.55. In addition we have time in human capital investment, n; which is set equal to 0.2. The knowledge of n; g; and H and the condition on the growth of human capital allows us to get the value of the capital-labor ratio in the human capital sector, (1 sH )k=(nh); and thus compute the value of the capital share in the education sector, " = 0:83. The chosen values imply AH = 0:21 with the weight on the leisure in the utility function given by = 1:84: In the banking sector we set the value of the inverse of the consumption velocity of money, m=c = a, equal to the average annual value for the period 1919-2004, which is 0.38. The average annual in‡ation rate, , over the same period is 2.6% which implies that the annual money growth, , is equal to 5%. Using an approximate cost of an exchange credit card (American Express) at $100, and the per capita annual consumption expenditure, c = $15780, both at 2006 prices, the share of the labor in the banking sector, = 100=[R(1 a)c] = 0:11:

13

g l n f i=y a

0.0177

Avg. annual output growth rate

0.067

Avg. annual in‡ation rate

0.248

Labor used in goods sector

0.20

Labor used in human capital sector

0.0018

Labor used in banking sector

0.238

Investment-output ratio in goods sector

0.38

Share of money transactions

Table 2: Target Values of Calibration

5

Simulations

In order to solve the model, we log-linearize the equilibrium conditions of the model around its deterministic steady state, and solve the resulting stochastic linear system of equations using standard techniques described, for example in Hartley, She¤rin, and Salyer (1997). However, to put the problem into a form for which standard solution techniques can be applied, the variables that grow along the balanced-path need to be normalized to form stationary variables. Here we divide by the human capital stock ht :

5.1

Comparison of Simulated Volatility to Actual

Table 3 presents US data stylized facts, in terms of moments of a set of variables for the period 1919-2004. The series have been detrended using the Christiano and Fitzgerald (2003) asymmetric frequency …lter with a band of 2-86 years (where 86 is the sample size). The table also presents simulations of the calibrated model, where level variables are normalized by human capital. It shows that simulated relative volatilities of consumption and investment ratios are 0.51 and 2.97, comparing well to the data values of 0.64 and 4.09. Output growth volatility is 0.29 again comparing well to 0.48 for the GDP data. The model’s consumption and investment correlation with output is 0.71 and 0.94 versus 0.56 and 0.53. On the monetary side, the simulated velocity volatility is 1.21 as compared to 1.39 in the data, about 87% of that found in the data. The normalized money and the in‡ation volatilities are 1.60 and 0.81, compared to 1.61 and 0.44 in the data. Velocity’s correlation with output is -0.03, compared to the data’s 0.06. Real money correlation with output is 0.65 compared to the data value of 0.47. The simulated in‡ation correlation with output is positive

14

Simulation Results

RELATIVE

OUTPUT

Data: 1919-2004

VOLATILITY

CORRELATION

RBC

Simulated

Data

Simulated

Data

Consumption

0.51

0.64

0.71

0.56

Investment

2.97

4.09

0.94

0.53

Output Growth Rate

0.29

0.48

0.35

0.20

Income Velocity of Money

1.21

1.39

-0.03

0.06

Normalized Real Money

1.60

1.61

0.65

0.47

Monetary

In‡ation Rate 0.81 0.44 -0.44 0.40 Note: See Appendix for data sources. All data series represent the cyclical component of the data …ltered with the Christiano-Fitzgerald asymmetric frequency …lter with a band of 2-86 years (86=sample size). Series are in logs except those that represent rates. Relative volatility is measured as the ratio of standard deviation of the series to the standard deviation of GDP

Table 3: US Business Cycle Facts, 1919-2004, and Simulations while it is negative in the data; but the simulated correlation of in‡ation with money growth is positive at 0.49, and positive in the data at 0.42 (not shown in table).

5.2

Transition Dynamics

The transition dynamics for a standard endogenous-growth/cash-in-advance model are given in Einarsson and Marquis (1999).8 Brncik (2004) adds in the speci…c extension with exchange credit as in the Section 3 model, with results that are qualitatively the same as Einarsson and Marquis in most dimensions. These papers focus on the dynamics following a permanent change in the rate of money supply growth rate, which in turn causes a change in the long run in‡ation rate. We simulate the transition dynamics and …nd similar results. To understand the results, note that the transition dynamics are sensitive to the equilibrium behavior of the state variable, kt =ht ; with respect to changes in the level of the in‡ation rate. Simulations show that this state variable follows a continuous hump pattern, rising as the in‡ation rate rises, and falling after some moderately high level of the in‡ation rate is reached, the exact 8

Gomme (1993) has the same shares of labor in both goods production and human capital production, with the result that there are no transition dynamics; when the shares are di¤erent, transition dynamics result.

15

TD of PMG "g

TD of PMG "y/h" 0.518

h

"

1.0235

0.516

TD of PMG " l " 0.25

1.024

0.248

1.023 0.246

0.514

1.0225 0.244

1.022 0.512 0

20 40 TD of PMG "khG"

0

20 40 TD of PMG "i/h"

0.08

0

20 40 TD of PMG " x "

0.56

8.2 0.07

8

0.555

0.06

7.8

0.55 0

20 40 TD of PMG " R "

0.2

0

20 40 TD of PMG " π"

0.12

0

20 40 -3 x 10 TD of PMG " F "

12

0.1

10

0.08

0.15

8

0.06

6

0.04

0.1 0

20

40

0

20

40

0

20

40

Figure 6: Transition Dynamics: Permanent Increase in Money Supply Growth Rate (Solid Line); Initial Balanced Growth Path (Dotted Line)

value of which depends upon the calibration (See Appendix C). This turning point is above that of the 1919-2004 US historical experience, which is used for the baseline calibration. The transition dynamics in Figure 6 show that when there is a money supply growth rate increase, starting from the baseline calibration, there is an increase in the state variable kt =ht to a new higher level, as in Einarsson and Marquis (1999). And this coincides with an initial jump upwards in the time in goods production (lGt ), as seen in the upper righthand box of Figure 6. This is like a Phillips type comovement, but here it results in order to produce the higher physical capital stock, while in the long run lGt falls to a lower level due to the in‡ation tax e¤ect. Einarsson and Marquis (1999) also shows that there is such a transitional increase in time spent in goods production. There is also a transitional increase in the physical capital investment to human capital stock ratio, i=h; as seen in the middle box of Figure 6. This is similar to an accelerator e¤ect on investment when the money supply growth rate is permanently increased.

16

6

Shocks and Comovements of GDP, Velocity and In‡ation

We construct the shocks as in Parkin (1988) and Benk et al (2005, 2007). This process is described in Appendix A.1, and the actual constructed shocks both under endogenous and exogenous growth are found in A.2. One di¤erence from previous work is that we use a band pass …lter that takes out only the 86 year trend from the data, a minimalist …lter desirable for leaving in the longer run features, along with business cycle and short run features.

6.1

Variance Decompositions

We decompose the ‡uctuations in GDP growth and velocity along two dimensions: First, we show how much of the variance is explained within each subperiod by each of the shocks, the productivity (PR), money (M) and credit (CR) shocks. Second, we further decompose the variance based on frequencies, across subperiod, and by shock (Referee Appendix B.3 describes the variance decompositions). The results are dependent upon which ordering of the shocks is assumed. There are six possible orderings and we examined all of these. However because of a near consensus regarding the importance of ordering the goods productivity shock …rst, the results reported take the average over just the two possible orderings with the productivity shock ordered …rst, and either the money or the credit shock ordered second. The results are qualitatively similar to when an average is taken across all six shocks; the main di¤erence is as expected: in our reported results the productivity shock has more weight relative to the other shocks as compared to when it is ordered second or third. The variance decompositions are reported for the entire 1919-2004 period, for 1919-2004 except for the WWII period, and for other various subperiods encompassing the interwar (and Great Depression) period of 19191939, the WWII period of 1940-1950, the postwar non-in‡ationary period of 1951-1965, the high in‡ation (Great In‡ation) period of 1966-1985, and the deregulatory/low-in‡ation (Great Moderation) period of 1986-2004. 6.1.1

By Subperiod

Table 4 shows that money and credit shocks had signi…cant contributions to GDP growth volatility over the entire period and for all of the subperi17

1919-1939 1940-1950 1951-1965 1966-1985 1986-2004 1919-2004 except 1941-1950 1919-2004

PR 41% 37% 17% 44% 39% 64% 32%

Endogenous M 16% 23% 33% 19% 18% 8% 14%

CR 23% 19% 22% 18% 23% 16% 31%

K 20% 21% 28% 19% 20% 12% 23%

PR 52% 21% 60% 48% 75% 61% 14%

Exogenous M 10% 27% 13% 23% 11% 7% 23%

CR 22% 25% 14% 12% 5% 18% 34%

K 16% 26% 13% 17% 8% 13% 29%

Table 4: Decomposition of Variance of GDP growth, by Shock and Subperiod 1919-1939 1940-1950 1951-1965 1966-1985 1986-2004 1919-2004 except 1941-1950 1919-2004

PR 38% 27% 26% 18% 14% 34% 17%

Endogenous M 21% 24% 26% 25% 33% 25% 32%

CR 20% 25% 23% 29% 25% 19% 23%

K 21% 24% 25% 27% 29% 22% 28%

PR 62% 30% 18% 38% 36% 54% 35%

Exogenous M 13% 23% 23% 17% 26% 15% 23%

CR 13% 24% 32% 25% 17% 15% 20%

K 13% 23% 27% 21% 21% 15% 22%

Table 5: Decomposition of Variance of In‡ation, by Shock and Subperiod ods. And the money shock contribution is underestimated in the exogenous growth model for three of the four non-war subperiods, as compared to the endogenous growth model. The money shock contributed the most to the variation in output growth and in velocity (in Table 6) during the moderation of the second volatility cycle, from 1951-1965. And Table 5 shows that the money shock’s e¤ect on in‡ation variation was the highest during the 1986-2004 moderation of the second volatility cycle. These subperiod contributions of the shocks, while suggestive, mask the variation across di¤erent spectra. Taking this variation into account quali…es the results as the next subsection shows. 6.1.2

By Subperiod and Spectral Frequency

In Tables 7-9, the variance is decomposed by spectral frequency across each subperiod (table rows), using the short run (SR), business cycle (BC) and long run (LR), as speci…ed in Levy (2001), and by shock components - the

1919-1939 1940-1950 1951-1965 1966-1985 1986-2004 1919-2004 except 1941-1950 1919-2004

PR 39% 49% 18% 34% 59% 24% 28%

Endogenous M 22% 23% 34% 21% 8% 27% 26%

CR 19% 12% 21% 23% 19% 24% 22%

K 20% 17% 27% 22% 14% 25% 24%

PR 54% 7% 9% 35% 40% 47% 44%

Exogenous M 15% 23% 37% 20% 27% 21% 25%

CR 15% 39% 24% 24% 13% 14% 12%

K 15% 31% 30% 22% 20% 18% 19%

Table 6: Decomposition of Variance of Velocity, by Shock and Subperiod

18

goods productivity (PR), the money (M) and the credit productivity (CR) shocks, and by the contribution of changes in the state variable (K), in the table colums.9 The numbers in the table columns show the fraction of variance at a given frequency that is explained by a given shock or the state variable. The columns denoted by ‘all’indicate the total variance of a given series at a given frequency. Focus …rst on GDP in Table 7. Similar to the subperiod results, the money shocks have their biggest contribution to GDP volatility during the moderation of the two volatility cycles, now by the same amount (25%) during both 1951-1965 and 1986-2004. The money shock also contributes the most to velocity varition, at the BC and LR frequencies (25%), during the …rst moderation period of 1951-1965, and contributes the most to in‡ation volatility during the same …rst moderation period (30%). At the same time, the money shocks show the least amount of contribution to the LR GDP volatility during the rises of the two volatility cycles, in 19191939 (9%), and in 1965-1986 (4%). And likewise the money shocks show the greatest amount of contribution to the SR GDP volatility during the same two volatility upswing periods of 1919-1939 (5%) and 1966-1985 (6%).10 Similar results to those for the money shocks hold for the contribution of the state variable to the volatility of GDP (denoted by K in Tables 4-9). This transition dynamics e¤ect is strongest at the LR frequency for both of the volatility moderation subperiods of 1951-1965 (12%) and 1986-2004 (16%); while it is strongest at the SR frequency, and at the sum of the SR and BC frequencies, for the two volatity build-up subperiods of 1919-1939 (5% for SR, 16% for SR+BC) and 1966-1985 (4% for SR, 16% for SR+LR). The credit shock and goods productivity shock also show the largest contributions to the SR frequency during the two volatility upswing periods, and the largest contributions to the LR frequency during the two volatility moderation periods. This …nding across all of the shocks provides for a stylistic characterization: the volatility upswings were characterized by the strongest SR contribution and the weakest LR contribution of all of the components of GDP volatility, while the volatility moderations were characterized by 9

The short-run (SR) frequency band corresponds to cycles of 2-3 years, the business cycle (BC) frequency band to cycles of 3-8 years, and the long-run (LR) band to cycles of 8 years and longer; the spectral density of the series is normalized by the series variance, and then its integral is computed over the corresponding frequency band. 10 King and Plosser (1987) stress such short run variation in GDP because of unanticipated in‡ation.

19

PR

Endogenous M

CR

K

all

PR

Exogenous M

CR

K

all

1919-1939 SR BC LR

13% 10% 4%

5% 9% 9%

6% 12% 7%

5% 11% 8%

30% 42% 28%

6% 15% 4%

9% 9% 5%

12% 11% 3%

11% 10% 4%

38% 46% 16%

SR BC LR

2% 9% 1%

1% 6% 22%

2% 4% 25%

2% 5% 23%

7% 23% 71%

6% 11% 8%

8% 6% 6%

9% 16% 4%

9% 11% 5%

32% 45% 23%

SR BC LR

7% 10% 13%

3% 10% 12%

5% 6% 11%

4% 8% 12%

19% 33% 48%

16% 20% 8%

5% 9% 8%

8% 5% 3%

6% 7% 5%

35% 40% 24%

SR BC LR

14% 18% 10%

6% 8% 4%

7% 8% 5%

6% 8% 4%

33% 44% 23%

17% 13% 7%

8% 12% 3%

7% 9% 3%

8% 11% 3%

39% 45% 16%

SR 6% BC 11% LR 14% 1919-2004 except 1941-1950 SR 17% BC 15% LR 6% 1919-2004 SR 9% BC 11% LR 12%

2% 5% 18%

2% 5% 14%

2% 5% 16%

12% 26% 62%

19% 2% 6%

6% 9% 6%

6% 17% 4%

6% 13% 5%

38% 40% 21%

3% 8% 8%

3% 9% 11%

3% 8% 9%

26% 40% 35%

18% 17% 2%

6% 9% 5%

6% 11% 5%

6% 10% 5%

36% 47% 18%

4% 8% 10%

4% 9% 10%

4% 8% 10%

21% 36% 43%

12% 15% 6%

9% 9% 4%

8% 10% 4%

9% 9% 4%

38% 43% 18%

1940-1950

1951-1965

1966-1985

1986-2004

Table 7: Decomposition of Variance of GDP growth by Frequency, 1919-2004 the strongest LR contribution of the components. The credit shock’s single highest contribution in the LR frequency was during the second volatility moderation of 1986-2004. The goods productivity shock in each of the non-war subperiods makes the largest total contribution to GDP variation. This is in line with traditional expectations of the role of the TFP shock. Compared to the exogenous growth results, the goods productivity shock has a greater impact on the LR frequency variations in all four subperiods, except the Great Depression subperiod, during which the LR contribution is the same in both models. And in the endogenous growth model, the goods productivity shock has a smaller impact at SR frequencies in three of the four non-war subperiods, but a larger impact in the SR during the Great Depression subperiod. In fact, compared to the exogenous growth model, the endogenous growth model shows higher or equal contributions to the LR frequencies of all components of GDP volatility, and lower or equal contributions to the SR frequencies of all components of GDP volatility, in all of the subperiods, with the only single exception to this being the greater SR contribution of the goods productivity shock during the 1919-1939 period. The credit shock’s highest contribution to SR GDP volatility is during 1919-1939, of 5%; and including the BC contribution of 12%, this gives a 17%

20

PR

Endogenous M

CR

K

all

PR

Exogenous M

CR

K

all

1919-1939 SR BC LR

15% 6% 5%

7% 12% 6%

5% 13% 6%

6% 13% 6%

33% 43% 24%

9% 13% 14%

5% 7% 11%

8% 11% 1%

7% 9% 6%

29% 41% 31%

SR BC LR

11% 27% 4%

7% 5% 9%

7% 4% 7%

7% 4% 8%

32% 41% 27%

2% 11% 3%

4% 5% 15%

14% 14% 4%

9% 10% 10%

30% 39% 31%

SR BC LR

9% 12% 4%

11% 14% 5%

8% 7% 5%

9% 10% 5%

37% 44% 19%

7% 11% 10%

7% 9% 11%

5% 7% 9%

6% 8% 10%

26% 35% 39%

SR BC LR

12% 20% 12%

6% 7% 6%

7% 7% 5%

6% 7% 6%

31% 41% 29%

7% 12% 21%

5% 7% 8%

6% 6% 9%

6% 6% 8%

24% 31% 45%

SR 9% BC 20% LR 9% 1919-2004 except 1941-1950 SR 4% BC 14% LR 4% 1919-2004 SR 12% BC 15% LR 9%

8% 7% 6%

8% 7% 6%

8% 7% 6%

33% 41% 26%

11% 8% 11%

5% 6% 9%

8% 12% 6%

7% 9% 8%

31% 35% 34%

10% 10% 7%

10% 9% 7%

10% 9% 7%

33% 41% 26%

9% 16% 11%

7% 8% 8%

6% 7% 7%

6% 7% 8%

28% 37% 34%

7% 9% 7%

6% 8% 5%

6% 8% 6%

32% 41% 27%

5% 19% 10%

7% 7% 11%

7% 6% 5%

7% 7% 8%

27% 38% 35%

1940-1950

1951-1965

1966-1985

1986-2004

Table 8: Decomposition of Variance of In‡ation by Frequency, 1919-2004

PR

Endogenous M

CR

K

all

PR

Exogenous M

CR

K

all

SR BC LR

5% 4% 11%

2% 7% 17%

1% 8% 18%

1% 7% 18%

9% 27% 64%

2% 9% 23%

2% 5% 27%

2% 6% 3%

2% 5% 15%

7% 25% 68%

SR BC LR

1% 1% 10%

0% 2% 30%

0% 1% 25%

0% 1% 28%

1% 5% 93%

1% 3% 8%

0% 5% 36%

3% 6% 10%

2% 5% 23%

6% 18% 76%

SR BC LR

7% 5% 5%

0% 7% 18%

0% 6% 23%

0% 6% 21%

8% 24% 68%

4% 5% 26%

0% 3% 22%

1% 3% 15%

1% 3% 18%

5% 14% 81%

SR BC LR

4% 9% 29%

0% 3% 17%

1% 3% 15%

1% 3% 16%

6% 18% 76%

5% 5% 28%

0% 4% 12%

2% 7% 17%

1% 5% 15%

8% 21% 71%

SR 2% BC 5% LR 50% 1919-2004 except 1941-1950 SR 4% BC 3% LR 34% 1919-2004 SR 2% BC 4% LR 35%

0% 2% 10%

0% 2% 14%

0% 2% 12%

4% 11% 85%

3% 2% 23%

1% 3% 21%

0% 4% 19%

1% 3% 20%

4% 12% 84%

1% 4% 13%

1% 5% 16%

1% 4% 14%

6% 17% 78%

5% 5% 31%

1% 5% 16%

1% 7% 10%

1% 6% 13%

8% 23% 69%

1% 3% 22%

1% 3% 10%

1% 3% 16%

4% 13% 83%

3% 7% 37%

1% 3% 14%

1% 4% 14%

1% 3% 14%

5% 17% 78%

1919-1939

1940-1950

1951-1965

1966-1985

1986-2004

Table 9: Decomposition of Variance of Velocity by Frequency, 1919-2004

21

share in 1919-1938. In 1966-1985, the SR and BC contribution was second highest at 10%, with a LR contribution of 26% as compared to 19% in 19191938. Thus while both the money and credit shock show the highest sums of SR and BC e¤ects in 1919-1938 and 1966-1985, the credit shocks contributes considerably more in the SR and BC frequencies during 1919-1938. For in‡ation, in Table 8, the greatest single component contribution to SR in‡ation volatility during a subperiod was that of the goods productivity shock during 1919-1939 (15%). And money and credit shocks both had their greatest contribution to in‡ation volatility, across all three frequencies, during the 1919-1939 subperiod. Versus the exogenous growth results, the endogenous growth model tends to show more variation at the SR frequency and less at the LR frequency, in contrast to the GDP volatility …ndings. For the velocity volatility, in Table 9, there is a huge contribution of the goods productivity shock (50%) to the LR velocity volatility during the second volatility moderation of 1985-2004. Meanwhile, the total SR contribution of all components to velocity volatility was highest during the …rst volatility upswing of 1919-1939; during this time the total BC contribution of all components to velocity volatility was also highest. There are no clear systematic di¤erences in the velocity volatility between exogenous and endogenous growth models.

6.2

E¤ect on Levels

The e¤ects of the shocks and the state variable on output, velocity and in‡ation can be devised by summing up their respective contributions, with error. Here, the decomposition into shocks can be written as: ybt =

yb ~ k kt

+

for GDP, and for velocity (Vt ytd =mt =

and for in‡ation ( t ) as: bt =

Vb ~ k kt

+

b ~ +

k kt

y z zt

y u ut

+

+

y v vt

+ errorty ;

(24)

yt =mt ) as: V z zt

z zt

V u ut

+

+

u ut

+

+

V v vt

v vt

+ errortV ;

+ errort ;

(25)

(26)

where the terms iz zt , iu ut and iv vt ; with i = y; V; and ; indicate the contribution to productivity, money and credit shocks to the cyclical component 22

d and in‡ation ( bt ).11 of GDP (b y ); velocity (y=m), The e¤ect of shocks on the level of GDP and on the growth rate of GDP are very similar. Here we present the GDP cycle relative to the long sample trend, using the 2-86 …lter, in Figure 7, for 1919-2004. Velocity and In‡ation Rate level decompositions are shown in Figures 8 and 9. 35%

25%

15%

5%

-5%

-15%

-25%

Effect of prod on Y

Effect of money on Y

Effect of credit on Y

Effect of k/h on Y

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

1957

1955

1953

1951

1949

1947

1945

1943

1941

1939

1937

1935

1933

1931

1929

1927

1925

1923

1921

1919

-35%

GDP cycle

Figure 7: E¤ect of Shocks and k=h Transition on GDP, 1919-2004

6.2.1

Long Run Cycle Comovements: Phillips vs In‡ation Tax

Examination of the cycles of GDP, velocity and in‡ation helps reveals different features in the two major volatility periods pre-war and post WWII. There are some rough patterns. The GDP cycle shows the rise and fall in the 1920s, the rise from 1933 to 1944, and then roughly a fall from 1944 down to 1982, and a rise from 1982 to 2004. The velocity cycle is a rise up to 1929, a fall to 1946, and a rise to 1981, with a fall to 1993, and a rise to 2000. In‡ation ‡uctuates more with a fall from 1925-1932, a rise to 1942, a fall roughly to 1961, and a rise up to 1980, and a fall to 1998. 11

The stationary variable is actually y=h; however as we are not aware of any human capital estimate back to 1919, and so cannot normalize y by h here, we assume a smooth trend for h that has no e¤ect on the …ltered data so that there is no di¤erence between the decomposition of the ‡uctuations of y versus y=h:

23

35%

25%

15%

5%

-5%

-15%

-25%

-35%

Effect of prod on vel

Effect of money on vel

Effect of credit on vel

Effect of k/h on vel

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

1957

1955

1953

1951

1949

1947

1945

1943

1941

1939

1937

1935

1933

1931

1929

1927

1925

1923

1921

1919

-45%

Velocity cycle

Figure 8: E¤ect of Shocks and k=h Transition on Velocity, 1919-2004

250%

25%

200%

20%

150%

15%

100%

10%

50%

5%

0%

0%

Effect of prod on inflation Effect of k/h on inflation

Effect of money on inflation Inflation cycle

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

1957

1955

1953

1951

1949

1947

1945

1943

1941

1939

1937

1935

1933

-20% 1931

-200% 1929

-15%

1927

-150%

1925

-10%

1923

-100%

1921

-5%

1919

-50%

Effect of credit on inflation

Figure 9: E¤ect of Shocks and k=h Transition on In‡ation, 1919-2004

24

The comovement periods of in‡ation and GDP are typical of Phillips curve type relations, while the opposite movements of in‡ation and GDP are characteristic of when the expected in‡ation tax lowers the GDP growth rate. The Phillips like comovements occur from 1921-1925, 1929-1945, and 1980-1983; the opposite movements that we call long run in‡ation tax periods occur from 1925-1929, 1966-1980, and 1983-1998. In‡ation acceleration plausibly caused Phillips-like behavior in the early 1920s, but adjustment to the high in‡ation in the later 1920s plausibly was an in‡ation tax behavior. Phillips behavior is plausible with the market crash and throughout the Depression period; while in‡ation tax behavior plausibly occurred with rough adjustment to the gradually rising in‡ation tax (and Vietnam War spending) of the late 60s and the 70s; Phillips behavior with the dramatic drop in the in‡ation in the early 80s; and in‡ation tax behavior largely after that as low in‡ation policies dominated. The comovement of in‡ation and velocity is consistent with such possible Phillips and in‡ation tax demarcations. When the in‡ation rate and the velocity comove, this is consistent with the in‡ation tax behavior. When they move in opposite direction, something else is dominating the velocity cycle. Consistent with the GDP analysis, the graphs indicate comovement from 1921-1925, as in in‡ation tax behavior, and from 1966-1993. But the opposite movement is detected for 1925-1942, consistent with Phillips behavior. Also there is opposite movement from 1993-2003, which is not consistent with Phillips behavior in that GDP and in‡ation moved opposite from 19931998, and comoved somewhat in 1998-2003. The 1993-1998 period then is a di¤erent behavior. 6.2.2

Shock E¤ects

As a secondary analysis to supplement the volatility decompositions, the e¤ects of the shocks on output, in‡ation and velocity are shown in Figures 6, 7, and 8. To see the e¤ects of each shock alone on output, Figures 12-14 in Appendix B.2 report the actual constructed shocks; these correspond closely to how the shocks a¤ect output (although this output e¤ect for the money shock is the inverse of the e¤ect shown in Figure 14). What is notable …rst is that the e¤ect of the productivity shock is strong for output, velocity and in‡ation; the money and credit shocks are small in e¤ect on GDP, and its growth rate, while sizeable for velocity and in‡ation.

25

For output, the productivity shocks seem to explain the bulk of the Depression but less of the 1970s …ltered decline, at which time money shocks appear to be important. Notable about credit shocks on output is that they start becoming less positive starting in 1934. For velocity, consider that there are two large velocity cycles which correspond closely to the GDP volatility cycles, and remarkably, the two velocity cycles pre and post war are very similar in sharing the same pattern: big rise, then big fall, small rise, further big fall, and then big rise back up again. For example, velocity moved up with the money supply and productivity shocks that made the Roaring 20s, and the depressing 70s. One big di¤erence however is that velocity fell down after the Great Crash and should have fallen more, if determined by the goods productivity shock. But it did not fall as such during 1932-1934, meaning that money fell and/or credit rose instead. This break in the velocity from the dictates of the shocks is not present when velocity rose with the 1970s in‡ation and then fell with the subsequent deceleration of in‡ation; there all of the shocks tend to go in the same direction. For the in‡ation rate, Figure 9 shows that the Roaring 20s in‡ation and 70s Great In‡ation was largely determined by the money shocks (black); while the Depression de‡ation and post-1992 …ltered decline in in‡ation was also explained in part by money shocks. Credit shocks played a more unusual role, contributing signi…cantly to the negative in‡ation spike from 1930-1934. This would be due to positive e¤ects of credit on GDP, as its e¤ects on both velocity and GDP were positive, while the e¤ect on in‡ation was negative. Productivity shocks contributed to price declines, apparently in a Harberger (1998) way that is consistent with the Section 3 model, so that for example in the stable 1957-1966 period they were the main determinants of the price decline.

6.3

The WWII Period

The credit shock explains the GDP volatility during WWII. In Figure 7, the credit shocks were going in the same, positive, direction as the output growth rate, while the goods productivity went in the opposite, negative, direction; the variance decomposition in Table 7 shows that credit contributed 31% of the total GDP variation, 25% of this being in the LR spectrum. The money shock also contributed much of the variation, again most in the LR 26

frequency, while the productivity shock accounted for only a total of 12% of the variation. Including the WWII period by using the whole period of 19192004, Table 7 shows that the total contribution of the goods productivity shock to GDP variation drops signi…cantly, from 38% to 32%. The marked e¤ect of the credit shock during the war period might be interpreted in terms of the War e¤ort to raise government …nance through the sales of government bonds, as a type of subsidization of credit use that is speci…c to the war period.

7

Discussion

The major candidate for driving large changes in the money supply growth has traditionally been the government de…cit. In our model, lump sum transfers have no source of …nance in direct taxation and so all such transfers, in a strict sense, induce government de…cits that are …nanced by printing money. Such a de…nition of a de…cit is a bit tentative however in that there are no direct taxes or other forms of government spending. But the model does introduce the printing of money in the same way in which seigniorage …nances de…cits in more extended models. And while evidence on the link between de…cits and money supply growth is mixed, Catao and Terrones (2005) for example …nd strong international evidence for this link in high-in‡ation countries. Such a link in the US for example during the Vietnam War has been long suspected.12 Figure 1 shows, the Korean War is highly correlated with the 1951 in‡ation spike, the Vietnam War with the early 1970s in‡ation spike, and the Iraq War with the post 2001 in‡ation spike; WWI is correlated with the 1919 high in‡ation rate, while WWII is correlated with the 1942-47 in‡ation spikes. In‡ation variability caused in part by money supply variability, possibly from war expenditure, is closely linked to GDP variability historically, as 12

Backus and Dri¢ ll (1985) note that "James Tobin, for one, disagrees that in‡ation was a policy choice derived from a desire to raise employment. ...‘The 1966-69 ride up the Phillips curve was not a conscious choice of novel macroeconomic strategy but a timeworn political decision about wartime …nance. Against the advice of his Keynesian advisors, President Johnson chose for his own reasons of domestic and international politics not to ask Congress for increased taxes to …nance his ill-starred escalation of the con‡ict in Indochina.’" Note that the 1969 government surplus resulted only because the Social Security system, which was running a surplus, was put on the budget and so hid the general Treasury de…cit in that year.

27

shown in Section 2. The economic converse of such variability is consumption smoothing. And the Jermann and Quadrini (2006) interpretation that …nance enables a greater smoothing of consumption is certainly attractive. The development of …nancial instruments, the completing of more inter-state markets, would certainly act in that way and even produce positive shocks on GDP, as we found for the US deregulatory period, in Benk, Gillman, and Kejak (2005).13 However the possibility of non-Jermann and Quadrini (2006) shocks are also possible, ones that cause negative e¤ects on GDP, and that increase its volatility; such shocks are the ones Bernanke (1983) emphasizes. The existence of these during the Great Depression, as occurred with the collapse of the banking sector, were characterized by Bernanke as signi…cantly decreasing the e¢ ciency of the banking sector. In contrast is the freeing up of …nance at the peak of the Great In‡ation. This may help explain why credit shocks had a bigger e¤ect on GDP during the Great Depression than in the Great Moderation, and why velocity and money volatility together bifurcated from GDP and in‡ation volatility during the Great Moderation and after. The deregulation period was one of expanding the spanning of the …nancial instruments across uncertain states that allowed for a positive expansion of credit, rather than the collapse that occurred in the Great Depression, and that facilitated an avoidance of changes in the in‡ation tax and of the GDP instability during ‡uctuating in‡ation taxes. Figure 10 focuses on how the constructed credit shock and GDP volatilities moved together everywhere except especially in 1927-1938, and in 19812004. While credit was repressed by banking failure in the Great Depression, GDP volatility went way up while credit shock volatility did not rise as much. And when deregulation began in 1981, credit volatility rose while GDP volatility fell. In terms of the average volatility levels post-1983, consider that during 1972-1982, they were 1.9% for credit and 2.7% for GDP growth, while for 1983-2004, credit volatility nearly doubled to 3.4% while GDP was nearly halved to 1.5%. This interpretation suggests that the high velocity and money volatility after 1983 re‡ected the rise of new credit instruments. In particular, money 13

There were some exceptions; an example of a negative credit shock given by Benk et al (2005b) was the 1986 law ending the trading of general partner unlimited liabilities to limited partners which gave non-economic tax subsidies to real-estate investment trusts (REITS); this repeal may have been linked to the subsequent collapse of the savings and loans sector that was heavily invested in such REITS.

28

16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0%

Volatility(GDP)

2003

1999

1995

1991

1987

1983

1979

1975

1971

1967

1963

1959

1955

1951

1947

1943

1939

1935

1931

1927

1923

1919

0.0%

Volatility(Credit)

Figure 10: Volatilities of GDP and the Credit Shock, 1919-2004

market accounts, at both investment banks and commercial banks, allowed interest to be earned; writing checks on these accounts meant the ability to use credit cards during the exchange period while earning nearly full government bond interest rates. This credit became the source of interest-earning means of exchange that were used in a way unprecedented in magnitude compared to earlier periods. Money aggregates like M1 include some of these interest-bearing accounts and so their expanded use simply represents more exchange funds also being used as part of intertemporal …nance, at the low end of the term structure. The con‡uence of exchange and intertemporal credit can explain the preference for short maturities, as is modeled in Bansal and Coleman (1996) and Canzoneri and Diba (2005). Therefore, the increased volatility of this money, but not of in‡ation or GDP, combined with the variance of decomposition analysis, suggests that these …nancial instruments were providing both exchange and intertemporal functions that allowed for greater consumption smoothing even while in‡ation levels fell. The private money volatility was increased post 1983, this being the creditbased money created by the …nance sector, that both Friedman and Schwartz (1963) and Bernanke and Blinder (1988) emphasize. Sargent, Williams, and Zha (2006) and Carboni and Ellison (2007) put forth a Barro-Gordon theory with learning to explain the Great In‡ation of the 1970s. Here the rise and fall of in‡ation is due to trying to exploit a Phillips relation and then learning that it does not work. Our theory somewhat di¤erently suggests that long run money supply increases play a 29

large role in this in‡ation, due to in‡ation taxation. This is something not supported in Canova and Gambetti (2004), but is consistent with Cogley and Sargent (2005). Canova and Gambetti (2004) restrict the impulse responses to only VAR evidence in which there is a liquidity e¤ect on money supply shocks over the last 6 periods. While Canova and Gambetti (2004), and for example Benati (2007), restrict their impulse analysis to only liquidity e¤ects and zero out all long run e¤ects, our model includes long run features that can be well-applied to historical analysis. In particular, money-induced in‡ation causes lower GDP growth in our model, and this is ruled out in Canova and Gambetti (2004) impulse responses. Further we explain why the M1 money supply growth rate shows variation that departs from the in‡ation and GDP growth paths, as based on increased credit contributions to velocity volatility. This makes the M1 growth rate volatility consistent with lower in‡ation and GDP volatility in our model.

8

Conclusion

Our interpretation is that the liberalization of credit post-1983 allowed supple in‡ation tax avoidance with high credit volatility that kept the high money supply volatility from translating into high GDP and in‡ation volatilities. In‡ation and credit volatilities, rooted in how money and credit are substitutes in exchange, had volatilities that substituted for each other in opposite ways during the Great Depression versus the Great Moderation, while GDP volatility followed the in‡ation and not the credit volatility, except for WWII This is based on the paper’s reported …ndings that the behavior of the money and credit shocks in the model play a di¤erent role during the two major volatility movements. This provides a distinguishing characteristic by which to contrast these episodes, and at the same time explain some puzzles occurring with the post 1983 Moderation. While the comovement of in‡ation volatility with GDP volatility remained remarkably consistent throughout the entire 1919-2004 period, the credit shocks, money velocity, and money supply growth rate volatilities have signi…cant breaks from moving with these other two. This gives rise to a characterization of the why GDP and in‡ation volatility fell post-1983 even as the volatility of the M1 money growth rate rose, in contrast to the 1927-1955 period when money velocity and all money aggregate growth rate volatilities coincided with the in‡ation and GDP volatility movements. 30

We also …nd that most of the GDP volatility occurs in the LR frequency, in certain subperiods and in the whole 1919-2004 period, when using the baseline endogenous growth economy; in contrast the exogenous growth framework forces this more into the SR and BC frequencies. Productivity shocks explain only 15% of GDP volatility in the BC frequency for the 1919-2004 period excluding WWII (1941-1950), although these shocks are typically the main story in even the most advanced standard business cycle analysis (Chari, Kehoe, and McGrattan 2007). While we hope that this provokes quali…cation of restricting analysis to only business cycle frequencies, and to only the productivity shock, this di¤erence of endogenous and exogenous growth sets the scene for the a signi…cant contribution of the paper. We explain both the Great Depression and the Great In‡ation/Great Moderation periods using a set of tools that is able to contrast somewhat starkly these two periods of great volatility swings. The paper presents the historical trends in which velocity volatility diverges from GDP volatility post-1983 but move together 1919-1939, while in‡ation and GDP volatility moved together all the time except WWII. And then for analysis of these trends, we determine the contribution of the model’s shocks to both …rst and second moments of GDP and velocity, across the frequencies and subperiods. The results are that both money and credit shocks have distinct behavior across the two periods; this provides the basis of the paper’s explanation of the di¤erence in the two great volatility swings. We use a good shock - bad shock characterization for this. Money shocks a¤ect more the SR and less the LR in both the Great Depression (1919-1939) and the Great In‡ation (1965-1985) subperiods. We interpret this as being the result of the unexpected deceleration and acceleration of in‡ation during these times. These we consider the "bad" money shocks, in that at these times the sum of the SR and BC frequency e¤ects on GDP are double those during the moderation of 1986-2004. During this moderation period, the "good" money shocks have a strong contribution to long run volatility through their in‡ation tax e¤ect on the growth rate. This notably gives a quantitative impact to Friedman’s money as a veil: only when it shudders does GDP ‡utter. But the quanti…cation here of the SR and BC e¤ects makes this dictum more about the shorter run frequencies, leaving the in‡ation tax e¤ect on growth for the long run frequency. Credit shocks likewise a¤ect more the SR and BC frequencies of GDP and less the LR during the Great Depression and Great In‡ation subperiods, 31

and so these are the "bad" credit shocks. But, in contrast to the bad money shocks which are fairly symmetric in these two subperiods, the bad credit shocks are more asymmetric in e¤ect. During the Great Depression the SR and BC credit e¤ects are about three times the magnitude as compared to the Great Moderation of 1986-2004, while during the Great In‡ation only about double that 1986-2004. We interpret this as a result of times when the credit was repressed: more severely from the …nancial collapse in the Great Depression and less so from the sometimes binding credit restrictions (Regulation Q for example) during the rise of the Great In‡ation. So we explain the two great volatility movements as both having bad money and credit shocks that contribute to greater SR and BC GDP growth volatility. But there is more of the good LR money shocks in the recent volatility decline, and the signi…cant "good" credit volatility after 1983 appears to have contributed to the money aggregate volatilities in such a way that GDP volatility could decline. The credit apparently acted as an taxescape valve that blew o¤ the velocity steam that would otherwise have increased GDP volatility, as it did in the 1919-1939 period.

A A.1

Appendix. Data Sources

Data used in the paper has been constructed on annual frequency, for the 1919 - 2004 time period. The main data sources were the Bureau of Economic Analysis (BEA) and the IMF International Financial Statistics (IFS). Series have been extended backwards until 1919 based on the series published in Kuznets (1941), Friedman and Schwartz (1963a) (F&S) and the online NBER Macrohistory Database (http://www.nber.org/databases/macrohistory/contents/) (NBER). 14 The data series are as follows: Gross Domestic Product (BEA, Kuznets). Consumer Price Index (BEA, F&S). 14

Note that Romer’s revised historical data for GDP was alternatively used. Romer reports Industrial Production rather than GDP, for the period up to 1939. This was chained to the GDP data for 1940 and after. Use of this alternative GDP series results in more volatility in the level and in the growth rate.of output. But the spectral decomposition results on volatility were not qualitatively a¤ected. Therefore these alternative results are not reported.

32

Price Index for Gross Domestic Product (BEA, Kuznets). Personal Consumption expenditures (BEA, Kuznets). Gross private domestic investment (BEA, Kuznets). Wage and salary accruals (BEA, Kuznets). Wage and salary accruals, Finance, insurance, and real estate (BEA, Kuznets). Full-time equivalent employees (BEA, Kuznets). Full-time equivalent employees, Finance, insurance, and real estate (BEA, Kuznets). M0 (IFS, NBER). M1 (IFS, NBER). M2.(IFS, NBER). Treasury Bill rate (IFS, NBER).

A.2

Variance Decomposition

The decomposition of the variance of the GDP growth and velocity by shocks is based on the principle described in Ingram, Kocherlakota, and Savin (1994), and has been done as follows: Let z, v and u be the three, possibly correlated shocks. Let’s assume the ordering z-v-u, that is, the movements in z are responsible for any comovements between z and v or z and u, and that movements in v are responsible for any comovements between v and u. We can formalize this notion by de…ning vte to be the residuals in a regression of vt on the vector (zt ; :::; zt s ) and uet to be the residuals in a regression of ut on the vector (zt ; :::; zt s ; vt ; :::; vt s ). Thus we interpret vte as capturing the movements of v that are not associated with current, future, or past movements in z. Given this particular ordering, consider the decomposition of the variance _ t ) into the components due to the various shocks that is of GDP growth (4y obtained by running the regression: _ t= 4y

S X

z;s zt s

|s=0 {z

_ z 4y t

}

+

S X

e v;s vt s

|s=0 {z

_ v 4y t

}

+

S X

e u;s ut s

|s=0 {z

_ u 4y t

}

+ "t

(27)

_ t explained by each shock is given Then the fraction of the variance of 4y z) v) _ _ _ u) V ar( 4y V ar( 4y V ar(4y v u t t t by: P z = V ar(4y _ ) , P = V ar(4y _ ) , P = V ar(4y _ ) . A similar regression to t

t

t

33

that of (27) is run on velocity and the same shocks to determine its variance decomposition. Unless the shocks z, v and u are orthogonal to each other, the results are sensitive to the ordering adopted. We considered all six possible orderings of the shocks. Results presented are the average for the two cases when the goods productivity shock is ordered …rst. The proportion of variance of a series due to SR, BC and LR components can be obtained as in Levy and Dezhbakhsh (2003): it amounts to estimating the spectral density of the series, normalizing it by the series variance, and then computing its integral over the corresponding frequency band. If we denote by f (!) the spectral density of the series and by 2 its variance, then the fraction of variance due to each frequency component is given by R 2 =2 R 2 =3 R 2 =8 H SR = 2 =3 f (!)= 2 d!, H BC = 2 =8 f (!)= 2 d!, H LR = 2 =1 f (!)= 2 d!. The frequency bands are determined by the mapping ! = 2 =p, where p measures the cycle length (2, 3 or 8 years). We are using an alternative, equivalent measure for the fractions of variance (suggested also by Levy and Dezhbakhsh (2003)): this consists of passing the series through a band-pass …lter, estimating the variance of the …ltered series and relating it to the variance of the original series. We employ the Christiano-Fitzgerald asymmetric band-pass …lter with the afore-mentioned 2-3, 3-8 and >8 year bands. This procedure is applied to the simulated series of output growth and velocity, where simulations have been run by feeding back the estimated variance-covariance structure of the shocks into the model. The variance-covariance matrices have been estimated separately for each of the subperiods, this way we obtained simulated series and decompositions that di¤er by subperiods. To assess the fraction of variance explained by each shock in turn at each frequency, we decompose each of the frequency component further, by shocks. The variance decomposition procedure is similar to that described in equation (27). The di¤erence consists in pre-…ltering the target series and the shock series to extract the adequate frequency component. According to this, the Christiano-Fitzgerald asymmetric band-pass …lter with the 2-3, 3-8 and >8 year bands is applied to the output growth and velocity series, as well as to the productivity, money and credit shock series.

34

B B.1

Referee Appendix Construction of Shocks

Assume that denotes the steady state value of variable ; and ^ denotes its percentage deviation from the steady state (^ = log( ) log( )). With k~ k=h; and any variable with a tilde above indicated the ratio of that variable to h; and using the solution of the model from section 2, the logdeviations of the model variables can be written as linear functions of the ~ z; u; v). By stacking the equations, the solution can be written state s = (b k; in matrix form as follows: h i h i0 Xt = A b (28) k~t + B zt ut vt ; h

i0 ^ ^ b where X = c~ x^ l n ^ f s^G a ^ ; and c~ c=h. From (28), one can construct the solution of any variable of the model, by forming the appropriate linear combination of the appropriate rows of (28), the linear combinations being given by the linearized versions of equations (3)-(8). Given the model solution h (28) (thatiis, knowing the value of matrices A and B), the series of shocks zt ut vt can be constructed by using data on k~ and "solving" the system of linear equations (28). It can be easily X and b t

t

seen, that in order to identify the three series of shocks, we need data on at least three variables from Xt . In a three-variable case the shocks represents the solution of a system of three linear equation. If more that three variables are used, then the shocks are "overidenti…ed" as we have more equations than unknowns. In such a case we apply a least-square procedure as we illustrate below. In the procedure of constructing the shocks, we employ the variables on which we were able to …nd reliable data. We construct stationary variables c=y, i=y and m=y, and on which we use data to construct the shocks. We also use data on labor hour in banking sector f . and on the wage rate in banking - the latter series being used as a proxy for the marginal product of labor in banking (mplb). The data series on b k~ is constructed by using for k the capital accumulation equation (5), data on investment to compute b~{t and the initial condition b k~ 1 = 0. For human capital, because of a lack of a

series going back to 1919, we use a smooth trend, as data in Jorgenson and Stiroh (2000) for 1959-1998 indicates. 35

c i=y, c m=y, d f^ and mplb, ~ c=y, d we set up a Having the data series on b k, system of linear equations: h i h i0 XXt = AA b (29) k~t + BB zt ut vt ;

i0 h c i=y c m=y d fb mplb d where XX = c=y and the rows of the matrices AA and BB result from the linear combinations of the corresponding rows of matrices A and B, the appropriate linear combinations being given by the linear equations that de…ne the variables from XX as functions of the variables from X. The marginal product of labor in banking, is derived from equation (11), while the de…nition of the other terms of the matrix XX is straightforward. The least square "estimates" for the shock series are computed as follows: h i0 i h 0 1 0 b ~ est zt ut vt = (BB BB) BB (XXt AA k t ): t

In this approach we used …ve variables to construct the economy’s three shocks. To test for the robustness of the process of shock construction, we repeated the computation by using combinations of …ve variables taken four at a time, and …ve taken three at a time, allowing for …fteen more possible ways to construct the shocks. Figure 11 graphs seven of the computed credit shock series of these along with the baseline of …ve variables. The results show that all combinations that include m=y, either c=y or i=y, and either f or mplb generate nearly the same shock series, while other combinations (not graphed) show randomness and lack of conformity. Thus, we found that the results are robust as long as the variables are included that correspond to the model’s three sectors in which the three shocks occur.

B.2

Shocks in Endogenous Growth versus Exogenous Growth

The next three …gures show each of the shocks, for both the baseline endogenous growth and the comparison model of exogenous growth. The productivity shock is little changed from exogenous growth; the credit shock shows signi…cant di¤erences from exogenous growth, for example during the Great Depression; and the money shock has various di¤erences such as during the Great In‡ation. 36

50.0% 40.0% 30.0% 20.0% 10.0% 0.0% -10.0% -20.0% -30.0%

c/y, i/y, mplb, f, m/y

i/y, mplb, f, m/y

c/y, i/y, f, m/y

c/y, i/y, mplb, m/y

i/y, f, m/y

2003

1999

1995

1991

1987

1983

1979

1975

1971

1967

1963

1959

1955

1951

1947

1943

1939

1935

1931

1927

1923

1919

-40.0%

i/y, mplb, m/y

Figure 11: Credit Shocks Constructed with Various Combinations of the Five Variables, 1919-2004

10%

5%

0%

-5%

-10%

-15%

-20%

z_endog

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

1957

1955

1953

1951

1949

1947

1945

1943

1941

1939

1937

1935

1933

1931

1929

1927

1925

1923

1921

1919

-25%

z_exog

Figure 12: Productivity Shocks, Endogenous and Exogenous Growth Models, 1919-2004

37

Figure 10 shows a strong negative e¤ect of the goods sector productivity shock during the Great Depression, consistent with total factor productivity stories of the Great Depression (Kehoe and Prescott 2002). Figure 13 shows the credit shock. The baseline endogenous growth shows for example negative e¤ects during the Great Depression that contributed to its prolongement, and some positive e¤ects during the 1970s and into the deregulatory period. 25%

20%

15%

10%

5%

0%

-5%

-10%

-15%

-20%

v_endog

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

1957

1955

1953

1951

1949

1947

1945

1943

1941

1939

1937

1935

1933

1931

1929

1927

1925

1923

1921

1919

-25%

v_exog

Figure 13: Credit Shocks, Endogenous and Exogenous Growth Models, 19192004

6%

4%

2%

0%

-2%

-4%

-6%

u_endog

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

1957

1955

1953

1951

1949

1947

1945

1943

1941

1939

1937

1935

1933

1931

1929

1927

1925

1923

1921

1919

-8%

u_exog

Figure 14: Money Shocks, Endogenous and Exogenous Growth Models, 19192004 Figure 14 shows the crash in the money supply starting in 1928 that 38

g

k/h

0.028

2.48

0.026

2.46

0.024

2.44

0.022

2.42

0.02

2.4

0.018

2.38

0.016 -0.5

0

0.5

2.36 -0.5

1

0

sGk/lGh

0.5

1

0.5

1

sHk/lHh

9

3.2 3.1

8.5

3 8 2.9 7.5

7 -0.5

2.8

0

0.5

2.7 -0.5

1

0

lasted through to 1940, and not really reaching a trough until 1946. And it shows the long prolonged increase during the Great In‡ation.

C

Long-Run E¤ects

Simulations show the long-run e¤ects of increasing the in‡ation rate, through steady increases in the the permanent money growth rate, on the various endogenous variables.

D

Transition Dynamics

See the simulated transition after the benchmark economy is exposed to the permanent increase of the money growth rate by 10%.

E

Impulse Responses

Here we report the simulated impulse responses of the baseline model’s variables to the three shocks on goods sector productivity (TS), the money supply 39

y/h

k/y 4

1.091

3.95

1.09

3.9

1.089

3.85

1.088

3.8

1.087

3.75

1.086

3.7

1.085

3.65 -0.5

0

0.5

1.084 -0.5

1

0

a

0.5

1

0.5

1

0.5

1

0.5

1

r

0.5

0.07

0.45

0.068

0.4

0.066

0.35 0.064 0.3 0.062

0.25

0.06

0.2 0.15 -0.5

0

0.5

0.058 -0.5

1

0

x

w 1.42

0.6

1.4 1.38

0.58

1.36 0.56 1.34 0.54 -0.5

0

0.5

1.32 -0.5

1

0

c/k

c/h

0.158

0.385

0.156

0.38 0.375

0.154

0.37 0.152 0.365 0.15

0.36

0.148 0.146 -0.5

0.355 0

0.5

0.35 -0.5

1

40

0

l

l

G

H

0.26

0.22

0.25

0.21

0.24

0.2

0.23

0.19

0.22

0.18

0.21

0.17

0.2 -0.5

0

0.5 l

0.16 -0.5

1

0 s

F

0.5

1

0.5

1

0.5

1

G

0.025 0.785 0.02 0.015

0.78

0.01 0.775

0.005 0 -0.5

0

0.5

1

-0.5

0

c/y

i/y

0.6

0.44

0.59

0.43

0.58

0.42

0.57

0.41

-0.5

0

0.5

1

-0.5

41

0

TD of PMG "k/h"

TD of PMG "c/h"

TD of PMG "m/h"

2.476 0.38

0.14

0.375

0.13

2.474 2.472 2.47

0.12

0.37 0

20 40 TD of PMG " r "

0.075

0.07

0

20 40 TD of PMG " n "

0

0.2

0.782

0.195

0.78

0.19

0.778

0.185

0.776

20 40 TD of PMG " s "

0.065 0

20 40 TD of PMG " w "

0

20 40 TD of PMG " a "

0.38 1.345

0 x 10

-3

20 40 TD of PMG " f "

4 3.5

0.36

3 1.34

2.5

0.34

2

1.335 0

20

40

0

20

40

0

20

40

Figure 15: E¤ects of the permanent increase in money growth (B): - - - initial BGP

42

TD of PMG "g "

TD of PMG "y/h"

TD of PMG " l "

h

0.518

0.25

1.024 1.0235

0.516

0.248

1.023 0.246

0.514

1.0225 0.244

1.022 0.512 0

20 40 TD of PMG "khG"

0

20 40 TD of PMG "i/h"

0.08

0

20 40 TD of PMG " x "

0.56

8.2 0.07

8

0.555

0.06

7.8

0.55 0

20 40 TD of PMG " R "

0.2

0

20 40 TD of PMG " π"

0.12

0

20 40 -3 x 10 TD of PMG " F "

12

0.1

10

0.08

0.15

8

0.06

6

0.04

0.1 0

20

40

0

20

40

0

20

40

Figure 16: E¤ects of the permanent increase in money growth (B): - - - initial BGP

43

TS to "k/h"

TS to "c/h"

TS to "m/h" 1

0.8 1

0.8

0.6

0.6 0.4

0.5

0.4

0.2 0

0

20 40 TS to " r "

60

0.2 0

20 40 TS to "g "

60

0

20 40 TS to " s "

60

0

20 40 TS to " f "

60

0

20

60

y

0.15

1.5

1

1

0.1 0.5

0.05

0.5

0 0

0

-0.05 0

20 40 TS to " w "

60

0

20 40 TS to " a "

60

0.25 0.8

0.2

0.2

0.6

0 -0.2

0.15

0.4

0.1

0.2

0.05 0

20

40

60

-0.4 -0.6 -0.8 0

20

40

60

40

Figure 17: Impulse responses to the technological shock - Part 1

growth rate (MS) and the credit sector productivity (CS):

References Backus, D., and J. Driffill (1985): “In‡ation and Reputation,”American Economic Review, 75(3), 530–538. Bansal, R., and W. J. Coleman (1996): “A Monetary Explanation of the Equity Premium, Term Premium, and Risk-Free Rate Puzzles,” Journal of Political Economy, 104(6), 1135–1171. Benati, L. (2007): “The ’Great Moderation’in the United Kingdom,”ECB Working Paper Series 769, European Central Bank. Benk, S., M. Gillman, and M. Kejak (2005): “Credit Shocks in the Financial Deregulatory Era: Not the Usual Suspects,”Review of Economic Dynamics, 8(3), 668–687. 44

TS to "g "

TS to "y/h"

TS to " l "

h

3 3

0

2

2 -0.1

1

1 0

-0.2

0 0

20 40 TS to "vel"

60

0

20 40 TS to "i/h"

60

0

20 40 TS to " x "

60

0

20 40 TS to "π"

60

0

20

60

0.08

3 10

0.06

2

0.04 0.02

5

1

0 -0.02

0

0 0

20 40 TS to " R "

60

0

-0.04 0

20 40 TS to "n "

60

0

0

-0.1 -2

-0.2 -0.3

-0.2

-4 0

20

40

60

-0.4 0

20

40

60

40

Figure 18: Impulse responses to the technological shock - Part 2

45

MS to "k/h"

MS to "c/h"

MS to "m/h"

0

0 -0.005

-0.05

-0.01

-0.1

-0.015

-0.15

-0.02

-0.2

0 -0.5 -1 -1.5 -2

0 -3 x 10

20 40 MS to " r "

60

0 -3 x 10

20 40 MS to "g "

60

0

20 40 MS to " s "

60

0

20 40 MS to " f "

60

0

20

60

y

0

0 0

-5

-0.02 -0.04

-10

-0.06 -20 -10

-0.08 0

20 40 MS to " w "

60

0

20 40 MS to " a "

60 8

0

0.05 0.04

6

-0.5

0.03

4 -1

0.02

2

0.01

-1.5 0

20

40

60

0

20

40

60

40

Figure 19: Impulse responses to the monetary growth shock - Part 1

46

MS to "y/h"

x 10

-3

MS to "g "

MS to " l "

h

6

0 -0.05

0

4

-0.1

-0.1 2 -0.15

-0.2

-0.2 0

20 40 MS to "vel"

60

1.5 1

0

20 40 MS to "i/h"

60

0.02

0.04

0

0.03

-0.02

0

20 40 MS to " x "

60

0

20 40 MS to " π"

60

0

20

60

0.02

-0.04

0.5

0.01

-0.06 0

20 40 MS to " R "

60

2.5

0

20 40 MS to "n "

60

2.5

0.08

2

2

0.06

1.5

1.5

0.04

1

1

0.02

0.5 0

20

40

60

0.5 0

20

40

60

40

Figure 20: Impulse responses to the monetary growth shock - Part 2

47

x 10

-3

CS to "k/h"

CS to "c/h"

CS to "m/h" 0

0.1 0.08

2

-0.5

0.06 1

0.04 -1

0.02 0

0 -3 x 10

20 40 CS to " r "

60

0 -3 x 10

20 40 CS to "g "

60

0

20 40 CS to " s "

60

0

20 40 CS to " f "

60

0

20

60

y

10 3

0.03

2

5

1

0

0

20 40 CS to " w "

60

0

0.02 0.01

0

20 40 CS to " a "

60

0

0

-0.005

-1 -0.5

-0.01

-2

-0.015 -1

-0.02 0

20

40

60

-3 0

20

40

60

40

Figure 21: Impulse responses to the credit sector productivity shock - Part 1

48

CS to "y/h"

x 10

-3

CS to "g "

CS to " l "

h

0

0.08

0.06 -1 0.04 0.02

0.06

-2

0.04

-3

0.02

-4 0

20 40 CS to "vel"

60

1.2

0

20 40 CS to "i/h"

60

0 -0.01

-2

0.6

-0.02

-4

0.4

-0.03

-6

-0.04

-8

0.2 0

20 40 CS to " R "

60

0

20 40 CS to "n "

60

0

1

20 40 CS to " x "

60

0

20 40 CS to " π"

60

0

20

60

0

1 0.8

0 -3 x 10

1

-0.02 0.5

0.5

-0.04 -0.06

0 0

20

40

60

0 0

20

40

60

40

Figure 22: Impulse responses to the credit sector productivity shock - Part 2

49

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