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On the Heterogeneity Bias of Pooled Estimators in Stationary VAR Specifications

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Author Info

  • Alessandro Rebucci

Abstract

This paper studies asymptotically the bias of the fixed effect (FE) estimator induced by cross-section heterogeneity in the slope parameters of stationary vector autoregressions (VARs). The paper also compares the FE, the mean group estimator (MG), and a simple instrumental variable alternative (IV) in Monte Carlo simulations. The main results are: (i) asymptotically, the heterogeneity bias of the FE may be more or less severe in VAR specifications than in standard dynamic panel data specifications; (ii) in Monte Carlo simulations, slope heterogeneity must be relatively high to be a source of concern for pooled estimators; (iii) when this happens, the panel must be longer than a typical macro dataset for the MG to be a viable solution.

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Bibliographic Info

Paper provided by International Monetary Fund in its series IMF Working Papers with number 03/73.

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Length: 45
Date of creation: 01 Apr 2003
Date of revision:
Handle: RePEc:imf:imfwpa:03/73

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Related research

Keywords: Economic models; Exchange risk; time series; finite sample; sample bias; econometrics; equation; correlation; covariance; standard errors; dynamic panel; dynamic panel data; monte carlo simulations; standard deviations; regression equation; parameter value; monte carlo simulation; forecasting; samples; cointegration; equations; mean group; dynamic panel data models; statistics; mean group estimator; granger causality; correlations; individual time series; autocorrelation; normal distribution; instrumental variable; linear regression; random coefficient; data analysis; constant variance; degree of heterogeneity; aggregate time series; regression model; simultaneous equation; dynamic models; simultaneous equations; econometrics of panel data; dynamic heterogeneous panels; estimation procedure; random variable; causation; standard error; arithmetic; fixed effects estimator; law of large numbers; linear models; prediction; factor analysis; econometric issues; instrumental variables; simulation results; polynomials; dynamic linear models; probability; mean square; number of parameters; number of variables;

References

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  1. Carroll, Christopher D. & Weil, David N., 1994. "Saving and growth: a reinterpretation," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 40(1), pages 133-192, June.
  2. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
  3. Karim Abadir & Kaddour Hadri & Elias Tzavalis, . "The Influence of VAR Dimensions on Estimator Biases," Discussion Papers 96/14, Department of Economics, University of York.
  4. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
  5. Michael Binder & Cheng Hsiao & M. Hashem Pesaran, 2000. "Estimation and Inference in Short Panel Vector Autoregressions with Unit Roots and Cointegration," Banco de Espa�a Working Papers 0005, Banco de Espa�a.
  6. Hendry, David F., 1984. "Monte carlo experimentation in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 16, pages 937-976 Elsevier.
  7. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-95, November.
  8. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
  9. Hsiao, C. & Pesaran, M. H. & Tahmiscioglu, A. K., 1998. "Bayes Estimation of Short-run Coefficients in Dynamic Panel Data Models," Cambridge Working Papers in Economics 9804, Faculty of Economics, University of Cambridge.
  10. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
  11. Larsson, Rolf & Lyhagen, Johan, 1999. "Likelihood-Based Inference in Multivariate Panel Cointegration Models," Working Paper Series in Economics and Finance 331, Stockholm School of Economics.
  12. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
  13. Donald Robertson & James Symons, 1991. "Some Strange Properties of Panel Data Estimators," CEP Discussion Papers dp0044, Centre for Economic Performance, LSE.
  14. Banerjee, Anindya, 1999. " Panel Data Unit Roots and Cointegration: An Overview," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 607-29, Special I.
  15. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  16. Cornwell, Christopher & Schmidt, Peter & Wyhowski, Donald, 1992. "Simultaneous equations and panel data," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 151-181.
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Citations

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Cited by:
  1. Robert Gillanders, 2011. "The Effects of Foreign Aid in Sub-Saharan Africa," Working Papers 201116, School Of Economics, University College Dublin.
  2. Towbin, Pascal & Weber, Sebastian, 2013. "Limits of floating exchange rates: The role of foreign currency debt and import structure," Journal of Development Economics, Elsevier, vol. 101(C), pages 179-194.
  3. Peter Tillmann, 2012. "Capital Inflows and Asset Prices: Evidence from Emerging Asia," Working Papers 182012, Hong Kong Institute for Monetary Research.
  4. Aiyar, Shekhar & Calomiris , Charles W & Wieladek, Tomasz, 2012. "Does macropru leak? Evidence from a UK policy experiment," Bank of England working papers 445, Bank of England.
  5. Tomasz Wieladek & Sergi Lanau, 2012. "Financial Regulation and the Current Account," IMF Working Papers 12/98, International Monetary Fund.

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