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NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY IN ITALY BEFORE STAGE III OF EUROPEAN MONETARY UNION Luca Fanelli∗and Paolo Paruolo** 1. Introduction The transmission mechanisms of monetary policy have been at the centre of a vast debate in the last decade and it is currently receiving increasing attention because of Stage III of European Monetary Union (EMU). It is common knowledge that focusing on a closed economy, standard transmission channels include an aggregate demand channel and an expectations channel; the aggregate demand channel can be furthermore separated into a ''conventional'' money channel (more precisely an interest rate channel) and a credit channel see e.g. Bernanke and Blinder (1988) (henceforth BB) and Gertler and Gilchrist (1993). This paper focuses on the aggregate demand channel of monetary policy transmission, with special attention to the credit channel; indeed, when commercial banks are a major source of financing for firms and in the presence of asymmetric information between borrowers and lenders (as it has been the case in Italy), indirect control on the supply of credit may have significant impact on real variables. In contrast to the money view implicit in the conventional IS-LM framework, the two main hypotheses underlying the credit view are: (1) bank credit is ''special'' in the sense that bank loans and external fundraising are considered as imperfect substitutes in firms' financing; (2) monetary policy affects bank lending. Thus different effects of monetary policy arise from the asset-side of banks' balance sheets, i.e. loans, and from the liability-side of banks' balance sheets, i.e. deposits. Due to the predominant role played by commercial banks in the process of financial intermediation, Italy, as other EMU countries, represents an interesting case-study for assessing the effectiveness of a credit channel of monetary policy. If a separate credit channel parallel to



Dipartimento di Scienze Statistiche, Università di Bologna.

**

Facoltà di Economia, Università dell’Insubria.

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the interest rate channel was effective in Italy, it could presumably play an important role for determining the effects of a single monetary-policy in force since the beginning of stage III of EMU. Assessing the existence and the effectiveness of a credit channel is an empirical question. Existing investigations on the transmission mechanisms of monetary policy in Italy separate the analysis of the money channel from that of the credit channel. Recent econometric studies on money demand include e.g. Angelini et al. (1994), Bagliano (1996) and Juselius (1998); these studies cover various samples and sampling frequencies. Concerning the credit channel, the existing literature is based on •

impulse response analyses on small-scale VAR models including measures of credit, measures of the stance of monetary policy and proxies of macroeconomic performance as in Buttiglione and Ferri (1994) and Bagliano and Favero (1995);



models based solely on interest rates as in Cottarelli et al. (1995) and Amisano et al. (1997);



evidence based on micro data as in Angeloni et al. (1995) and Favero et al. (1999);



evidence based on the ''narrative approach'' as in Bertocco (1997)1.

The purpose of the present paper is to investigate the domestic transmission mechanisms of monetary policy in Italy before Stage III of EMU. In particular, we examine whether the aggregate demand channel can be separated into an interest rate channel and a parallel credit channel2. Altough different econometric approaches have been advocated as primary tools in assessing the existence and effectiveness of a credit channel, we 1 Both Buttiglione and Ferri (1994) and Bagliano and Favero (1995) show, with different approaches and different data sets covering part of the eighties and nineties, that the loan-bond interest rate spread tends to increase and real output tends to contract in the face of policy tightening. They interpret this evidence as consistent with the effectiveness of an separate lending-channel. In their view, this mechanism operated mostly during the monetary tightening of 1992: the contraction of the credit growth rate observed between the end of 1992 and the beginning of 1993 in correspondence of the withdrawal of the Italian lira's participation in the EMS was due to a shock on the supply of loans. On the other hand, according to Bertocco (1997), the lending channel operated in Italy in the sixties and seventies as a direct consequence of the policy of the Central Bank and not in the eighties and nineties. 2

We ignore other important channels of monetary policy transmission such as the exchange rate channel. However, we observe that the influence of foreign factors is partly reflected on the level of the real interest rate used in the analysis.

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use a variant of BB's model as a benchmark ''structural'' guideline and specify a dynamic simultaneous system of error correcting equations as the econometric reference model. We thus focus on the systematic portion of monetary policy actions and aggregated behavior rather than on shocks; it has been argued that the variability of shocks is small in relation to the variability of systematic components of monetary policy3, hence if during the transition towards the EMU a credit channel has been working in parallel to an interest rate channel, its existence and effectiveness should emerge mostly from systematic relations. Since the money channel operates through banks' liabilities and the credit channel operated through their assets and these quantities are related by accounting identities, it can be argued that macroeconomic time series are ill-suited to identify a credit channel from a money channel, see Section 2. In this paper it is explicitly argued that the two channels of monetary policy can be identified also at the macro level if use is made of a ''theorybased'' model where the systematic components of banks' balance sheet are explicitly modelled. This can be done by properly identifying the structural relations and coefficients related to the asset and liability components of banks' balance sheets. The specified system of simultaneous error correcting equations includes: (a) an aggregate demand equation; (b) an inflation equation; (c) the Central Bank's monetary policy rule along with a money demand equation; (d) the price-setting equation used by the Banking System for fixing the interest rate on loans along with a loan demand equation; (e) the price-setting equation used by the Banking System for fixing the interest rate on bank deposits, and the data set covers the period 1974:4 - 1998:2. As detailed in Section 4, in 1983 the institutional ceilings on the expansion of credit were removed in a context of gradual lifting of capital mobility constraints and deregulation. This fact suggested to split the sample into two periods, 1974:4 - 1983:2 and 1983:3 - 1998:2. The empirical model was estimated over the latter period while the former was retained only for comparative graphical analyses. The methodology used for estimating and testing the model is based on cointegration techniques as proposed in e.g. Johansen and Juselius (1994). 3

As McCallum (1999, pp. 3) puts it: ''Perhaps the simplest way of arguing for an emphasis on the systematic component of policy is to recognize that quantitatively the unsystematic portion of policy-instrument variability is quite small in relation to the variability of the systematic component''. Criticism on the use of impulse responce analysis in the evaluation of policy models may be found in e.g. Ericsson et al. (1998).

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Impulse responses with respect to shocks to the system can be easily derived in this type of models. However, the identification of structural shocks requires strong assumptions that would shift the emphasis on the unsystematic part of the model, see e.g. Ericsson et al. (1998); we thus chose not to present any impulse response, concentrating on the systematic part of the model. Although the period 1983:3 - 1998:2 is characterized by different regimes of monetary policy, the estimated system seems to be consistent with a framework where both the interest rate channel and the credit channel are effective. The present results suggest the necessity to enlarge existing policy models to include a credit channel. The paper is organized as follows. Section 2 sketches some methodological issues and Section 3 summarizes the theoretical reference model. Section 4 provides some institutional and historical backgrounds concerning monetary policy in Italy. Section 5 reports the econometric evidence over the period 1983:3 -1998:2 and Section 6 contains some conclusions. The Appendix is divided into three parts: Appendix A.1 reports a simple variant of the original BB model; Appendix A.2 contains a detailed description of the data set used in the analysis; Appendix A.3 describes the determination of the cointegration rank of the system. 2. Methodological issues Following Friedman (1995), the empirical studies on the real effects of monetary policy can be divided into three main lines of research: structural models; vector autoregressions; non-quantitative information. When using vector autoregressions, the empirical analysis of the transmission mechanisms is carried out by measuring the effects of monetary policy on output, prices and other variables, in terms of policy shocks. In this context policy shocks represent the random unsystematic component of the monetary authorities' behavior, and their impact on real variables is of major interest. ''Identified'' or ''semi-structural'' VAR systems are estimated in order to perform impulse response analyses. The approach followed in this paper for investigating the domestic transmission mechanisms of monetary policy in Italy can be recast within structural models, in the sense that we focus on the systematic portion of aggregate behavior and monetary policy actions, rather than on shocks. 606

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More precisely, in the spirit of McCallum (1999), this work is based on the recognition that the unsystematic portion of aggregate behavior and policyinstrument variability is small in relation to the variability of the systematic component. Focusing on the systematic portion of monetary policy actions amounts to specify what that Bernanke and Mihov (1998, pp. 897) call: ''a structural model identified by strong prior restrictions''. Our empirical analysis is therefore based on a model of this type. More precisely, the present paper is based on a structural theoretical model of the transmission mechanisms of monetary policy where both the interest rate and credit channels are effective. This model, which is introduced in Section 3, is inspired by BB's paper; it includes an inflation equation and an explicit monetary policy rule where the Central Bank controls a nominal interest rate. The methodology used for estimating and testing the model is based on cointegration techniques as proposed in e.g. Johansen and Juselius (1994). The econometric evidence is reported in Section 5 and a similar approach for investigating the effectiveness of the credit channel in Norvay is implemented in Bårdsen and Klovland (1998). Although in this type of models impulse responses with respect to shocks to the system might be easily derived, following the arguments in Ericsson et al. (1998) we chose to concentrate the analysis on the systematic part of the model. Some authors contend that macroeconomic time series are illsuited to identify a credit channel from a money channel, see e.g. Favero et al. (1999). The hinge of their argument is that the money channel operates through bank's liabilities whereas the credit channel operates through their assets; since assets and liabilities are tightly related by accounting identities, the identification of the two transmission mechanisms proves to be difficult. They suggest that microeconometric evidence from banks' balance sheets might help to solve the problem4. At the macro level, if use is made of a ''theory-based'' model where both the asset and liability component of banks' balance sheets are explicitly modelled, a credit channel can be identified from a money channel. This can be done by properly identifying the structural econometric relations and coefficients related to the asset and liability 4

Despite a similar balance sheet effect is present at the micro level, they argue that the effects of monetary policy on banks are heterogeneous, depending on the ''strength'' and ''size'' of their balance sheets. For instance, small banks find it costly to insulate their loans' portfolio from a Central Bank's liquidity restriction, therefore the identification of a credit channel should be easier.

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components of banks' balance sheets. The empirical evidence provides insights for assessing the validity of the chosen specification. 3. The model In this section we sketch a simple backward-looking dynamic model of the transmission mechanisms of monetary policy, where both the interest rate channel and the credit channel are effective. The model is specified as a dynamic simultaneous system of error correction equations where the growth rates of variables adjust with respect to disequilibria between lagged levels and the medium run target positions. Its theoretical underpinnings are derived from a variant of the BB's model as summarized in the Appendix A.1. The economy consists of four markets: the goods market, the money market, the credit market and the bonds market, and three operators: the Private Sector, the Banking System and the Central Bank. It is assumed that: (i) prices and wages are rigid in the short-medium term; (ii) expectations are adaptive; (iii) bonds and loans are not perfect substitutes for both lenders and borrowers and credit rationing phenomena are negligible5; (iv) the Central Bank's operative instrument is represented by a short term nominal interest rate and the stock of money is demanddetermined in the sense that the Central Bank supplies whatever amount of money is demanded by the Private Sector at the pre-set policy interest rate6; (v) the Banking System fixes the interest rate on loans through a loan price setting equation, interpretable as the ''Banking System's reaction function'' and the stock of loans is demand-determined in the sense that the Banking System supplies whatever amount of credit is demanded by the Private Sector at the pre-set interest rate on loans.

5

It is well recognized that phenomena of credit rationing occurred in the southern regions of Italy, especially for small-size loans, in the seventies and in the beginning of the eighties. Pittaluga (1991) shows that such ''equilibrium credit rationing'' phenomena decreased in the second-half of the eighties due to increased competition among commercial banks. 6

The eighties were characterized by a process of gradual lifting (culminated in 1990) of the restrictions on capital movements. Hence, even though the Bank of Italy has announced an annual target for the expansion of M2 since 1984, given the regime of semi-fixed exchange rates (at least up to 1992) and (quasi)-perfect mobility of capitals, the stock of money could not be completely controlled, as highlighted by the frequent failures in fulfilling the targeted ranges for the growth of M2, see e.g. Sarcinelli (1995). It seems reasonable to assume that also after the withdrawn from the EMS in 1992 the Bank of Italy shifted its actual attention on interest rates rather than on the quantity of money.

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Under (iv) and (v), issues of supply-demand identification should not arise both in the money and credit market. As regards (iv), the model we specify below partly recognizes that during the Italian participation to the EMS and the period preceding the EMU, the Bank of Italy tied closely its policy to the Bundesbank, see e.g. Clarida et al. (1997); indeed, though the equations that follow do not include foreign variables, the monetary policy rule is formulated in a way that the constraint given by the Bundesbank's policy is implicitly accounted. The rationale underlying (v) is that from the abolition of credit ceilings (1983) onwards, the Italian commercial banks operated almost unrestrictedly in setting the loan rate in a context of increasing competition. Let yt be (the log of) real output, mt (the log of) the real stock of money (including bank deposits), bt (the log of) the stock of total reserves held by commercial banks at the Central Bank (bt can be decomposed into two parts: required reserves i.e. a fraction of mt, and excess reserves7), lt (the log of) the stock of loans granted to firms, πt the inflation rate, it a short nominal interest rate, ρt the nominal interest rate on loans and iDt the nominal interest rate on bank deposits. All the stock variables are expressed in real terms. We denote by yt∗ the medium run level of aggregate demand, by mt∗ the medium run targeted level of money demand, by ρt∗ the medium run targeted level of the interest rate on loans, by lt∗ the medium run targeted level of loan demand and by rr the equilibrium level of the real interest rate accepted by the Central Bank in the medium run. The first part of the model is given by the following set of error correcting structural equations

∆yt = −ay,1∆(it−πt) − δy,1 (yt−1−yt−1∗) + vty

(1)

∆πt = δπ,1(yt−1−yt−1∗) + δπ,2(mt−1−mt−1∗) + δπ,3(lt−1−lt−1∗) − δπ,4(ρt−1−ρt−1∗) + vtπ

7

(2)

bt can be interpreted as the monetary base less currency.

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∆it = ai,1 ∆πt + ai,2 (∆bt − ∆bb) + ai,3 (∆mt − ∆mm) − δi,1 [(it−1−πt−1) − rr ]+ vti

(3)

where all the coefficients are positive, ∆ is the difference operator, ∆bb and ∆mm are respectively the targeted growth rates of total reserves and money, and vth, h=y,π,i, are white noise terms. Equation (1) is an aggregate demand equation where the growth rate of real output depends on the contemporaneous first difference of the (ex-post) real interest rate, (it−πt) and adjusts with respect to excess demand (yt−1−yt−1∗). (2) represents an inflation equation where several factors contribute to the dynamics of the acceleration rate, i.e. excess demand (δπ,1 > 0), excess money (δπ,2 > 0) and excess loans (δπ,3 > 0 ; δπ,4 > 0)8; here the constraints δπ,i= 0, i=2,3,4 would imply an aggregate supply curve, while the constraints δπ,1=δπ,3=δπ,4 = 0 would imply a variant of the ''P* model'', see e.g. Svensson (1999). The structure of the inflation equation (2) can be justified by observing that during the ''quite turbulent'' transition process towards the EMU, several factors other than excess demand influenced inflation dynamics in Italy. Equation (3) represents the Central Bank's monetary policy rule expressed as an error correcting rule, and where vti can be interpreted as the unsystematic component of monetary authorities' actions. The rationale for a policy rule of the form (3) is the following: the Italian commitment to the EMS and the Bundesbank's monetary policy resulted in high real interest rates necessary to sustain the exchange rate parity and the disinflationary process (see e.g. Clarida et al., 1997); the error correction reaction function (3) captures the idea that the Central Bank adjusted its policy rate to guarantee stationary fluctuations of the real interest rate (it−πt) around the level rr chosen in the medium run. Moreover, according to (3), the real growth rate of total reserves and money serve as indicators of monetary policy; whenever the growth rates of bt and mt exceed (are less than) the

8

This seems to be consistent with the Hendry's (1998) view that no single cause explanation sufficies for inflation dynamics. Observe that a level of ρt being less than (exceding) the desired equilibrium level, ρt∗, implies an increase (restriction) of the quantity of credit granted to firms for a given demand for loans schedule.

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targeted values, the Central Banks restricts (releases) its policy rate9. Observe that being an error correction equation (3) reflects the tendency of central banks to smooth changes in interest rates. Given (3) and the error correction nature of the specified equations, we can turn on (2) and include the quantity −δπ,5[(πt−1 − it−1) + rr] (δπ,5 > 0) as a further component of inflation dynamics; this factor reflects the capability of the Central Bank to control the acceleration rate of inflation through the management of the interest rate. The system (1)-(3) can be completed by the introduction of the following error correction money demand equation

∆mt = − am,1∆πt−1 + am,2∆lt−1+am,z′zmt−1 − δm,1(mt−1−mt−1∗) + vtm

(4)

where am,1, am2, δm,1 > 0, mt∗ represents the medium run level of money demand, and vtm is a money demand shock. In this framework, the quantity (mt−1−mt−1∗) reads as the (real) money gap, while the short run dynamics of ∆mt is influenced by lagged inflation acceleration rate and lagged growth rate of loans; again, zmt−1 in (4) is a vector containing variables affecting the short run dynamics of the money growth rate, and am,z is the corresponding vector of parameters. The second part of the model describes the behavior of the Banking System and includes an aggregate loan demand equation. In particular, we consider the following set of error correcting equations

∆bt = ab,z′zbt−1− δb,1(it−1−πt−1−rr) − δb,2 (mt−1−mt−1∗) + δb,3 (lt−1−lt−1∗)+vtb

(5)

9

The output gap does not appear explicitly in (3) as it would be the case in a coventional backward-looking Taylor rule; this assumption seems reasonable in that in the eighties and nineties the Bank of Italy focused, more or less directly, its primary interest on the task of fighting inflation.

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∆ρt = aρ,1∆it + aρ,z′zρt−1 − δρ,1(ρt−1−ρt−1∗) + vtρ

(6)

∆iDt = aiD,1∆it + aiD,z′ziDt−1− δiD,1(ρt−1−ρt−1∗) + v tiD

(7)

∆lt = −al,1∆ρt + al,2∆it + al,z′zlt−1 − δl,1(lt−1−lt−1∗) + vtl.

(8)

where all coefficients are positive and vth, h=b,ρ,iD,l are white noise terms. In each equation the vectors zht−1, h=b,ρ,iD,l contain lagged explanatory variables and ahz, h=b,ρ,iD,l are the vectors of corresponding parameters. Equation (5) can be interpreted as the Banking System's demand for total reserves, where the growth rate of total reserves depends inversely on the deviations of the real interest rate from the equilibrium level rr; the dependence of ∆bt from (it−1−πt−1) must be attributed to the part of bt consisting in excess reserves. In (5) the growth rate of reserves adjusts with respect to excess money (δb,2 > 0) and reacts to deviations of the loan stock from its medium run level (δb,3 > 0); we shall turn on this equation below. Given (v) above, equation (6) can be interpreted as the loan price setting equation; by using (6), the Banking System adjusts the loan rate with respect to its deviations from the targeted medium run level. The structure of (6) also points that monetary policy directly constraints the price of bank lending through the dependance of ∆ρt on ∆it. Equation (7) can be regarded as the Banking System's price setting equation for deposits. Its interpretation is similar to (6); adjustment takes place with respect to the gap between the interest rate on loans and its targeted medium run level because the dynamics of ρt and iDt are closely related. Finally, (8) is an error correcting loan demand equation where the signs associated with al,1 and al,2 mimic the signs of the coefficients associated with the theoretical counterpart (20) of Appendix A.1. In summary, the credit market is described by (6) and (8). Turning on equation (5), the adjustment behavior of ∆bt with respect to (mt−1−mt−1∗) and (lt−1−lt−1∗) can be justified in the light of the Banking System's balance sheet constraint. Indeed, mt is related to the liability side of the Banking System's aggregate balance sheet, whereas bt 612

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and lt are related to the asset side; when the stock of money exceeds its medium run level, the quantity ∆mt adjusts in equation (4) and from the balance sheet constraint it follows that, ceteris paribus, also ∆bt adjusts in (5). On the other hand, when the stock of loans exceeds its medium run level, ∆lt adjusts in (8) and, ceteris paribus, ∆bt increases in (5). The dynamic system (1)-(8) does not specify the determinants of the quantities yt∗, mt∗, ρt∗ and lt∗. Potential output, yt∗, is defined as the level of yt that would arise if wages and prices were perfectly flexible10; our specification of yt∗ is inspired by the CC curve (24) (which is here preferred to the specification (23) for identification issues) reported in Appendix A.1, and it is equal to yt∗ = −β y1(it−πt) + β y2bt + β y3t

(9)

where the parameters βy1, βy2, and β y3 are assumed to be positive. Observe that the parameter βy2 in (9) reflects the intuition, implicit in the ''strong version'' of the credit view11, that monetary policy can affect the goods market by modifying the supply of loans through bank reserves. As regards mt∗, the medium run level of the real stock of money in equation (4) is assumed to be generated by the equation

mt∗ = yt − β m1 (it − iDt) − β m2πt

(10)

where the parameters β m1, β m2 are expected to be positive. The medium run level of the interest rate of loans of equation (6) is inspired by the theoretical counterpart (19) of Appendix A.1, and specified as In large part of applied research, the standard practise is to measure yt∗ by means of a Hodrick-Prescott filter with smoothing parameter set to 1600; we refer, inter alia, to Cogley and Nason (1995) for the drawbacks associated with the use of Hodrick-Prescott-type filters.

10

11 It is possible to distinguish between a ''strong version'' and a ''weak version'' of the credit view; the BB's model can be recast within the former.

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ρt∗ = it + βρ1iDt + β ρ2(yt − bt)

(11)

where the quantity (yt−bt) can be interpreted as the ''velocity'' of reserves and β ρ1,βρ2 > 0. The structure of (11) reflects the medium run impact of the policy rate it on ρt∗, reproducing the structure of (6) and the mark-up behavior of commercial banks, which regards the level of the deposits interest rate iDt as the lower threshold of their financial intermediation activity. Finally, the quantity lt∗ in (8) is specified as

lt∗ = βl1it − β l2ρt + β l3yt

(12)

and provided β li > 0, i=1,2,3, it can be regarded as the medium run empirical counterpart of equation (20) of Appendix A.1. Here the restriction βl3=1 would imply the medium run homogeneity of the demand for real loans with respect to real income; as in the conventional money demand framework, the quantity (lt−yt) could be interpreted as the inverse of the ''velocity of credit''. The remaining determinants of the model, zht−1,h=m,b,ρ,iD,l, are specified in Section 5.2. Finally, before discussing the econometric specification of the dynamic system (1)-(8) with (9)-(12), we briefly underline the role of parameters which are mostly involved in the functioning of the credit channel. Observe that equation (5) shows that an expansionary monetary policy at time t (i.e. a reduction of it by equation (3)) increases bank reserves (δb,1 > 0) at time t+1, and equation (9) reveals that if β y2 > 0, the increase of bank reserves has a direct impact on yt+1∗ and consequently on (yt+1−yt+1∗); this will cause an increase of ∆yt+2 because of the dynamic structure of adjustment in (1) (δy,1 > 0). This channel amplifies the effects of the interest rate channel which affects real output by the effect of ∆it on ∆yt in (1) (ay,1 > 0), and by the effect of the interest rate on yt+1∗ in (9) (β y1 614

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> 0). As a consequence, also the effects of monetary policy on ∆πt in equation (2) (δπ,1 ≠ 0) are amplified. Moreover, an expansionary monetary policy has direct consequences on the credit market through (6)-(11) and (8)-(12); whether in (2) δπ,3 > 0 and δπ,4 > 0, the disequilibrium on the credit market impacts on the dynamics of ∆πt. 4. Institutional and historical background In the last decades the Italian monetary system has experienced different regimes characterized by different policy instruments and institutional constraints (see, inter alia, Sarcinelli (1995), Bertocco (1997) and Juselius (1998)); this complicates the task of disentangling the different operating mechanisms of monetary policy. In the sixties and seventies the efforts of the monetary authorities were mainly directed at preventing disequilibria in the balance of payments; the Bank of Italy's Annual Reports in this period suggest that the quantity of credit, rather than the quantity of money, was regarded as the intermediate target of monetary policy12. From 1973 to 1983 the banking system was subject to an administrative ceiling on the expansion of credit and a security investment constraint. In the first part of the eighties the Italian economy experienced an high inflation rate and increasing public deficits. The monetary policy was thus modified in order to face these major problems. The ''divorce'' between the Bank of Italy and the Treasury occurred in the third quarter of 1981, and represented an important step towards the complete autonomy of the Central Bank in conducting the monetary policy and resulted in fundamental changes in the structure of the money market. From the mid eighties, monetary policy became more dependent on the exchange rate market, due to the joining of the ERM in 1979, and the subsequent gradual lifting of credit and capital restrictions. In this period the exchange rate parity was one of the main concern of the Central Bank, who also used the quantity of money as an intermediate target of monetary policy; since 1984 an annual target for the expansion of M2 was officially declared (along with a target for the expansion of credit to the Private Sector). The credit ceilings were removed in the third quarter of 1983 and re-introduced occasionally in the first semester of 1986 and from the third 12

See e.g. Cotula and Micossi (1977).

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quarter of 1987 to the first quarter of 1988. The removal of credit ceilings increased competitiveness among banks, inducing a very large reduction of bank holdings of government bonds through the second half of the eighties. The Italian Lira was later withdrawn from the ERM in 1992, and the interest of the Italian Monetary Authorities shifted even more on the quantity of money and interest rates. Thereafter the nominal exchange rate anchor was substituted by an anti-inflationary target, and the Bank of Italy implemented an increasingly austere policy in order to meet the Maastricht criteria in the wake of Stage III of EMU. 5. Econometric specification The simultaneous system of error correcting equations (1)-(8) with (9)-(12) represents a very simplified description of the domestic transmission mechanisms of monetary policy; it abstracts from the expectations channel of monetary policy and depicts a closed-model economy where the credit market strengthens the effects of monetary policy. Foreign variables such as the real exchange rate, export demand and measures of competitiveness are ignored; nevertheless, as seen before, domestic interest rates and the inflation rate partly reflect the influence of foreign factors experienced by Italy during 1983 - 1998. In this section we present the empirical analysis. More precisely, in Section 5.1 we estimate the medium run relations (9)-(12) of the system through cointegration techniques and test whether these are consistent with the features of observed data. After substituting the estimated relations (9)(12) into the simultaneous system of error correcting equations (1)-(8), in Section 5.2 we estimate the structural model of the transmission mechanisms of monetary policy and provide a test for the implied overidentifying restrictions. We consider a system including the variables Xt=(yt, πt, it, mt, bt, ρt, iDt, lt)′, where it is a short nominal interest rate13, the stock of money mt 13

As detailed in the Appendix A.2, it is here measured as an average interest rate on Treasury Bills with maturity less than one year. This choice is motivated by the fact that due to the dimensions of the system, we could not include in Xt both the actual interest rate controlled by the Central Bank (the Bank of Italy's repurchase agreement operations or an overnight interest rate) and an interest rate on Government bonds. The approximation of the policy rate with an interest rate on short run government bonds can be justified in the light of equation (15) of Appendix A.1, for a value of ω close to zero. For

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is measured as M2 without certificates of deposit and lt includes credit granted to both firms and households14. A complete description of the data can be found in the Appendix A.2; the variables in levels and first differences are plotted in Fig. 1a-1b and 2a-2b. The DGP for Xt is assumed to belong to the class of VAR processes of the type

∆Xt = α(β′,ω ,κ) (X′t−1, d92t , t) ′ + Φ(Z′t, d′t) ′ + εt

(13)

where εt is i.i.d. N(0,Ω), Zt=(∆Xt−1′, ..., ∆Xt−k+1′)′, dt=(d1t,d2t,d3t, 1,dceilt)′ with dit i=1,2,3 centered quarterly dummies and dceilt the difference of the dummy variable for the presence of the occasional credit ceilings of 1986:1 - 1986:2 and 1987:3 - 1988:1. Φ = (Γ1, ..., Γk−1, ϒ) with ϒ matrix of parameters associated with dt, and d92t is a dummy variable introduced for the speculative attack on the Italian lira of 1992, see Fig. 4b. The Error Correction Model (ECM) (13) can be interpreted as the reduced form of the ECM structural system of equations

A0∆Xt = δ (β′, ω, κ) (X′t−1, d92t , t) ′ +A(Z′t, d′t) ′ + vt

(14)

where δ = A0α, A=A0Φ = (A1,...,Ak−1, ϒ), vt = A0εt, and A0 is a non singular matrix. The inclusion of the restricted trend component in (13) (and (14)) is justified by the structure of equation (9). The inclusion of the restricted dummy d92t aims to capture the effects of the exchange rate speculative attack against the Italian lira observed between the end of 1991 and the beginning of 1993; this attack resulted in a sharp increase of the real Italy this approximation seems reasonable in that in the eighties and nineties interest rates on Treasury Bills with maturity less than one year were remarkably affected by the Bank of Italy's policy. In the empirical analysis below all interest rates are divided by 100. 14

The level of indebtness of Italian households is traditionally negligible, so the variable lt can be reasonably interpreted as the amount of credit granted to firms. As regards the measure of money, it is worth noting that in recent years M2 growth has been mainly determined by the growth of certificates of deposits with maturity longer than eighteen months.

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interest rate (it−πt) (see Fig. 4b). The calculation were performed in PcFiml 9.0, see Hendry and Doornik (1996) and Gauss 3.01. 5.1 Evidence on the medium run relations An unrestricted VAR of the form (13) with k=2 lags was first fitted to the data for the sample 1983:3 to 1998:2. Diagnostics on the residuals of this system indicate the absence of autocorrelation and the symmetry of the innovations distribution, although they signal a possible presence of leptokurtosis. These findings suggest that the chosen lag specification may be appropriate in describing the dynamics of Xt. A detailed description of the determination of the cointegration rank, r, of model (13) is reported in the Appendix A.3 where we selected r=5 and possibly a common I(2) trend. The choice r=5 is consistent with the information implied by the four medium run equations sketched in Section 3 and the stationarity of the real interest rate. As known from theory of I(2) systems, all the inference on the medium run relations in β of model (13) reported in the following is robust with respect to the possible presence of an I(2) common trend. For k=2 and r=5, we next specified in the ECM (13) a set of identification restrictions of the type β = (β y, β m, β ρ, β l, β i−π), with β h=Hhϕh, h=y,m,ρ,l,(i−π) (see e.g. Johansen, 1995). In particular, β y′Xt−1 was formulated as (yt−1−yt−1∗) with yt∗ given by (9); β m′Xt−1 was formulated as (mt−1−mt−1∗) with mt∗ given by (10), including also the possibility that β m2=0 and the possibility of a linear trend as an approximation of the effects of financial innovation15; β ρ′Xt−1 was formulated as (ρt−1−ρt−1∗) with ρt∗ given by (11); β l′Xt−1 was formulated as (lt−1−lt−1∗) with lt∗ given by (12) and including also a linear trend since the model ignores foreign variables which might affect the demand for loans; β i−π′Xt−1 was specified as (it−1− πt−1− βdd92t−rr) where d92t is the dummy introduced in order to

15

Although a linear trend may be viewed as a rather imprecise description of the process of financial innovation which characterized the Italian monetary and financial markets since the secondhalf of the seventies, its inclusion in the medium-long run money demand equation aims to capture the inverse velocity decline that it is hard to explain in terms of opportunity cost variables only (see the graphs of (it−iDt), πt and (mt−yt) in Fig. 1 and 4). In the spirit of Baba et al. (1992), Angelini et al. (1994) model the effects of financial innovation through nonlinear transformations of interest rates.

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account for the speculative attacks against the Italian lira around 1992, and βd is the corresponding coefficient16. The whole specification β = (β y, β m, β ρ, β l, β i−π) is summarized in Table 3 and the corresponding estimated βs are reported in Table 4 17; the systems of simultaneous relations β ′Xt−1 is identified and all the estimated coefficients are significant at conventional levels (except the estimated β m1, whose t-value is equal to 1.88) and the LR test of the overidentifying restrictions, which is asymptotically χ2(14) under the null, is equal to 20.51 with a p-value of 0.1149. Thus the theoretical formulation of the medium run structure of (13) seems to be consistent with the model with unrestricted β of dimension r=5. Moreover, the estimated coefficients are interpretable as predicted by (9)-(12). The estimated deviations from equilibria, eq1 = (yt−1−yt−1∗), eq2 = (mt−1−mt−1∗), eq3 = (ρt−1−ρt−1∗), eq4 = (lt−1−lt−1∗) and eq5 = (it−1−πt−1−0.029d92t) are plotted in Fig. 6a. More precisely, Fig. 6a plots the estimated β′Rt∗, where Rt∗ is (Xt−1′, d92t, t)′ corrected for the constant and seasonal dummies (dubbed eqisa), whereas Fig. 6b reports the profiles of the estimated β′Rt, where Rt is (Xt−1′, d92t, t)′ corrected for the constant, seasonal dummies and Zt (dubbed eqiDa). In both cases the estimated β corresponds to the values of Table 4. The five deviations appear substantially mean-reverting; only for the β y′Xt−1= (yt−1−yt−1∗) relation there seem to exist a visually evident difference between β y′Rt∗ and β y′Rt, thus indicating the possibility of multicointegration, which is a possibility under the I(2) interpretation of (13).

5.2 Evidence on the system of error correction equations Although a possible I(2) common trend could characterize the variables in Xt as detailed in the Appendix A.3, for simplicity in this

16

This specificatiton is consistent with the structure of (13) ((14)), where d92t is restricted to belong to the cointegration space.

17 The estimation of β = (β y, βm, β ρ, βl, βi−π), was carried out with an unrestricted β m2 in the βm excess money demand relation (10) and then set to zero. Indeed, the estimate of β m2 did not exhibit the expected sign in that πt may be a poor measure the opportunity cost of holding deposits and cash relative to real stock in Italy over the period 1983:3 1998:2.

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section the empirical investigation will be conducted as if (13) and (14) were I(1) 18. By replacing (β′, ω ,κ) by the super-consistent estimates of Table 4, we estimated the remaining parameters (α, Φ) of the ECM (13). All short run equations fit rather well, as reported in Table 6 and Fig. 7a. The estimated α adjustment coefficients of the reduced form ECM (13) are reported in Table 5. In order to analyze the constancy of the model over the period 1992:1 - 1998:2, we carried out a recursive analysis on the parameters (α, Φ) of (13) after fixing (β′,ω ,κ) at the estimates of Table 4. The recursive residuals are shown in Fig. 7b. The graphs suggest that only the ∆bt equation exhibits weak signs of short run instability around 1994 19. On the whole, the eight recursive residuals seem to confirm the constancy of the model. In order to estimate the structural model (1)-(8) we focus on (14), where the medium run parameters (β ′,ω ,κ) are fixed at the estimates of Table 4. The matrices (A0, δ, A), A=(A1,Υ∗), in (14) are specified consistently with the structure of the equations (1)-(8), leaving open the possibility for zht−1, h=m,b,ρ,iD,l to be determined by the data. The Constrained FIML estimates of (A0, δ, A1) of model (14) are reported in Tables 7, 8 and 9 (for simplicity we do not report the coefficients of Υ∗ associated with the deterministic components of the model, dt); the system is identified, all the coefficients are significant, and the LR test of the overidentifying restrictions, which is asymptotically χ2(79) under the null, was equal to 91.68, with a p-value of 0.1557. On the whole, the system estimated in the Tables 7, 8 and 9 reproduces the structure of the theoretical counterpart (1)-(8) with expected signs. In particular, the parameters mostly involved with the function of the

18 The analysis of the I(2) structure of the model is a subject for future research. From the statistical point of view, if an I(2) common trend really characterizes (13) and inference is carried out as if it was I(1), the consequences are the following: the (full information) maximum likelihood estimator of the parameters (α, Φ) and (A0, δ, A) in (13) and (14) retains the property of consistency but its asymptotic distribution is non standard. Thus the t-statistics reported in the Tables 5, 7, 8 and 9 below should be interpreted with caution. 19 Note that in 1993 an institutional reform lowered the average bank's required reserve coefficient from about 17.8% to 13.4%.

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NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

credit channel highlighted at the end of Section 3, exhibit expected signs and reasonable magnitude. A remarkable difference with the theoretical counterparts (1)-(8) occurs in the money demand equation (fourth row of Tables 7, 8 and 9) where adjustment takes place not only with respect to the real money gap, (mt−1−mt−1∗), but also with respect to the excess demand, (yt−1−yt−1∗). We do not have a clear explanation for such behavior of ∆mt; this evidence points that the specification of short run money demand relations ought to take account of deviations from medium run equilibria in the credit and commodities markets. The inflation equation (fourth row of Tables 7, 8 and 9) reproduces the structure of (2) and shows that excess aggregate demand has the major impact on the dynamics of the acceleration rate, however, also excess money and excess loans have significant effects on ∆πt. Moreover, the quantity [(πt−1−it−1)+rr] enters significantly the ∆πt equation reflecting the Central Bank's control over the acceleration rate of inflation during the investigated period. Given the estimates of Tables 7, 8 and 9, we tested for the ''aggregate supply'' constraints δπ,2=δπ,3=δπ,4=δπ,5=0 and the ''P* model'' constraints δπ,1=δπ,3=δπ,4=δπ,5=0. We rejected both; in the first case, the corresponding LR test of the over-identifying restrictions, which is asymptotically χ2(83) under the null, was equal to 149.29 with a p-value close to zero and in the second case it was equal to 148.84 with a similar pvalue.

6. Conclusions The purpose of this paper was to investigate the domestic transmission mechanisms of monetary policy in Italy, before Stage III of EMU. By using cointegration techniques and a dynamic simultaneous system of structural error correcting equations, the attention was focused on the aggregate demand channel, with the aim of assessing whether a credit channel has been working in Italy in parallel to the interest rate channel. We focused on the period 1983:3 - 1998:2, i.e. after the removal of credit ceilings which characterized the Italian system for large parte of the seventies and used the Bernanke and Blinder (1988) model as a benchmark 621

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''structural'' theoretical guideline. We specified and estimated a small simultaneous dynamic model of the Italian economy over quarterly data, where a loan channel supplements the interest rate channel. Although the period 1983:3 - 1998:2 is characterized by different regimes of monetary policy and rather ''turbulent'' episodes, our results seem to be consistent with a framework where monetary policy actions directly affected bank lending with consequences on real output and inflation dynamics. The functioning of the credit channel helps to explain why monetary policy in Italy maintained a substantial degree of effectiveness with respect to domestic objectives (real output and inflation) in a regime of (quasi)fixed exchange rates. Our results also suggest that any policy model attempting to evaluate monetary policy rules in Italy cannot avoid modelling the credit market.

A Appendix A.1 Theoretical model In this section we sketch a simple benchmark model of the credit view of monetary policy. This model represents the guideline for the specification of Sections 3 and 5, and is directly inspired by BB's paper. Some adaptations and modifications to the original BB's model are introduced; open-economy versions of the BB's model can be found in Bårdsen and Klovland (1998) for the Norvegian economy, and in Chiades and Gambacorta (1999) for the Italian economy. The economy consists of four markets: the goods market, the money market, the credit market and the bonds market, and three operators: the Private Sector, the Banking System and the Central Bank. The model assumes that bonds and loans are not perfect substitutes for both lenders and borrowers and ignores credit-rationing. In equilibrium one of the four markets can be modelled residually; we model the bonds market residually. The Central Bank's operative instrument is represented by a short term nominal interest rate denoted by i. Let iA be the interest rate on bonds; it is assumed that the two interest rates are linked by the relation

622

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

iA= g(i+) + ω

(15)

where g(·) is a function measuring the capability of the Central Bank to affect the term structure (henceforth the expected signs of the first derivatives are reported near arguments) and ω measures the portion of iA which is not subject to the control of the Central Bank; the shorter is the maturity of bonds, the grater is the influence of the Central Bank over iA implying a small ω. The credit market The credit market is characterized by the behavior of the Banking System and the Private Sector. The simplified balance sheet constraint of the Banking System is given by Ad + Ls +τ DEP + Ed = DEP

(16)

where the left-hand-side contains assets, i.e. the net holdings of bonds, Ad, the supply of loans, Ls 20, required reserves, τDEP (τ is the fraction of required reserves), the demand for excess reserves, Ed, and the right-handside contains liabilities, i.e. the stock of bank deposits, DEP. It is assumed that the Banking System holds excess reserves Ed and bonds Ad equal to

Ed =ξ(i-)(1−τ)DEP

(17)

Ad = ς (iA+) (1−τ) DEP

(18)

where (1−τ)DEP is the stock of deposits free of reserve requirement and ξ(·) and ς (·) are functions measuring respectively the desired shares of excess reserves and bonds over (1−τ)DEP. 20

Loans supplied by foreign institutions are not considered here.

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The Banking Systems fixes the price of credit through the loan price setting equation

ρ = φ(iA+ , iD- , Y+ , B- )

(19)

where ρ is the interest rate on loans, Y is real GDP and B = τDEP + Ed is the stock of total reserves. Equation (19) contains Y and B as determinants of the price of credit; the inclusions of B maintains that owing to reserve requirement (τDEP) and its influence on excess reserves (Ed), monetary policy affects the interest on loans and hence the availability of bank credit. Moreover, by substituting (15) into (19) it turns out that the policy rate influences the price and thus the supply of credit. Finally, the inclusion of iD as a determinant of ρ tries to capture the mark-up behavior of commercial banks, which regards the level of the deposits interest rate as the lower threshold of the financial intermediation activity. The demand for loans of the Private Sector is generated by the equation

Ld = L(ρ- , iA+ , Y+ )

(20)

where Y is included in order to capture the transaction demand for credit. The credit market is thus described by equations (19) and (20); this implies that the stock of loans observed on the credit market, L, is demand determined in the sense that the Banking System supplies whatever amount of credit, Ls, the Private Sector demand at the pre-set value of ρ.

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NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

The money market The money market is characterized by the behavior of the Central Bank and the Private Sector. Ignoring cash for simplicity, the demand for money coincides with the demand for deposits of the Private Sector, i.e.

DEPd = D( iA−iD - , π- , Y+)

(21)

with (iD − iA) a measure of the opportunity cost of holding money and π the inflation rate. The Central Bank fixes i through a policy rule of the form

i = f( π−π∗+ , ∆DEP−∆DEP*+ , ∆B−∆B*+)

(22)

where here the difference operator, ∆, denotes growth rates and the asterisk denote targeted values. The money market is given by (21) and (22), so the stock of deposits observed on the money market, DEP, is demand determined in the sense that the Central Bank supplies whatever amount of deposits, DEPs, the Private Sector demand at the pre-set value of i. The goods market The goods market is characterized by the behavior of the Private Sector and is described by the equation

Y=Y[ iA- , ρ- ]

(23)

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which differs from a standard IS relation because of the inclusion of the interest rate on loans. The appearance of ρ in (23) is justified by observing that if bonds and loans are not perfect substitutes, firms' investment decisions depend on both the loan and bond interest rates. Equation (23) can be transformed into the so-called CC curve, i.e. the curve for commodities and credit. Indeed, by substituting (19) into (23), one obtains

Y = Y[iA , φ(iA , iD, Y , B)] = Y∗[iA , iD , B]

(24)

where

∂Y∗/ ∂iA = (∂Y / ∂iA) + (∂Y / ∂ρ) (∂φ / ∂ iA) < 0 ∂Y∗/ ∂B = (∂Y / ∂ρ) (∂φ / ∂ B) > 0

The important feature of (24) is that Y can be influenced also through credit-market actions or shocks that affects either the loan price setting (19) or the demand for loans (20). The CC curve reduces to a conventional IS curve if commodity demand is insensitive to the loan rate, i.e. ∂Y/∂ρ = 0 (Þ ∂Y/∂B=0). Thus necessary conditions for a lending channel to be operational are:

−∞ < ∂Ld/∂ρ < 0

(25)

and

−∞ < ∂Y/∂ρ < 0 626

or

0 < ∂Y/∂B < ∞

(26)

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

Aggregate supply The original BB model can be completed by the following aggregate supply equation

π = πc+δ(Y−Yp)−1

(27)

where actual inflation, π, depends upon core inflation, πc, and the lagged output gap (Y−Yp)−1, with Yp potential real GDP. Core inflation in (27) will be a function of conditions in all markets and in particular of conditions in the labor market; it should also reflect expectations on inflation dynamics. Since the model does not describe the behavior on the labor market, the simplest way to specify πc is: πc=π−1, where π−1 is the lagged level of inflation. As regards Yp, in this paper it will be assumed that in the medium run Yp can be approximated by the CC curve (24). A.2 The data The data set consists of quarterly observation for 1974:4 to 1998:2. lt=log(Lt/Pt), yt=log(Yt/Pt), bt=log(Bt/Pt), mt=log(M2t/Pt), πt=log(Pt/Pt−1), Lt total amount of loans granted by banks to Italian residents (source: Bank of Italy), Yt GDP (seasonally adjusted) (source: ISTAT), Bt total amount of reserves held by banks at the Central Bank (source: Bank of Italy), M2t stock of money measured as the official M2 less the stock of certificates of deposits of all maturities (source: Bank of Italy), Pt implicit DGP price deflator (source: ISTAT), ρt average interest rate on loans (source: Bank of Italy), it average interest rate on Treasury Bills with maturity 3, 6, and 12 months (BOT), (source: Bank of Italy), iDt average interest rate on deposits (source: Bank of Italy).

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A.3 Determination of the cointegration rank In this Appendix we discuss the determination of the cointegration rank of the ECM used for investigating the transmission mechanisms of monetary policy in Italy. Given Xt=(yt, πt, it, mt, bt, ρt, iDt, lt)′ defined as in Section 5, an ECM of the form (13) with k=2 lags was fitted to the data for the sample 1983:3 to 1998:2. The modulus of the reciprocals of the 7 largest characteristic AR roots of the estimated unrestricted system (13) were 1.01, 0.89 (twice), 0.86 (twice), 0.61 (twice); all the AR roots are plotted in Fig 5a). The sequence of LR trace tests for cointegration rank is reported in Table 1, along with the corresponding I(1) asymptotic critical values. On the basis of these critical values, the LR test of r ≤ 5 is significant at the 5% level, while the LR test of r ≤ 6 is insignificant. If the system was I(1), r=5 would imply 3 roots at unity, while r=6 would imply 2 roots at z=1. Estimating (13) with r=5, gave the following 7 largest-in-modulus reciprocal AR roots: 1 (3 times), 0.992, 0.754 (twice). The AR characteristic roots of the restricted system are shown in Fig. 5b). Estimating (13) with r=6, gave the following 7 largest-in-modulus reciprocal AR roots: 1.0269, 1 (twice), 0.869, 0.665 (twice). Thus both for r=5 and 6 there is evidence of additional unit roots at z=1, which are not connected with the rank of αβ′. We interpret this evidence as suggesting the presence of an I(2) common trend. If a single I(2) trend is present, r=5 would imply 4 roots at z=1, conformably with the previous evidence. This specification is therefore the preferred one in the rest of the analysis. The visual inspection of the series in first differences reveals that at least lt shows signs of I(2)-ness, see Fig. 2a and 2b. Thus, though the system does not include nominal variables in levels, a possible I(2) common trend could characterize the variables in Xt. This results is not completely surprising and admits an economic interpretation. For instance, looking at the graphs of Fig. 1, 2, 3 and 4, it can be seen that over the period 1983:3 - 1998:2 the dynamics of the growth rate of real loans, ∆lt, might be reasonably approximated by an I(1) process. In general, the presence of I(2) components within real (deflated) aggregates seems justifiable on the basis of a slowly changing mean growth rate of variables, which may well characterize transition periods. Indeed, after the removal of credit ceilings in 1983:3, commercial banks adjusted their portfolio assets by reducing the large security holdings they had accumulated under 628

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direct credit control; this caused, in a context of increasing competition and deregulation, a relevant shift in the quantity of loans which lasted until the first part of the nineties when the Italian Banking System aggregate balance sheet reached a new equilibrium, see e.g. Buttiglione and Ferri (1994). This transition process is fully reflected on the dynamics of loans. In order to abstract from the strong seasonal component of ∆lt, we also plotted ∆4lt = lt−lt−4 in Fig. 3a; the fourth differences of the remaining quantity variables mt, yt, bt are also reported. Fig. 3b reports the first differences of lt, mt, yt, bt least squares corrected for the constant and seasonal dummies. All the graphs in Fig. 3a and 3b suggest that quantity variables may have a (possibly small) I(2) component. In the following we will consider both the I(1) and the I(2) interpretation of the system. In the latter case we will maintain a single I(2) common trend, p−r−s=1, in the notation of Johansen (1996), given the evidence on the 4 unit roots present in the system with r=5; this case allows for the possibility of multicointegration, see e.g. Johansen (1995, 1997) and Paruolo (2000). We thus re-examined the LR tests in Table 1 with the I(2) critical values with p−r−s=1. More precisely, Table 1 reports the sequence of LR trace tests for cointegration rank with the corresponding I(1) 5% and 10% asymptotic critical values, along with the sequence of LR trace tests for cointegration rank under the hypothesis of an I(2) trend with the corresponding I(2) 5% and 10% asymptotic critical values; the asymptotic critical values were taken from Rahbek et al. (1999). With these critical values the hypothesis r ≤ 5 is not rejected at both the 5% and 10% critical values, while r ≤ 4 is not rejected at the 5% critical level only. This is interpreted as further evidence in favor of r=5 cointegration relations under the hypothesis of a single I(2) common trend, i.e. p−r−s=1. In order to further investigate the presence of I(2) common trends we also calculated the Sr,s statistic of Rahbek et al. (1999), reported in the upper panel of Table 2; here r is the number of I(0) relations, s is the number of I(1) common trends and p−r−s is the number of I(2) common trends of the system. The lower panel of the table reports the ratio of the obtained Sr,s statistic to the 10% asymptotic critical values, a value greater than 1 indicating rejection of the hypothesis. The overall strategy of testing row-wise from the upper left hand corner to the lower right hand corner is usually employed in absence of prior knowledge; this procedure would select the value (r,s)=(3,3), corresponding to 7 unit roots in the system. Such a conclusion is not in line with the observation of 4 roots at z=1 in the system estimated under the hypothesis r=5. It can be observed that, 629

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although strictly less than 1, the values of the ratios of Sr,s to the critical values are close to 1 in the rows corresponding to r=3 and 4. Since the testing sequence is based on the consistency of the tests corresponding to all the hypothesis preceding the correct one, this evidence could be consistent with r > 4 due to the small sample size used in the analysis. A similar phenomenon occurs in the row corresponding to r=5 where both the S5,1 and S5,2 statistics are less than the asymptotic 10% critical values. Since a priori a single I(2) common trend is more interpretable on economic grounds, we favored p−r−s=1 and r=5.

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NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

r≤j trace

0

1

2

283.7 201.3 153.70

3 112

4

5

6

7

73.28 46.18 21.91 7.35

10% I(1) c.v

176.6 141.0 110.42 83.2 59.14 39.06 22.76 10.49

5% I(1) c.v

182.8 146.8 114.90 87.3 62.99 42.44 25.32 12.25

10% I(2) c.v

195.7 158.8 125.79 96.5 71.33 49.69 31.61 17.57

5% I(2) c.v

202.1 164.6 130.93 101.4 75.30 53.19 34.36 19.87

Table 1: Lr cointegration rank tests; the I(2) critical values are taken from Rahbek et al. (1996). The I(2) critical values correspond to p− −r− −s=1 common I(2) trend.

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p−r

R

8

0

7

1

6

2

5

3

4

4

3

5

2

6

1

7

p−r−s

650.4

8

p−r

r

8

0

7

1

6

2

5

3

4

4

3

5

2

6

1

7

p−r−s

Sr,s

1.50

8

537.3

451.5

392.9

336.6

299.2

265.40 239.3

225.7

532.3

427.6

352.8

296.5

248.0

216.30 190.2

176.8

395.0

308.3

248.0

199.2

164.60 145.5

132.1

275.3

191.6

143.7

109.20 94.6

92.8

193.9

115.2

80.90

65.1

63.1

86.1

52.00

37.6

36.0

50.60

18.9

17.0

20.1

6.5

7

6

5

4

Sr,s /

cv10%

3

2

1

0

1.38

1.30

1.27

1.22

1.22

1.21

1.22

1.28

1.55

1.41

1.32

1.27

1.21

1.20

1.20

1.25

1.51

1.36

1.26

1.18

1.13

1.16

1.20

1.43

1.18

1.05

0.95

0.98

1.12

1.47

1.07

0.92

0.91

1.07

1.05

0.81

0.76

0.92

1.14

0.60

0.75

1.14

0.62

1

0

7

6

5

4

3

2

Table 2: 2SI2 tests on r and s (upper panel) and the ratio of the test statistic to the 10% asymptotic critical values tabulated in Rahbek et al. (1999) (lower panel). Here r is the number of I(0) relations, s is the number of I(1) common trends and p− −r− −s is the number of I(2) common trends.

632

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

Var.

yt−1

πt−1

it−1

mt−1

bt−1

ρt−1

iDt−1

lt−1

d92t

t

β y′

1

−β y1

β y1

0

−β y2

0

0

0

0

−β y3

β m′

−1

β m2

β m1

1

0

0

−β m1

0

0

β m3

β ρ′

−β ρ2

0

−1

0

β ρ2

1

−β ρ1

0

0

0

β l′

−1

0

−β l1

0

0

β l2

0

1

0

−β l3

β i−π′

0

−1

1

0

0

0

0

0

βd

0

Table 3: Identifying restrictions on the matrix β of system (13), as suggested by the equations (9)(12) and the stationarity of the real interest rate.

Var.

yt−1

πt−1

it−1

mt−1

bt−1

ρt−1

iDt−1

lt−1

d92t

β y′

1

-0.45

0.45

0

-0.064

0

0

0

0

-0.0059

βm′

-1

0

0.53

1

0

0

-0.53

0

0

0.0084

βρ′

-0.028

0

-1

0

0.028

1

-0.37

0

0

0

βl′

-1

0

-15.1

0

0

7.5

0

1

0

-0.014

βi−π′

0

-1

1

0

0

0

0

0

-0.029

t

0

Table 4: Estimated β of system (13) corresponding to the specification of Table 3; in the second row β m2 was estimated unresctrictedly and then set to zero; all estimated coefficients are significant at all conventional levels, except the estimated β m1 whose t-value is equal to 1.88.

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α Eq./Var.

eq1

eq2

eq3

eq4

eq5

∆yt

0.112

-0.007

0.000

0.026

0.001

∆πt

0.919

0.425

-2.213

0.138

0.355

∆it

0.399

0.042

-0.944

0.103

-0.277

∆mt

-0.929

-0.352

0.016

-0.038

-0.002

∆bt

1.222

-1.467

-0.031

0.483

-0.005

∆ρt

0.276

0.056

-0.954

0.06

-0.127

∆iDt

0.022

0.022

-0.284

0.016

-0.043

∆lt

0.11

-0.054

0.016

-0.07

-0.002

Table 5: Estimated α of system (13) with eq1, eq2, eq3, eq4 corresponding to the disequilibria stemming from Table 4. Bold entries correspond to t-ratios greater than 2 in absolute value.

∆yt

∆πt

∆it

∆mt

∆bt

∆ρt

∆iDt

∆lt

0.66

0.79

0.79

0.97

0.82

0.91

0.83

0.94

Table 6: Correlations between actual and fitted values.

634

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

A0 Eq./ Var.

∆yt

∆πt

∆it

∆yt

-1

0.22

-0.22

∆πt

-1

∆it

0.789

-1

∆mt

∆mt

∆bt

0.149

0.041

∆ρt

∆iDt

∆lt

-1

∆bt

-1

∆ρt

0.521

∆iDt

0.204

∆lt

3.7

-1 -1 -6

-1

Table 7: Constrained FIML estimate of the matrix A0 of system (14); empty entries denote zeroes, all coefficients have t-ratios with absolute value greater than 2 exept italic entries have t-rations with absolute value between 1.5 and 2.

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δ Eq./Var.

eq1

∆yt

-0.071

∆πt

0.679

eq2

0.253

eq3

-1.28

eq4

0.101

∆it ∆mt ∆bt

0.286 -0.470

-0.785

-0.267 -1.91

0.308

∆ρt

-0.274

∆iDt

-0.092

∆lt

eq5

-0.51

-0.121

Table 8: Constrained FIML estimates of the error correction coefficients δ of system (14); eq1, eq2, eq3, eq4 eq5 corresponding to the disequilibria of Table 4; all coefficients have t-ratios with absolute value greater than 2 exept italic entries have t-rations with absolute value between 1.5 and 2.

636

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

A1 Eq./Var ∆yt−1 .

∆πt−1

∆it−1

∆mt−1

∆bt−1

∆ρt−1

∆iDt−1

∆lt−1

∆yt ∆πt ∆it

-0.169

∆mt ∆bt

-0.24 2.03

0.386 5.2

∆ρt

0.262

∆iDt ∆lt

1.124 -0.323 0.021 -0.018

-0.16

1.6

-5.4

0.258 0.05 0.195 -1.1

1.8

Table 9: Constrained FIML estimate of the matrix A1 of system (14); empty entries denote zeroes, all coefficients have t-ratios with absolute value greater than 2 exept italic entries have t-rations with absolute value between 1.5 and 2. The coefficients associated with deterministic variables are not reported.

637

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b

12.8

y

7

12.6 6.75 12.4

1975

6.5

1980

1985

9

1990

1995

m

6.25

1975

9

1980

1985

1990

1995

1980

1985

1990

1995

l

8.9 8.8

8.8

8.6

8.7 8.6

1975

1980

1985

1990

1995

1975

Fig. 1a. Levels of the variables; the vertical line at 1983:3 indicates the lifting of credit ceilings and beginning of the sample used in the estimation.

638

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

25

20

ro

i

15

10

id

5

pi

1975

1980

1985

1990

1995

Fig. 1b. Levels of the variables; the vertical line at 1983:3 indicates the lifting of credit ceilings and beginning of the sample used in the estimation.

639

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Dm

Dy

.1

02 01

0

0 -.1

01

1975

1980

1985

1990

1995

1980

1985

1990

1995

Dl

Db

.1

1975 .05

0 0

.1 .2

1975

1980

1985

1990

1995

1975

1980

1985

1990

1995

Fig. 2a. Differences of the variables; a vertical line at 1983:3 indicates the lifting of credit ceilings and beginning of the sample used in the estimation.

640

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

10

Dpi

5

Di

5

0

0

-5

1975

1980

1985

1990

1995

5

Dro

1975

3

1980

1985

1995

Did

2

2.5

1990

1 0

0

-1

1975

1980

1985

1990

1995

1975

1980

1985

1990

1995

Fig. 2b. Differences of the variables; a vertical line at 1983:3 indicates the lifting of credit ceilings and beginning of the sample used in the estimation.

641

RICERCHE QUANTITATIVE PER LA POLITICA ECONOMICA. 1999

075

D4y

.05

.1

025

0

0

-.05

1975

.2

D4m

.05

1980

1985

1990

1975

1995 .1

0

-.2

.05

D4b

1980

1985

1990

1995

1985

1990

1995

D4l

0 -.05

1975

1980

1985

1990

1995

1975

1980

Fig. 3a. ∆4lt, ∆4yt, ∆4bt, ∆4mt in the full period; the vertical line at 1983:3 in the upper panel indicates the lifting of credit ceilings.

642

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

Dysa

.01

.05

Dmsa

.025 0 0 -.01

-.025

1985

1990

1990

Dbsa

1995

Dlsa

.02

0 -.1

1985

1995

0

-.2

-.02

1985

1990

1995

1985

1990

1995

Fig. 3b. ∆lt, ∆yt, ∆bt, ∆mt least squares corrected for a constant and three seasonal dummies in the estimation period.

643

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-3.6

6.5

y-b

l-y

-3.7 -3.8

6

-3.9 -4

5.5

1975

1980

1985

1990

1995

ro - i

1975

1980

1985

1990

1995

i - id

7.5

5 5 0

1975

2.5

1980

1985

1990

1995

1975

1980

1985

1990

1995

Fig. 4a. Some differentials; the vertical line at 1983:3 indicates the lifting of credit ceilings.

644

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

m-y

-3.5

i - pi

10 5

3.75

0 -4 -5

1975

1980

1985

1990

1995

1975

1980

1985

1990

1995

4

d92

3 2 1

1975

1980

1985

1990

1995

Fig. 4a. Some differentials; the vertical line at 1983:3 indicates the lifting of credit ceilings.

645

RICERCHE QUANTITATIVE PER LA POLITICA ECONOMICA. 1999

Fig. 5. Characteristic roots a) of the unrestricted system (14), b) of system (14) with r=5, with 3 roots at z=1 and one at z=0.992.

646

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

eq1sa : (y - y*)

025

eq2sa : (m - m*)

.1

0 0

025

0

20

40

60

0

eq3sa : (ro - ro*)

2 1

20

40

60

eq4sa : (l - l*) 0

0 -1

0

20

40

60

eq5sa : (i- pi–rr)

0

20

40

60

0 -5 Fig. 6a. Deviations from medium run equilibria; β i′Rt∗=eqisa, where Rt∗ is (Xt−−1′,d92t,t)′ corrected for the constant and seasonal dummies.

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eq1sa : (y - y*)

025

eq2sa : (m - m*)

.1

0 0

025

0

20

40

60

0

eq3sa : (ro - ro*)

2 1

20

40

60

eq4sa : (l - l*) 0

0 -1

0

20

0

20

40

60

40

60

eq5sa : (i - pi - r)

0

20

40

60

0 -5

Fig. 6b. Deviations from medium run equilibria; β i′Rt=eqiDa, where Rt is (Xt−−1′,d92t,t)′ corrected for the constant, seasonal dummies and short run dynamics Zt.

648

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

Dy

.02 .01 0 .01

0 -2.5

1985 2

Di

1990

1995

1985 .1

0

0

-2

-.1

1985

1990

.1 0 -.1 -.2 .5 0 -.5 -1

Dpi

2.5

1985

1995

Db

2

1990

1995

1990

1995

Dm

Dro

0 -2

1985

Did

1990

1995

1985

1990

1985

1990

.05

1995

Dl

0

1985

1990

1995

1995

Fig. 7a. Actual and fitted values of system (14).

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RICERCHE QUANTITATIVE PER LA POLITICA ECONOMICA. 1999

.01

Dpi

Dy

2

0

0

-.01

-2

2 1 0 -1

.1

Di

1995 .025

1995

0 -.025

Db

1995

0 -.1

.5

Dm

Did

1 .5 0 -.5

1995 .02

0

1995

Dro

Dl

1995

0

-.5

-.02

1995

1995

Fig. 7b. Recursive residuals of system (14) with (β β ′,ω ω ,κ κ) fixed at the estimates of Table 4.

650

NEW EVIDENCE ON THE TRANSMISSION MECHANISMS OF MONETARY POLICY …

References

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