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Pitfalls in Measuring Exchange Rate Misalignment

The Yuan and Other Currencies

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Abstract

We evaluate whether the Renminbi (RMB) is misaligned, relying upon conventional statistical methods of inference. A framework built around the relationship between relative price and relative output levels is used. We find that, once sampling uncertainty and serial correlation are accounted for, there is little statistical evidence that the RMB is undervalued, even though the usual regression point estimates indicate substantial misalignment. The result is robust to various choices of country samples and sample periods, as well as to the inclusion of control variables. We then update the results using the latest vintage of the data to demonstrate how fragile the results are. We find that whatever misalignment we detected in our previous work disappears in this data set.

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Notes

  1. One relevant work is Hinkle and Montiel (1999).

  2. See Table 1 of Cheung et al. (forthcoming) for a typology of these different approaches.

  3. Coudert and Couharde (2005) implement the absolute PPP regression on a cross-section, while their panel estimation relies upon estimating the relationship between the relative price level to relative tradables to nontradables price indices.

  4. Frankel (2006) discusses whether one can speak of an “equilibrium exchange rate” when there is more than one sector to consider.

  5. β 0 can take on currency specific values if a fixed effects specification is implemented. Similarly, the error term is composed of a currency specific and aggregate error if the pooled OLS specification is dropped.

  6. Since the price levels being used are comparable across countries, in principle there is no need to incorporate country-specific constants as in fixed effects or random effects regressions. In addition, fixed effects estimates are biased in the presence of serial correlation, which is documented in the subsequent analysis.

  7. Note that, in addition to differences in the sample, our estimates differ from Frankel’s in that we measure each country’s (logged) real GDP per capita in terms relative to the US rather than in absolute terms.

  8. In essence, the Prais-Winsten method is an efficient procedure that incorporates serial correlation into the estimation process. We also implemented the Arellano-Bond approach that introduces lagged dependent variables into the model to account for serial correlation. The validity of the Arellano-Bond depends on the use of “good” instruments and the assumption that the number of time series observation is greater than the number of cross-sectional variables. In the current case, the choice of instruments is a practical issue and the time series dimension is smaller than the number of economies.

  9. Frankel (2008) observes that the 5% significance level might be too stringent a criterion in this context. For instance, a 50% significance level of a two-sided test would be consistent with a plus/minus 0.67 standard error band.

  10. Statistics from Asian Development Bank (2007). See also Elekdag and Lall (2008) and International Comparison Program (2007) for discussion.

  11. Note that the serial correlation in the residual drops only slightly in this data set, to about 0.94.

  12. See Devereux (1999) for an early observation of this pattern.

  13. The slope coefficient estimates from year-by-year regressions show a similar upward trend. The slope coefficient starts with a low of 0.14 at 1975 and moves up gradually to the high of 0.39 at 1995. Then it stays quite steady around the level of 0.36 for the rest of the sample.

  14. See Rose and Supaat (2007) for a discussion. They focus on fertility rate, in their model of the trade weighted exchange rate, as their key demographic variable.

  15. In our previous analysis, we included a government deficit variable because Chinn and Prasad (2003) find that it explains part of current account balances over the medium term. In the newest version of the WDI, there were many missing observations; hence, we opted to exclude the fiscal variable, in order to maintain the sample size. Note that in our previous set of results, the budget deficit was seldom statistically significant.

  16. The results are also substantially different from the corresponding results obtained using the previous vintage of the WDI.

  17. The bivariate estimation results indicate that a) the Thai baht was overvalued by 11.5% in 1996, and b) the Argentine peso was overvalued by 35% in 2001.

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Acknowledgments

We thank for useful comments Jeffrey Frankel, the discussant, Christoph Fischer, and participants at the workshop “Panel Methods and Open Economies”, held at Goethe University Frankfurt, May 21, 2008, organized by Michael Binder and Heinz Herrmann. Kavan Kucko provided assistance in collecting data, and Hiro Ito provided additional data. Financial support of faculty research funds of the University of California at Santa Cruz, the University of Wisconsin, the Japan Center for Economic Research grant, and the Nomura Foundation for Social Science research grant is gratefully acknowledged. The views expressed are solely those of the authors.

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Authors and Affiliations

  1. Department of Economics, University of California, Santa Cruz, CA, 95064, USA

    Yin-Wong Cheung

  2. Graduate School of Systems and Information Engineering, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki, Japan

    Eiji Fujii

  3. Robert M. LaFollette School of Public Affairs, Department of Economics, University of Wisconsin, and NBER, 1180 Observatory Drive, Madison, WI, 53706-1393, USA

    Menzie D. Chinn

  4. School of Economics and Finance, University of Hongkong, Pokfulam Road, Hongkong

    Yin-Wong Cheung

Authors
  1. Yin-Wong Cheung
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  2. Menzie D. Chinn
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  3. Eiji Fujii
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Corresponding author

Correspondence to Menzie D. Chinn.

Appendix 1: Data and sources

Appendix 1: Data and sources

The data for macroeconomic aggregates are drawn mostly from the World Bank’s World Development Indicators. The old vintage is the 2006 version. The new vintage is the 2008 version. These include demographic variables, per capita income, M2/GDP and government deficits. The capital controls index is from Chinn and Ito (2006). The (inverse) corruption index is drawn from the International Country Risk Guide. Data for Taiwan are drawn from the Central Bank of China, International Centre for the Study of East Asian Development, and Asian Development Bank, Key Indicators of Developing Asian and Pacific Countries (old data set).

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Cheung, YW., Chinn, M.D. & Fujii, E. Pitfalls in Measuring Exchange Rate Misalignment. Open Econ Rev 20, 183–206 (2009). https://doi.org/10.1007/s11079-008-9097-1

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