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Models with Unexpected Components: The Case for Efficient Estimation

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  • Tufte, David
  • Wohar, Mark E

Abstract

Financial models often use unexpected explanatory variables. Conventionally, these are generated as the residuals of auxiliary equations, which are then substituted into the model of interest in a second step. This induces an econometric problem into the estimates, which is typically ignored. We propose a maximum likelihood estimation method as a solution. While there may be a predisposition when using financial data to dismiss our method as difficult to specify correctly, Monte Carlo simulations show that our method is robust. Further, we show that the magnitude of errors due to the generated regressor problem is somewhat larger than that due to ignoring the effects of plausible levels of leptokurtosis. An empirical example using commercial bank stock returns finds that hypothesis test conclusions from the conventional method can often be overturned. Copyright 1999 by Kluwer Academic Publishers

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  • Tufte, David & Wohar, Mark E, 1999. "Models with Unexpected Components: The Case for Efficient Estimation," Review of Quantitative Finance and Accounting, Springer, vol. 13(3), pages 295-313, November.
  • Handle: RePEc:kap:rqfnac:v:13:y:1999:i:3:p:295-313
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    Cited by:

    1. Lahrech, Abdelmounaim & Sylwester, Kevin, 2011. "U.S. and Latin American stock market linkages," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1341-1357.
    2. Alexey Akimov & Simon Stevenson, 2013. "Securitised Real Estate Regime-Switching Behaviour and the Relationship with Market Interest Rates," ERES eres2013_346, European Real Estate Society (ERES).
    3. Kim, J.W. & Leatham, D.J. & Bessler, D.A., 2007. "REITs' dynamics under structural change with unknown break points," Journal of Housing Economics, Elsevier, vol. 16(1), pages 37-58, March.

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