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Purchasing Power Parity and Degree of Openness in Latin America: A Panel ... PPP, trade openness, financial openness, panel cointegration, law of one price.
Purchasing Power Parity and Degree of Openness in Latin America: A Panel Analysis

Omar Esqueda*a Tibebe Assefa b

a, b

Department of Economics and Finance University of Texas - Pan American, 1201 West University Drive, Edinburg, Texas 78539 *Corresponding Author. Tel: +956-665-3383; e-mail address: [email protected]

Abstract We move beyond the all or nothing purchasing power parity (PPP) proposition that has been the norm in previous literature. We test whether inflation, nominal exchange rate volatility, and trade and financial openness influence the stationarity of real exchange rates on a sample of Latin American and Caribbean countries plus Canada during the post Bretton-Woods era. Countries with high inflation and high exchange rate volatility are more likely to support PPP. Classifying the countries by traditional trade openness is not possible to find stationarity consistent with the findings of Alba and Papell (2007) that more open countries are more likely to support PPP. However, when trade openness is parameterized by “relative weight trade intensity” (RWTI), which provides a bi-dimensional approach to international trade weight, we find that higher trade openness leads to higher support of PPP. Westerlund (2007) cointegration error correction model (ECM) tests in panels indicate that financial openness supports PPP only when national debt is not considered. Financially open countries and those that open their borders to trade are more likely to have a mean reversion of exchange rates. These outcomes are relevant to emphasize the benefits of trade and financial openness in economies close to the United States.

JEL Codes: C33, F31, O57 Key Words: PPP, trade openness, financial openness, panel cointegration, law of one price

We sincerely thank Dr. André Varella Mollick for important suggestions. We are also grateful to the participants of the 37th Annual conference of the Academy of Economics and Finance for helpful comments.

Electronic copy available at: http://ssrn.com/abstract=2101739

1. INTRODUCTION The concept of PPP has been extensively analyzed and is the foundation of an extensive number of empirical studies in the international economics area. PPP indicates that the nominal exchange rate of two currencies must be equal to the ratio of their countries’ price indices; as a result, one currency must have the same purchasing power in the other country. In a survey paper, Froot and Rogoff (1995) identify three main stages of empirical studies in PPP. The second stage of PPP has generated plenty of empirical research that test for mean reversion of exchange rates. Following unsuccessful attempts of researchers to provide evidence of real exchange rate mean reversion, Lothian and Taylor (1996) increase the power of the test by using two centuries of data rejecting the null of unit root. The third stage relaxes the assumption that β = 1 in equation (1). (1) qt  st  pt pt * Where Pt = log of CPI domestic Pt* = log of U.S. CPI St = log of nominal exchange rate That is, at least one combination of coefficients generates a stationary series. The cointegration stage has slightly enhanced previous evidence in favor of PPP. Still, the evidence for PPP is not conclusive and many supporters provide several reasons for PPP not to hold. Non-linearity can reduce the already weak power in unit root tests (Taylor and Taylor, 2004; Sollis, 2007). Non-linearity can be caused by price stickiness and transaction costs (Sollis, 2007). Taylor and Taylor (2004) emphasize the contemporary consensus pointing that short-run PPP does not hold, whereas contrary to the predictions of the Balassa-Samuleson proposition, long-run PPP tends to support the mean reverting hypothesis of real exchange rates. As more years of information become available, testing only the post Bretton-Woods era becomes more appealing in order to avoid possible structural breaks. Although Lothian and Taylor (1996) find no structural breaks for a long sample, many researchers prefer to abandon the long-run approach. To overcome the reduction of power originated from removing years of data from the sample, some researchers turned to the use of panel data during the mid 1990s, as are the cases of Oh (1996), Wu (1996), and Lothian (1997) During recent years, after the inception of cointegration and panel studies, there have not been major advances in empirical studies of PPP. However, some important progress in the area includes new econometric techniques in time series (Ng and Perron, 2001, and Lopez, 2008) and in panel studies (Levin, Lin, and Chu, 2002, and Im, Pesaran, and Shin, 2003). The objective of both approaches has been to increase statistical power by increasing time length and sample size. However, empirical issues such as sample size are not the only elements that the literature suggests as factors influencing PPP. For instance, there was the notion that PPP holds more strongly in panels dominated by countries with monetary shocks rather than in panels dominated by countries with real shocks. However, Akram (2006) contradicts this idea by finding relatively low half-lives of about one and a half years for Norway, a country with predominantly real shocks. Papell and Theodoridis (2001) mention that the numerator effect is significant in panel studies. They find that PPP holds more strongly in panels where the domestic and the reference (foreign) currencies are less volatile relative to each other. Additionally, they find that openness and distance are important factors to determine PPP unit root tests. Extending these results, Alba and Papell (2007) test whether country characteristics determine if PPP holds. Their results support the notion that country characteristics contribute to the stationarity of exchange rates. Alba and Papell’s (2007) contribution is the motivation of this empirical paper. They put forward that country characteristics are highly influential in the stationarity of the real exchange rates. They find that openness, distance, inflation, growth, and exchange rate volatility can partially explain the rejection of the unit root hypothesis in a panel study with data from the post Bretton-Woods era. Alba and Papell

Electronic copy available at: http://ssrn.com/abstract=2101739

(2007) only consider the traditional definition of trade openness. Our contribution lies on the inclusion of financial openness benchmarks and the use of an alternative measure of trade openness. We analyze the effect of openness on PPP for the sample of 15 Latin American and Caribbean countries plus Canada during the post Bretton-Woods era. We use quarterly data from 1974 to 2008. Although we consider distance an important factor, for our sample we can rule out this variable since all countries are relatively close to the United States. We use two definitions of trade openness. First, the sum of imports and exports deflated by gross domestic product (GDP) is our measure of traditional trade openness (TTO). Our second measure of trade openness is suggested by Squalli and Wilson (2006). RWTI equals to the sum of imports and exports deflated by sample GDP. RWTI relates country’s import and exports to world income as well as to domestic income, therefore providing a bi-dimensional approach to international trade weight. Furthermore, we use two financial openness benchmarks from Lane and Milesi-Ferretti (2007). The aim of this paper is to determine whether the degree of trade and financial openness, nominal exchange rate volatility, and inflation can explain real exchange rate mean reversion. Moreover, for the non-stationary series, we test for a cointegrating relationship using Westerlund (2007) ECM in a panel setting. Our findings indicate that countries with high inflation and high exchange rate volatility are more likely to support PPP. Country groups formed based on trade openness proxied by RWTI support the idea that more open countries are more likely to have mean reverting exchange rates. This result is intuitive because PPP is based on the law of one price, which relies on arbitrage to keep prices at equilibrium. However, Shleifer and Vishny (1997) suggest that arbitrage forces are constrained by transaction costs and few opportunities to trade. Thus, restrictions to trade can maintain prices at disequilibrium levels for an indefinite period of time (i.e. prices become non-stationary). For the groups with unit roots, we use ECM to test whether some form of cointegration exists between price differentials and exchange rates. Results based on financial openness measures indicate that cointegration exists when price differential is the dependent variable. However, for the international financial integration (IFI) classification we find weak support for a cointegrating relationship. This confirms our argument that the latter criterion has drawbacks in estimating financial openness. High inflation and high exchange rate volatility are supportive of PPP. Financially open countries and those that open their borders to trade are more likely to have a mean reversion of exchange rates during the post Bretton-Woods era. We further find that the best methods to estimate financial and trade openness are by applying country’s equity plus foreign direct investment (GEQ) and RWTI respectively, where RWTI is able to depict the most robust outcomes compared to other measures of openness. The paper is organized in the following order; section 2 presents the data and section 3 describes the econometric technique applied. Section 4 includes a discussion of the results, and section 5 concludes. 2. DATA Data is primarily from the International Financial Statistics (IFS) database, a part of the International Monetary Fund (IMF). GDP, imports and exports, plus the data required to calculate exchange rate volatility and inflation, namely consumer price index (CPI) and nominal exchange rates, were collected quarterly from 1974 to 2008 from the IFS. We used Lane and Milesi-Ferretti (2007) data to calculate international financial integration measures. Based on population, we select a sample of 15 Latin American and Caribbean countries plus Canada. We believe only countries with a population of more than five million are relevant for our study1. Small nations generally do not have complete data available for the sample period for the variables required in this study. These countries may not have a significant economic and trade relation with the U.S. Moreover, excessively small countries’ exchange rate behavior might differ substantially from that of the average Latin American or Caribbean country. The Latin American countries that satisfy this condition and are included in our study are Argentina, Bolivia, Brazil, Chile, Colombia, Dominican Republic, Ecuador, El Salvador, Haiti, Honduras, Mexico, Nicaragua, Paraguay, Peru, and Venezuela.

Latin American and Caribbean countries plus Canada form our sample because distance with the United States is an important factor according to Alba and Papell (2007). By studying only countries close to the U.S. we can rule out the distance variable, since all countries are relatively close to the United States and we are able to focus on their level of trade and financial openness. Furthermore, closeness to the U.S. increases the likelihood of bilateral trade opportunities, and consequently we would expect PPP to be more likely to hold in countries relatively close to the U.S. Finally, countries close to the U.S. are more likely to have a common economic linkage with the U.S. Dollar, which is the base currency in this study. 3. ECONOMETRIC TECHNIQUE We use the standard Augmented Dickey-Fuller2 (ADF) to perform univariate tests and two tests particularly designed for panel data - Levin, Lin, and Chu (2002) (LLC) and Im, Pesaran and Shin (2003) (IPS). IPS and LLC tests differ in the null hypothesis, which is more restrictive for LLC, therefore in the latter test the probability of committing a type two error is higher. First, we run univariate ADF unit root tests for each country in the sample. Then we run two panel unit root tests as in Alba and Papell (2007), using LLC and IPS tests for the sample of 16 countries and eventually for each group of countries according to Table 3 classification. // Table 1 and Table 2 around here// Table 3 presents classification by country characteristics, according to the indicators of trade openness, financial openness, exchange rate volatility, and inflation volatility. For every measure, countries are divided into four balanced groups based on their quartile membership. //Table 3 around here// Univariate test model: K

qt   qt    ciqt  i  t

(2)

i 1

Where: ∆qt is the first difference of real exchange rate and i varies from 1 to k, where k is the number of lagged first differences. k is determined using Campbell-Perron (1991) method of data dependent procedure, whose method is usually superior to k chosen by the information criterion according to Ng and Perron (1995). The method starts with an upper bound, kmax = 13. Panel test model: K

qtj  j qjt    c jiqjt  i  jt

(3)

i 1

Where: μj represents heterogeneous intercept and the subscript j is the country index, i varies from 1 to k. The lag length k and the coefficient cij are heterogeneous across countries. Eq. (3) is estimated using feasible GLS (SUR), with the values of k taken from the results of univariate ADF tests. The α coefficient is equal across countries. The restriction on α follows the panel unit root tests developed by Levin et al. (2002). The t- statistics on α is the test statistics. If α is negative and significantly different from zero, the null hypothesis that all the real exchange rates in the panel have unit roots is rejected in favor of the alternative hypothesis that all the real exchange rates in the panel are level stationary. We run the panel unit root tests on each of the four country groups formed according to the level of trade and financial openness, exchange rate volatility, and inflation volatility. We structure the groups by quartile membership. Additionally, in order to increase statistical power, we divide the sample into two groups; lower fifty percentile and upper fifty percentile. To measure trade and financial openness we compute IFI and GEQ measures as defined by Lane and Milesi-Ferretti (2007) IFI it =

 FAit  FLit  GDPit

Where: FAit (FLit) denotes the stock of external assets (liabilities) for country i at time t.

(4)

GEQYit =

 PEQAit  FDIAit  PEQLit  FDILit  GDPit

(5)

Where: PEQA (PEQL) denotes the stock of portfolio equity assets (liabilities) and FDIA (FDIL) denotes the stock of foreign direct investment assets (liabilities). Trade openness measures used in this study are the TTO and an alternative measure of trade openness, RWTI, suggested by Squalli and Wilson (2006).  X  M it (6) TTOit =

GDPit

Where X (M) is total exports (imports) of country i at time t. Squalli and Willson (2006) explain the weakness of traditional trade openness measures: This is commonly used in the literature, however, all these measures suffer from the same problem; they capture only one dimension of trade openness, the dimension linking trade to domestic income. It overlooks this second important dimension of trade openness which captures the income generating benefits associated with trading relatively heavily with the rest of the world. Squalli and Wilson (2006) suggest that a more accurate estimate of the degree of trade openness can be obtained by using the RWTI scale. (7)  X  M it RWTIit = 16  ( X  M )it i 1

Where X and M are as defined above and the denominator is the sum of all imports and exports of all counties in our sample at time t Using data from 1974 – 2008, we calculate the average exchange rate volatility vis-à-vis the U.S. dollar employing the approach of Papell and Theodoridis (2001). Furthermore, inflation is measured using the average annual change in CPI during the same period of time. Finally, for countries that fail to reject the panel unit root test, cointegration tests are performed using Westerlund (2007) ECM for panel data. 4. RESULTS The results for the univariate unit root tests of the 16 countries in our sample are presented in Table 1. From the total sample, 14 countries appear to be I(1). Only Chile and Ecuador have mixed results but leaning towards being I(0). Lack of power might be one of the reasons for the unit root discovered in the majority of the sample. Therefore, we proceed to run a panel unit root test in order to increase statistical power. Table 2 shows panel unit root test results for IPS and LLC tests for a panel of 16 countries. LLC results indicate that the null hypothesis that the series have a unit root is rejected, whereas IPS does not reject the null hypothesis that at least one of the 16 countries is stationary. However, LLC tests seem to be more commonly used in the literature, thus we can rely more on these results and conclude that the panel series of 16 countries appear to be I(0). However, it is not appropriate to characterize all series as stationary; therefore, we proceed to create subgroups of the sample as shown in table 3. Country subgroups are based on four different openness criteria, exchange rate volatility, and inflation. // Table 4 around here// In table 4, TTO classification shows that the most open and the least open countries reject the unit root null hypothesis at different significance levels. Similarly, The GEQ measure shows that the most open and the less open countries reject the unit root null hypothesis. The IFI indicator shows that the less open and least open groups reject the unit root null hypothesis. TTO and IFI have results that contradict previous empirical evidence from Alba and Papell (2007), while GEQ provides mixed results.

Conversely, RWTI is the only openness measure able to show intuitive results (i.e. more open countries tend to more easily adjust to parity). An explanation for the counterintuitive results is that the groups based on TTO, IFI, and GEQ, seem not to reflect the conventional perception about the most open countries. For instance, classifying Nicaragua and Honduras into the most open group and Mexico (a NAFTA member) in the least open group, appears to differ from the ordinary depiction of country openness. In the case of Nicaragua and Honduras they have high debt and low GDP which makes their IFI index relatively higher than other Latin American countries in our study. IFI measure has a weakness estimating the degree of openness of countries with high debt and low GDP. For instance, Lane and Milesi-Ferretti (2007, p. 240) exclude Nicaragua due to its negatively skewed distribution of financial openness measures. Additional indication about the poorness of financial openness measures for some low income countries is mentioned by Bonfiglioli (2008, p. 343) and Kose et al. (2006, p. 14). However, GEQ excludes debt, therefore the aforementioned bias must be less severe assessing financial openness using GEQ than using IFI. The exchange rate volatility results are similar to those of Alba and Papell (2007). We are able to reject the unit root hypothesis for the most and more volatile countries. High exchange rate volatility is consistent with exchange rate mean reversion; hence PPP is more likely to hold in countries with high exchange rate volatility, as indicated by the results in the highest and higher quartiles of exchange rate volatility classification. Papell and Theodoridis (2001) find that exchange rate volatility is the most important determinant of PPP, although they conclude that it is inversely related to PPP. Since countries have exactly the same group membership when they are grouped by inflation or by exchange rate volatility, the results are identical. Our inflation rate findings are in agreement with the view that high inflation countries support more strongly PPP, contrary to Alba and Papell (2007) who find that only the lowest inflation countries tend to support PPP. These results might be attributable to differences in sample constituents. While Alba and Papell (2007) have a combination of developed and developing economies, we include only countries close to the U.S. from which 15 are developing countries and only Canada is a developed economy. Consequently, opposite results could be due to high inflation in Latin American and Caribbean countries. However, our results appear realistic, since it is reasonable to believe that high inflation economies support PPP since inflation is one of the main factors affecting exchange rates (Frenkel 1978). Finally, we classify the sample into NAFTA and non-NAFTA countries. The results indicate that both groups support PPP. We would expect NAFTA countries to be highly significant, due to the high level of trade with the U.S. However, results indicate that the NAFTA group rejects the null hypothesis of unit root only at the 10 percent significance level. While the non-NAFTA countries find stationarity at the one percent level. We believe the lack of stronger support of PPP from NAFTA members can be attributable to low statistical power since only two countries (Mexico and Canada) form the group of NAFTA members. In order to increase power and as a robustness check of the results revealed in Table 4, we categorize the sample into two balanced groups. Eight countries above the median benchmark are in the first group and the remaining eight countries in the bottom 50th percentile form the second group. Table 5 results follow similar patterns as those obtained previously in table 4 where the quartile classification of table 3 was utilized. //Table 5 around here// Evidence of cointegrating relationships is observed when exchange rate is the dependent variable and price differential is the independent variable for inflation/nominal exchange rate volatility and RWTI groups. TTO has a similar relationship but only weak support for cointegration. On the other hand, the results based on financial openness measure GEQ indicate that some cointegration exists when price differential is the dependent variable. However, for the IFI classification we find weak support for a cointegrating relationship. This confirms our argument that the latter openness criterion has drawbacks in estimating financial openness. //Table 6 around here//

In sum, our findings indicate that high inflation and high exchange rate volatility characteristics are supportive of PPP. Financially open countries and those that open their borders to trade are more likely to have a mean reversion of real exchange rates during the post Bretton-Woods era. We further find that the best methods to estimate financial and trade openness are through GEQ and RWTI respectively, where RWTI is able to depict the most robust outcomes compared to other measures of openness. 5. CONCLUSION We extend previous PPP research by relating countries’ financial openness estimations to real exchange rate mean reversion. Additionally, we contribute to the literature by including RWTI, a novel approach to assess trade openness to the PPP analysis. Our findings indicate that countries with high inflation and high exchange rate volatility are more likely to support PPP. Testing for unit roots, we find that classifying the countries by traditional trade openness is not possible to determine patterns that support the findings of Alba and Papell (2007) that more open countries are more likely to support PPP. However, when trade openness is parameterized by RWTI we find that higher trade openness leads to higher support of PPP. Finally, Westerlund (2007) ECM tests in panels indicate that financial openness supports PPP only when national debt is excluded from the parameterization of the countries’ level of financial openness. Financially open countries and those that open their borders to trade are more likely to have their exchange rates returning to an equilibrium point relative to the U.S. dollar. We conclude that the best methods to estimate financial and trade openness are through GEQ and RWTI respectively. Our findings are important to emphasize the benefits of trade and financial openness in economies close to the United States.

Table 1. Univariate Unit Root Tests on the Real Exchange Rate of 16 Countries series

Trend?

ADF(k)

DF-GLS(k)

KPSS(4)

Ng-Perron MZα(K)

Ng-Perron Mzt(K)

Quarterly: 1974Q1 – 2008Q4 Argentina Yes Δ(Argentina) No

-0.007 (3) -.22 (2)***

-0.006(3) -0.18 (2)**

-40.33*** 0.35***

.865(0) -12.51 (2)**

-0.91(0) -2.50 (2)**

Bolivia Δ(Bolivia)

Yes No

-0.011(9)* -0.16(8)**

-0.011(9)* -0.14(8)**

-21.77*** -0.18***

-11.48(2) -29.22(3)***

-2.33(2) 3.82(3)***

Brazil Δ(Brazil)

Yes No

-0.004(3) -0.12(2)**

-0.005 (3) -0.11(2)**

59.97*** -0.45***

-4.55(1) -13.47(0)**

-1.33(1) 2.59(0)***

Canada Δ(Canada)

Yes No

- 0.05(3)*** - 0.60(0)***

-0.03(3)** -(0.59)***

0.15*** -0.002

-4.79(1) -63.63(0)**

-1.42(1) -2.75(0)**

Chile Δ(Chile)

Yes No

-0.01(12)*** -0.30(6)***

-0.003(7)* -0.005(11)

-0.39 -0.10***

-2.33(1) -0.63(2)

-0.94(1)* -0.49(2)

Colombia Δ(Colombia)

Yes No

- 0.004(3) - 0.24(2)***

-0.02(12)** -0.24(2)***

-0.74*** 0.06***

-1.01(1) -39.69(0)***

-0.40(1) -4.20(0)***

Dominican Republic Δ(Dominican Republic)

Yes No

-0.04(1)* -0.72(0)***

-0.03(1) -0.63(0)

-3.31*** -0.05***

-5.01(1) -57.92(0)***

-1.57(1) -5.38(0)***

Ecuador Δ(Ecuador)

Yes No

-0.01(3) - 0.10(2)

-0.01(3) -0.08(2)

-5.18*** 0.12***

-37.52(3)*** -1.36(2)

-4.26(3)*** -0.38(2)

El Salvador Δ(El Salvador)

Yes No

- 0.002(0) -0.85(0)***

-0.006(0) -0.85(0)***

-1.29*** 0.02***

-0.85(0) -69.01(0)***

-0.46(0) -5.87(0)***

Haiti Δ(Haiti)

Yes No

- 0.03(11)*** - 0.25(10)

0.02(11)** -0.25(10)

-1.39*** 0.035***

-1.08(1) - 63.88(0)***

-0.63(1) -5.65(0)***

Honduras Δ(Honduras)

Yes No

-0.02(2)** -0.37(1)***

-0.01(2) -0.36(1)***

-2.46*** -0.03***

-3.22(2) -29.52(1)***

-1.26(2) 3.84(1)***

Mexico Δ(Mexico)

Yes No

-0.004(1) -0.23(1)***

-0.01(12)* -0.19(1)***

-10.65*** -0.10***

-4.21(2) -18.33(1)***

-1.29(2) -2.93(1)***

Nicaragua Δ(Nicaragua)

Yes No

-0.01(13)* -0.18(12)**

-0.01(13)* -0.15(12)**

-51.34*** 0.35***

-5.94(2) -18.40(1)***

-1.64(2) -3.03(1)***

Paraguay Δ(Paraguay)

Yes No

-0.02(8) -0.42(2)***

-0.01(8) -0.19(7)*

1.25*** 0.05***

-4.10(3) -18.98(2)***

-1.35(3) -2.92(2)***

Peru Δ(Peru)

Yes No

-0.01(9) -0.23(8)***

-0.01(9) -0.19(8)**

-37.55*** 0.27***

-3.09(1) -32.03(0)***

-1.10(1) -4.00(0)***

Venezuela Δ(Venezuela)

Yes No

-0.03(9)** -0.42(0)***

-0.01(9) -0.11(8)

-13.15*** 0.09***

-2.08(1) -34.92(0)***

-1.01(1) -4.17(0)***

Notes: Data has quarterly frequency from 1974Q1 to 2008Q4. Fifteen Latin American and Caribbean countries plus Canada are in the list . The symbol ∆ refers to the first difference of the original series. We include the deterministic trend only when testing in levels as suggested from graph inspection. ADF(k) refers to the Augmented Dickey-Fuller t-tests for unit roots, in which the null is that the series contains a unit root. The lag length (k) for ADF tests is chosen by the Campbell-Perron data dependent procedure, whose method is usually superior to k chosen by the information criterion, according to Ng and Perron (1995). The method starts with an upper bound, kmax = 13, on k. If the last included lag is significant, choose k = kmax. If not, reduce k by one until the last lag becomes significant (we use the 5% value of the asymptotic normal distribution to assess significance of the last lag). If no lags are significant, then set k = 0. Next to the reported calculated t-value, in parenthesis is the selected lag length. DF-GLS (k) refers to the modified ADF test proposed by Elliott et al. (1996), with similar lag length selection criteria in ADF(k). The KPSS test follows Kwiatkowski et al. (1992), in which the null is that the series is stationary and k=4 is the used lag truncation parameter. We reported two of the M-tests developed by Ng and Perron (2001) with MAIC used for lag-length selection. The MZα and Mzt tests have less severe size distortions when the errors have a negative moving average (MA) root. The symbols * [**] (***) indicate rejection of the null at the 10%, 5%, and 1% levels, respectively.

Table 2: Panel Unit Root Tests of Real Exchange Rates Organized by Country Characteristic K

qtj  j qjt    c jiqjt  i  jt i 1

Country group Levin, Lin and Chu (LLC) test ∆q

Im, Pesaran and Shin (IPS) test ∆q

Number of countries 16

16

α

-0.0022 -0.3000

t- statistics

-3.890 -17.792

p-values

0.0028 0.0000

-1.0637 -4.9293

Notes: Data has quarterly frequency from 1974Q1 to 2008Q4. Fifteen Latin American and Caribbean countries plus Canada are included in the panel. The symbol ∆ refers to the first difference of the original series. We include the deterministic trend only when testing in levels as suggested from graph inspection.

Table 3: Country Classification by Openness Measures: Traditional Trade Openness (TTO), International Financial Integration (IFI), Portfolio Equity and FDI stocks (GEQ), Relative World Trade Intensity (RWTI), Nominal Exchange Rate Volatility (ERV), and Inflation. Panel A: Openness TTO IFI GEQ RWTI Most Open Canada Nicaragua Canada Canada Honduras Canada Chile Brazil Nicaragua Bolivia Bolivia Mexico Chile Honduras DominicanArgentina Republic More Open

Less open

Least Open

Paraguay Ecuador El Salvador Bolivia

Chile Venezuela Ecuador Peru

Ecuador Brazil Venezuela Honduras

Chile Colombia Peru Haiti

Venezuela Haiti Mexico Colombia

Argentina DominicanRepublic Brazil El Salvador

Argentina Mexico Peru Nicaragua

Ecuador Venezuela DominicanRepublic El Salvador

DominicanRepublic Peru Argentina Brazil

Mexico Paraguay Colombia Haiti

Colombia El Salvador Paraguay Haiti

Honduras Bolivia Nicaragua Paraguay

Panel B: Nominal Exchange rate Volatility (EV) Highest Volatility Higher Volatility Lower Volatility Lowest Volatility

Bolivia, Nicaragua, Peru and Brazil Argentina, Chile, Ecuador and Mexico Venezuela, Dominican Republic, Colombia and Paraguay Haiti, Honduras, El Salvador, and Canada Panel C: Inflation

Highest Inflation Higher Inflation Lower Inflation Lowest Inflation

Nicaragua, Brazil, Bolivia and Peru Argentina, Chile, Mexico and Ecuador Venezuela, Colombia, Dominican Republic and Paraguay Haiti, Honduras, El Salvador, and Canada

We measured Traditional Trade Openness(TTI) using the export plus import divided by gross domestic product data used (1991 – 2006). International Financial Integration is measured IFI = (Fa it + Flit)/GDPit where FA(FL) denotes the stock of external assets and liabilities data used (1970 – 2004). GEQit = (PEQAit +FDIAit+PEQLit+FDILit)/GDPit where PEQA(PEQL) denotes the stock of portfolio assets (liabilities) and FDIA(FDIL) denotes the stock of their direct investment assets (liabilities) (1970 – 2004). For IFI and GEQit we used Lane and Milesi- Ferretti (2007) data. Relative world trade intensity is measured using Squalli and Wilson (2006) method. We calculated the average exchange rate volatility vis-à-vis the US dollar using the formula in Papell and Theodoridis (2001) data used (1974 – 2008). We measure inflation using the average annual change in CPI data used (1974 – 2008).

Table 4: Panel Unit Root Tests of Real Exchange Rates Ordered by Country Characteristics. K

qtj  j qjt    c jiqjt  i  jt i 1

Country group Openness Measures TTO Most Open More Open Less open Least Open IFI Most Open More Open Less open Least Open GEQ Most Open More Open Less open Least Open RWTI Most Open More Open Less open Least Open Inflation/Nominal exchange rate volatility (ERV) Highest Higher Lower Lowest NAFTA Member Non-member

Number of countries

α

t- statistics

p-values

4 4 4 4

-0.00453 -0.00125 -0.00127 -0.00332

-2.620 -0.896 -1.484 -3.147

0.0712 0.5192 0.1259 0.0018

4 4 4 4

-0.00293 -0.00103 -0.00364 -0.00178

-1.908 -0.816 -3.117 -2.027

0.1580 0.6590 0.0015 0.0336

4 4 4 4

-0.00720 -0.00037 -0.00354 -0.00167

-3.576 -0.369 -3.007 -1.690

0.0095 0.6977 0.0013 0.0698

4 4 4 4

-0.00347 -0.00251 0.00100 -0.00225

-3.403 -2.541 0.691 -1.769

0.0005 0.0186 0.9670 0.1609

4 4 4 4

-0.00308 -0.00329 -0.00135 -0.00018

-3.012 -2.713 -1.481 -0.091

0.0056 0.0232 0.1091 0.8507

2 14

-0.00326 -0.00209

-1.845 -3.481

0.0502 0.0088

We measured Traditional Trade Openness(TTO) using the export plus import divided by gross domestic product data used (1991 – 2006). International Financial Integration is measured IFI = (Fa it + Flit)/GDPit where FA(FL) denotes the stock of external assets and liabilities data used (1970 – 2004). GEQit = (PEQAit +FDIAit+PEQLit+FDILit)/GDPit where PEQA(PEQL) denotes the stock of portfolio assets (liabilities) and FDIA(FDIL) denotes the stock of their direct investment assets (liabilities) (1970 – 2004). For IFI and GEQit we used Lane and Milesi- Ferretti (2007) data. Relative world trade intensity is measured using Squalli and Wilson (2006) method. We calculated the average exchange rate volatility vis-à-vis the US dollar using the formula in Papell and Theodoridis (2001) data used (1974 – 2008). We measure inflation using the average annual change in CPI data used (1974 – 2008). Lag length selection criteria is Schwarz Information criteria with maximum lag length of 12.

Table 5: Panel Unit Root Tests of Real Exchange Rates Arranged by Country Characteristic

K

qtj  j qjt    c jiqjt  i  jt i 1

Country group Openness Measures TTO Most Open Least Open IFI Most Open Least Open GEQ Most Open Least Open RWTI Most Open Least Open Inflation/ERV Highest Inflation/ERV Lowest Inflation/ERV

Number of countries

α

t- statistics

p-values

8 8

-0.00256 -0.00208

-2.350 -3.127

.1847 .0033

8 8

-0.00178 -0.00245

-1.831 -3.489

.3822 .0006

8 8

-0.00175 -0.00252

-1.931 -3.451

.2749 .0009

8 8

-0.00297 -0.00083

-4.187 -0.865

.0001 .6793

8 8

-0.00317 -0.00114

-4.048 -1.374

.0006 .2524

We measured Traditional Trade Openness(TTO) using the export plus import divided by gross domestic product data used (1991 – 2006). International Financial Integration is measured IFI = (Fa it + Flit)/GDPit where FA(FL) denotes the stock of external assets and liabilities data used (1970 – 2004). GEQit = (PEQAit +FDIAit+PEQLit+FDILit)/GDPit where PEQA(PEQL) denotes the stock of portfolio assets (liabilities) and FDIA(FDIL) denotes the stock of their direct investment assets (liabilities) (1970 – 2004). For IFI and GEQit we used Lane and Milesi- Ferretti (2007) data. Relative world trade intensity is measured using Squalli and Wilson (2006) method. We calculated the average exchange rate volatility vis-à-vis the US dollar using the formula in Papell and Theodoridis (2001) data used (1974 – 2008). We measure inflation using the average annual change in CPI data used (1974 – 2008). Lag length selection criteria is Schwarz Information criteria with maximum lag length of 12.

Table 6: Cointegration Tests for CPI, CPI*, and S for those Country Groups that resulted I(1) in Table 5. p– values are Reported Using Westerlund (2007) ECM panel Cointegration Tests. The First Row Represents the p-values Resulting from ln(s)  ln(p)  ln(p*)] and the Second Row is for

ln(p)  ln(p*)  ln(s)] Number of countries

Gt

8

0.198 0.454

Most Open

8

GEQ Most Open

Country group Openness Measures TTO Most Open

Ga

Pt

Pa

0.105 0.199

0.039 1.000

0.000 1.000

0.333 0.039

0.264 0.028

0.396 0.999

0.000 0.684

8

0.428 0.024

0.581 0.048

0.188 0.003

0.020 0.000

8

0.011 0.158

0.013 0.147

0.022 1.000

0.000 0.998

8

0.027 0.013

0.006 0.000

0.098 0.999

0.000 0.737

IFI

RWTI Least Open Inflation/ERV Lowest Inflation /ERV

We measured Traditional Trade Openness(TTO) using the export plus import divided by gross domestic product data used (1991 – 2006). International Financial Integration is measured IFI = (Fa it + Flit)/GDPit where FA(FL) denotes the stock of external assets and liabilities data used (1970 – 2004). GEQit = (PEQAit +FDIAit+PEQLit+FDILit)/GDPit where PEQA(PEQL) denotes the stock of portfolio assets (liabilities) and FDIA(FDIL) denotes the stock of their direct investment assets (liabilities) (1970 – 2004). For IFI and GEQit we used Lane and Milesi- Ferretti (2007) data. Relative world trade intensity is measured using Squalli and Wilson (2006) method. We calculated the average exchange rate volatility vis-à-vis the US dollar using the formula in Papell and Theodoridis (2001) data used (1974 – 2008). We measure inflation using the average annual change in CPI data used (1974 – 2008). Lag length selection criteria is Schwarz Information criteria with maximum lag length of 12.

FOOTNOTES 1

Although Cuba fits the population requirement it is excluded due to its evident limitation of data We also use DF-GLS, KPSS and Ng and Perron (2001) as a robustness check in the univariate unit root test. 2

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