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A Lagrange multiplier-type test for idiosyncratic unit roots in the exact factor model under misspecification

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  • Zhou, X.

    (Externe publicaties SBE)

  • Solberger, M.

    (Externe publicaties SBE)

Abstract

We consider an exact factor model and derive a Lagrange multiplier-type test for unit roots in the idiosyncratic components. The asymptotic distribution of the statistic is derived under the misspecification that the differenced factors are white noise. We prove that the asymptotic distribution is independent of the distribution of the factors, and that the factors are allowed to be integrated, cointegrate, or be stationary. In a simulation study, size and power is compared with some popular second generation panel unit root tests. The simulations suggest that our statistic is well-behaved in terms of size and that it is powerful and robust in comparison with existing tests.

Suggested Citation

  • Zhou, X. & Solberger, M., 2013. "A Lagrange multiplier-type test for idiosyncratic unit roots in the exact factor model under misspecification," Research Memorandum 058, Maastricht University, Graduate School of Business and Economics (GSBE).
    • Handle: RePEc:unm:umagsb:2013058
    DOI: 10.26481/umagsb.2013058
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    References listed on IDEAS

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    1. Breitung, Jörg & Tenhofen, Jörn, 2011. "GLS Estimation of Dynamic Factor Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1150-1166.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Christian Gengenbach & Franz C. Palm & Jean-Pierre Urbain, 2010. "Panel Unit Root Tests in the Presence of Cross-Sectional Dependencies: Comparison and Implications for Modelling," Econometric Reviews, Taylor & Francis Journals, vol. 29(2), pages 111-145, April.
    4. Sargan, John Denis & Bhargava, Alok, 1983. "Testing Residuals from Least Squares Regression for Being Generated by the Gaussian Random Walk," Econometrica, Econometric Society, vol. 51(1), pages 153-174, January.
    5. Moon, H.R.Hyungsik Roger & Perron, Benoit, 2004. "Testing for a unit root in panels with dynamic factors," Journal of Econometrics, Elsevier, vol. 122(1), pages 81-126, September.
    6. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    7. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
    8. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    9. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    10. Kruiniger, Hugo, 2008. "Maximum likelihood estimation and inference methods for the covariance stationary panel AR(1)/unit root model," Journal of Econometrics, Elsevier, vol. 144(2), pages 447-464, June.
    11. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Jun 2011.
    12. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    13. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    14. van der Leeuw, Jan, 1994. "The covariance matrix of ARMA errors in closed form," Journal of Econometrics, Elsevier, vol. 63(2), pages 397-405, August.
    15. Karanasos, M., 1998. "A New Method For Obtaining The Autocovariance Of An Arma Model: An Exact Form Solution," Econometric Theory, Cambridge University Press, vol. 14(5), pages 622-640, October.
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