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1. 20Arellano and Bond’s (1991) test of serial correlation suggests that the error terms are white noise. RBE Rio de Janeiro v. 63 n. 4 / p. 341–360 Out-Dez
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2. Alonso-Borrego, C. & Arellano, M. (1999). Symmetrically normalized instrumental variable estimation using panel data. Journal of Business and Economic Statistics, 17:36–49.

3. Arellano, M. & Bond, S. R. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58:277–97.

4. Arellano, M. & Bover, S. R. (1995). Another look at the instrumental-variable estimation of errocomponents models. Journal of Econometrics, 68:29–52.

5. Arida, P., Bacha, E., & Lara-Resende, A. (2005). Credit, interest and jurisdictional uncertainty: Conjectures on the case of Brazil. In Giavazzi, F., Goldfajn, I., & Herrera, S., editors, Inflation Targeting, Debt and the Brazilian Experience, 1999 to 2003, pages 265–93. MIT Press.
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6. Armas, A., Ize, A., & Levy-Yeyati, E. (2006). Financial Dollarization: The Policy Agenda. Palgrave Macmillan, New York.
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7. Arteta, C. O. (2003). Are financially dollarized countries more prone to costly crises? International Finance Discussion Papers 763, Board of Governors of the Federal Reserve System.
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8. As shown in Ize and Levy-Yeyati (2003), if the uncovered interest-parity condition holds, the dollar share of the optimal investment portfolio, which replicates the minimum variance portfolio, is equal to: MV P = [V ar(π) + Cov(π,q)]/[V ar(π) + V ar(q) + 2Cov(π,q)] where π is the inflation rate in local currency and q is the real exchange rate. To estimate a country ’s MVP for a given year, we used monthly data on inflation (CPI) and exchange rate changes for that country in that year. Source: IMF’s International Financial Statistics.
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9. Bacha, E. L. (2003). Brazil’s Plano Real: A view from the inside. In Dutt, A. K. & Ros, J., editors, Development Economics and Structuralist Macroeconomics: Essays in Honor of Lance Taylor. Edward Elgar, Cheltenham.

10. Bacha, E. L., Holland, M., & Gonçalves, F. (2007). Is Brazil different? Risk, dollarization and interest rate in emerging market. IMF Working Paper 07/294.

11. Barajas, A. & Morales, R. (2003). Dollarization of liabilities: Beyond the usual suspects. IMF Working Paper 03/11.

12. Barcelos-Neto, P. & Portugal, M. (2006). The natural rate of interest in Brazil between 1999 and 2005. In Annals of the XXXIV National Economic Meetings, Salvador, BA. ANPEC. Available at www.anpec.org. br/encontro_2006.htm#trabalhos.

13. Beers, D. & Cavanaugh, M. (2006). Sovereign Credit Ratings: A Primer. Standard & Poor’s Ratings Direct. McGraw Hill, New York.
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14. Bevilaqua, A., Mesquista, M., & Minella, A. (2007). Brazil: Taming inflationary expectations. Central Bank of Brazil, Discussion Paper 129. Brasília.
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15. Blundell, R. & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87:115–43.

16. Bolino, T., Bennett, A., & Borensztein, E. (1999). Monetary policy in dollarized economies. IMF Occasional Paper 171. Washington, DC.

17. Capital Account Liberalization Index (CAPLIB) – Index described in Edwards’ (2005), gently provided to us by the author. It is a scale from zero to 100 in which higher values indicate increasing degrees of capital account liberalization.
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18. Central Bank of Brazil (2008). Central Bank survey.
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19. De La Torre, A. & Schmukler, S. (2004). Coping with risks through mismatches: Domestic and international financial contracts for emerging economies. International Finance, 7(3):349–390.

20. De Nicoló, G., Honohan, P., & Ize, A. (2003). Dollarization of the banking system: Good or bad? IMF Working Paper WP 03/146.

21. Dollarization Ratio (DOLLAR) – Ratio of Dollar (or Euro) deposits over total bank deposits. Source is Levy-Yeyati (2006). Original sources: IMF Staff Reports, Central Bank bulletins, Bolino et al. (1999), De Nicoló et al. (2003), and Arteta (2003). Delta-inflation (∆π ) – Difference between this year’s and last year’s inflation, both calculated as the average of the annualized monthly consumer price index variation (IFS, line CPI), in percentage terms.
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22. Fraga, A. (2005). Comments. In Giavazzi, F., Herrera, S., & Goldfajn, I., editors, Inflation Targeting, Debt and the Brazilian Experience, 1999 to 2003. MIT Press.
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23. Galindo, A. & Leiderman, L. (2005). Living with dollarization and the route to dedollarization. IADB Research Department, Working Paper 526.

24. Giavazzi, F., Herrera, S., & Goldfajn, I. (2005). Inflation Targeting, Debt and the Brazilian Experience, 1999 to 2003. MIT Press.
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25. Gonçalves, F. M., Holland, M., & Spacov, A. D. (2007). Can jurisdictional uncertainty and capital controls explain the high level of real interest rates in Brazil? Evidence from panel data. Revista Brasileira de Economia, 61(1):49–75.

26. IADB (2005). Financial dollarization. Ch. 4 of Unlocking Credit: The Quest for Deep and Stable Bank Lending, Elsevier.
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27. IMF (2007). International financial statistics online. International Monetary Fund.
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28. In the case of dynamic panel data models, the ordinary least square estimator is known to be biased and inconsistent. In dynamic panel data models where the autoregressive parameter is moderately large and the number of time series observations is moderately small, exactly as in our dataset, Blundell and Bond (1998) find the widely used linear Generalized Method of Moments (GMM) estimator obtained from the first differences of the sample variables to have large finite sample biases and poor precision in simulation studies. Lagged levels of the series provide weak instruments for first differences in this case.17 When estimating a dynamic model for the equation of the real interest rate we were therefore interested in transformations that allowed the use of lagged endogenous variables as instruments in the transformed equation.
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29. Investment Grade (IGRADE) – Equal to 1 for a sovereign investment-grade rating and zero for a speculative-grade rating. This variable was maintained constant for each country on the basis of its status in 2004. Source: Standard & Poor’s. Jurisdictional Uncertainty (JU) – Equal to 100 minus the World Bank rule-of-law index ranging from 0 to 100. As we had values for this variable only for even years, odd-years values were interpolated. Source: World Bank, Governance Indicators.
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30. Ize, A. & Levy-Yeyati, E. (2003). Financial dollarization. Journal of International Economics, 59:323–347.

31. Levy-Yeyati, E. (2006). Financial dollarization: Evaluating the consequences. Economic Policy, 21(45):61– 118.

32. Miranda, P. & Muinhos, M. (2003). A taxa de juros de equilíbrio: Uma abordagem múltipla. Banco Central do Brasil, Discussion Paper 66.

33. Muinhos, M. & Nakane, M. (2006). Comparing equilibrium real interest rates: Different approaches to measure Brazilian rates. Banco Central do Brasil, Discussion Paper 101.

34. Per Capita GDP (Y ) – In constant 2000 US$. Source: World Bank, World Development Indicators (on line).
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35. RBE Rio de Janeiro v. 63 n. 4 / p. 341–360 Out-Dez 356 Edmar L. Bacha, Márcio Holland and Fernando M. Gonçalves Edwards, S. (2005). Capital controls, sudden stops and current account reversals. NBER Working Paper 11170. Cambridge, MA.

36. Reinhart, C. & Rogoff, K. (2004a). The modern history of exchange rate arrangements: A reinterpretarion. Quarterly Journal of Economics, 119(1):1–48.

37. Reinhart, C. & Rogoff, K. (2004b). Serial default and the ‘paradox’ of rich to poor capital flows. American Economic Review, 94(2):52–8.

38. Reinhart, C., Rogoff, K., & Savastano, M. (2003). Addicted to dollars. NBER Working Paper 10015.

39. Rennhack, R. & Nozaki, M. (2006). Financial dollarization in Latin America. IMF Working Paper WP/06/07. Washington, DC.

40. Restrictions (R) – Index of restrictions on holdings of foreign currency deposits by residents, ranging from zero (no restrictions) to 5 (maximum restrictions). Source: Levy-Yeyati (2006). Original sources: IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (EAER), revised and expanded by Levy-Yeyati from De Nicoló et al. (2003) using the same methodology. Minimum Variance Portfolio (MV P) – This is derived from a portfolio choice model, in which riskaverse local investors opt between a local-currency-denominated and a dollar-denominated asset.
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41. Salles, F. (2007). Liquidity, Jurisdictional Uncertainty and High Interest Rates in Brazil. London School of Economics, London.
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42. Thus, to estimate the real interest rate equation with its one-year lagged value as one of the regressors, we adopted a two-step GMM system estimation (level and difference combined, GMM-SYS) proposed by Blundell and Bond (1998), based on Arellano and Bond (1991) and Arellano and Bover (1995). In this procedure, the one-lagged real interest rate is treated as an endogenous variable and the two-lagged real interest rate is an additional instrument.18 We also used the variance of the two-step estimation to deal with the downward bias in variance estimation in small samples (Windmeijer, 2005).
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43. Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126:667–678.

44. World Bank (2007b). World development indicators online. www.worldbank.org. RBE Rio de Janeiro v. 63 n. 4 / p. 341–360 Out-Dez 357 A Panel-Data Analysis of Interest Rates and Dollarization in Brazil 6. APPENDIX I DATA SOURCES AND DESCRIPTIONS Real Interest Rate (RIR) – Ratio of one plus the average of the annualized end-of-month moneymarket interest rate in IFS (line 60B, ZF) to one plus the average of the annualized monthly consumer price index variation (IFS, line CPI), minus one, in percentage terms.
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