Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund

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Appendix

Appendix Table 1.

De Facto Exchange Rate Regime Classification

article image
Source: Course classification from Reinhart and Rogoff.
Appendix Table 2.

Sample

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Inflation targeting adoption date in parentheses. Czech Rep. and Israel are now considered Advanced Economies by the IMF.Source: Roger (2009).
Appendix Table 3.

Data and Sources

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Appendix Table 4.

Robustness–Random Effects Ordered Probit Estimates

(Controlling for Crisis Dummies)

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Random effects probit model with panel data; constant included but not reported; the control variables as well as additive terms forming the interaction variables (not reported) are the same as in Table 1, in addition to Banking crisis, Currency crisis, and Sovereign debt crisis dummies; all the controls (except IT) are included with 1 year lag; the Wald chi2 test is a test for the null hypothesis that all the coefficients except the constant, are jointly equal to zero; ***, **, * indicate the statistical significance at 1, 5, and 10 percent respectively.Source: IMF staff estimates.
Appendix Table 5.

Robustness–Random Effects Ordered Probit Estimates

(Controlling for Central Bank Independence)

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Random effects logit model with panel data; constant included but not reported; control variables as well as additive terms forming the interaction variables (not reported) are the same as in Table 1, in addition to a proxy for central bank independence, all the control variables (except IT) are included with 1 year lag; the Wald chi2 test is a test for the null hypothesis that all the coefficients except the constant, are jointly equal to zero;***, **, * indicate the statistical significance at 1, 5, and 10 percent respectively.Source: IMF staff estimates.
Appendix Table 6.

Robustness–Linear Probability Model

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OLS panel fixed effects estimates; all the control variables as well as additive terms forming the interaction variables (not reported) are the same as in Table 1; control variables (except IT) are included with 1 year lag; robust T-statistics in parentheses; ***, **, * indicate the statistical significance at 1, 5, and 10 percent respectively.Source: IMF staff estimates.
Appendix Table 7.

Probit Model of the Matching Estimates

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T-statistics are reported in parentheses. ***, **, and * indicate statistical significance at the 1, 5, and 10 percent levels, respectively. For the conditional variables considered, “Low” and “High” indicate that IT countries’ observations have been restricted to values lower and higher than the median, respectively.Source: IMF staff estimates.
1

We are indebted to Daria Zakharova, Ken Kang, Shekhar Aiyar, Francisco Vazquez, Andrew Berg, and Selim Elekdag for the insightful comments provided on an earlier draft of this paper. We also thank participants at the IMF EUR Surveillance Meetings and IMF Workshop on Monetary Policy in Low and Middle Income Countries for fruitful discussions on several aspects of the paper.

2

Note that the 5th category mostly captures hyperinflationary periods, and the 6th category includes countries or periods that cannot be classified due to lack of data availability.

3

The 2008/09 global financial crisis showed how severely domestic financial sectors can be affected by an international financial shock.

4

As an alternative measure, we consider the total external debt as a share of exports receipts of goods and services, and that does not change the results.

5

We used a de facto index of financial openness, calculated as the sum of external financial assets and liabilities in percentage of GDP.

6

This specification is used to reduce the co-linearity between the interaction term and zit, but also to ease the interpretation of the interaction.

7

We use the change in government debt because these data are more available (in terms of time dimension and sample coverage) than fiscal surplus/deficit data.

8

Since we are not interested in measuring the magnitude of the effect of IT on ERR, but rather the direction of causality, we do not derive the marginal effects from the probit and logit models.

9

Note that we also test the interaction between IT and inflation volatility and we reach the same conclusion: the interaction term exhibits a negative and significant effect, suggesting that IT countries with higher inflation volatility have a lower probability to adopt a freely floating exchange rate regime.

10

Note that as an alternative to the two ratios related to the banking system balance sheet used in this empirical exercise, we test the interaction terms between IT and the growth rate of bank foreign assets and the growth rate of bank foreign liabilities. The findings support our conclusions that higher exposure of the financial system to external shocks (higher growth rates) is associated with lower flexibility of the ERR in IT countries.

11

While the general rule requires that both interacted variables should be included in the regression, we do not include Time because its values are the same as those of the interaction term.

12

Similar approaches have been used in the empirical literature dealing with interaction effects when the dependent variable is not continuous (see for example the paper from Martin et al., 2012 on trade agreements).

13

Conclusions are broadly in line with this finding when estimating the ATT conditional to the growth rates of bank foreign assets and bank foreign liabilities.

Inflation Targeting and Exchange Rate Regimes in Emerging Markets
Author: Mr. Christian H Ebeke and Mr. Armand Fouejieu