48Martin S. Feldstein, "Inflation, Tax Rules and Investment: Some Econometric Evidence, Econometrica, vol. 30, 1982, p. 829.
3. Thou shalt know the context (do not perform ignorant statistical analysis).
4. Thou shalt inspect the data.
5. Thou shalt not worship complexity. Use the KISS principle, that is, keep it stochastically simple.
6. Thou shalt look long and hard at thy results.
7. Thou shalt beware the costs of data mining.
8. Thou shalt be willing to compromise (do not worship textbook prescriptions).
9. Thou shalt not confuse significance with substance (do not confuse statistical significance with practical significance).
10. Thou shalt confess in the presence of sensitivity (that is, anticipate criticism).
You may want to read Kennedy's paper fully to appreciate the conviction with which he advocates the above ten commandments. Some of these commandments may sound tongue-in-cheek, but there may be a grain of truth in each.
1. The assumption of the CLRM that the econometric model used in analysis is correctly specified has two meanings. One, there are no equation specification errors, and two, there are no model specification errors. In this chapter the major focus was on equation specification errors.
2. The equation specification errors discussed in this chapter were (1) omission of important variable(s), (2) inclusion of superfluous vari-able(s), (3) adoption of the wrong function form, (4) incorrect specification of the error term ui, and (5) errors of measurement in the regressand and regressors.
3. When legitimate variables are omitted from a model, the consequences can be very serious: The OLS estimators of the variables retained in the model not only are biased but are inconsistent as well. Additionally, the variances and standard errors of these coefficients are incorrectly estimated, thereby vitiating the usual hypothesis-testing procedures.
4. The consequences of including irrelevant variables in the model are fortunately less serious: The estimators of the coefficients of the relevant as well as "irrelevant" variables remain unbiased as well as consistent, and the error variance a2 remains correctly estimated. The only problem is that the estimated variances tend to be larger than necessary, thereby making for less precise estimation of the parameters. That is, the confidence intervals tend to be larger than necessary.
5. To detect equation specification errors, we considered several tests, such as (1) examination of residuals, (2) the Durbin-Watson d statistic, (3) Ramsey's RESET test, and (4) the Lagrange multiplier test.
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