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Estimator Choice and Fisher's Paradox: A Monte Carlo Study

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  • Guglielmo Maria Caporale
  • Nikitas Pittis

Abstract

This paper argues that Fisher's paradox can be explained away in terms of estimator choice. We analyse by means of Monte Carlo experiments the small sample properties of a large set of estimators (including virtually all available single-equation estimators), and compute the critical values based on the empirical distributions of the t-statistics, for a variety of Data Generation Processes (DGPs), allowing for structural breaks, ARCH effects etc. We show that precisely the estimators most commonly used in the literature, namely OLS, Dynamic OLS (DOLS) and non-prewhitened FMLS, have the worst performance in small samples, and produce rejections of the Fisher hypothesis. If one employs the estimators with the most desirable properties (i.e., the smallest downward bias and the minimum shift in the distribution of the associated t-statistics), or if one uses the empirical critical values, the evidence based on US data is strongly supportive of the Fisher relation, consistently with many theoretical models.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 23 (2004)
Issue (Month): 1 ()
Pages: 25-52

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Handle: RePEc:taf:emetrv:v:23:y:2004:i:1:p:25-52

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Related research

Keywords: Fisher's paradox; Cointegration; Single-equation estimators; Monte Carlo analysis;

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Cited by:
  1. E.Panopoulou, 2005. "A Resolution of the Fisher Effect Puzzle: A Comparison of Estimators," Economics, Finance and Accounting Department Working Paper Series n1500205, Department of Economics, Finance and Accounting, National University of Ireland - Maynooth.
  2. Westerlund, Joakim, 2006. "Panel Cointegration Tests of the Fisher Effect," Research Memorandum 054, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  3. Joakim Westerlund, 2008. "Panel cointegration tests of the Fisher effect," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 193-233.
  4. Ang, James B., 2010. "Research, technological change and financial liberalization in South Korea," Journal of Macroeconomics, Elsevier, vol. 32(1), pages 457-468, March.
  5. Westerlund, Joakim, 2005. "Panel Cointegration Tests of the Fisher Hypothesis," Working Papers 2005:10, Lund University, Department of Economics.
  6. Ang, James, 2009. "The Saving-Investment Dynamics And Financial Sector Reforms in India," MPRA Paper 14498, University Library of Munich, Germany.
  7. Ang, James B., 2011. "Finance and consumption volatility: Evidence from India," Journal of International Money and Finance, Elsevier, vol. 30(6), pages 947-964, October.
  8. Arnwine, Neil & Yigit, Taner M., 2008. "What Fisher knew about his relation, we sometimes forget," Economics Letters, Elsevier, vol. 101(3), pages 193-195, December.
  9. Tang, Chor Foon, 2010. "Revisiting the health-income nexus in Malaysia: ARDL cointegration and Rao's F-test for causality," MPRA Paper 27287, University Library of Munich, Germany.
  10. Ang, James, 2009. "Financial Liberalization Or Repression?," MPRA Paper 14497, University Library of Munich, Germany.
  11. Thorbecke, Willem, 2008. "Global imbalances, triangular trading patterns, and the yen/dollar exchange rate," Journal of the Japanese and International Economies, Elsevier, vol. 22(4), pages 503-517, December.
  12. Ang, James, 2009. "Growth Volatility and Financial Repression: Time Series Evidence from India," MPRA Paper 14412, University Library of Munich, Germany.

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