Models with Unexpected Components: The Case for Efficient Estimation

Models with Unexpected Components: The Case for Efficient Estimation 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. Review of Quantitative Finance and Accounting Springer Journals

Models with Unexpected Components: The Case for Efficient Estimation

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Kluwer Academic Publishers
Copyright © 1999 by Kluwer Academic Publishers
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
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