ECONOMETRIC MODELING: MODEL SPECIFICATION AND DIAGNOSTIC TESTING
Applied econometrics cannot be done mechanically; it needs understanding, intuition and skill.1
. . . we generally drive across bridges without worrying about the soundness of their construction because we are reasonably sure that someone rigorously checked their engineering principles and practice. Economists must do likewise with models or else attach the warning 'not responsible if attempted use leads to collapse'.2
Economists' search for "truth" has over the years given rise to the view that economists are people searching in a dark room for a non-existent black cat; econo-metricians are regularly accused of finding one.3
One of the assumptions of the classical linear regression model (CLRM), Assumption 9, is that the regression model used in the analysis is "correctly" specified: If the model is not "correctly" specified, we encounter the problem of model specification error or model specification bias. In this chapter we take a close and critical look at this assumption, because searching for the correct model is like searching for the Holy Grail. In particular we examine the following questions:
1. How does one go about finding the "correct" model? In other words, what are the criteria in choosing a model for empirical analysis?
'Keith Cuthbertson, Stephen G. Hall, and Mark P. Taylor, Applied Econometrics Techniques, Michigan University Press, 1992, p. X.
2David F. Hendry, Dynamic Econometrics, Oxford University Press, U.K., 1995, p. 68. 3Peter Kennedy, A Guide to Econometrics, 3d ed., The MIT Press, Cambridge, Mass., 1992, p. 82.
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2. What types of model specification errors is one likely to encounter in practice?
3. What are the consequences of specification errors?
4. How does one detect specification errors? In other words, what are some of the diagnostic tools that one can use?
5. Having detected specification errors, what remedies can one adopt and with what benefits?
6. How does one evaluate the performance of competing models?
The topic of model specification and evaluation is vast, and very extensive empirical work has been done in this area. Not only that, but there are philosophical differences on this topic. Although we cannot do full justice to this topic in one chapter, we hope to bring out some of the essential issues involved in model specification and model evaluation.
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