By Z. Griliches, M.D. Intriligator
The guide is a definitive reference resource and instructing reduction for econometricians. It examines versions, estimation thought, info research and box functions in econometrics. finished surveys, written by means of specialists, talk about contemporary advancements at a degree compatible for pro use by way of economists, econometricians, statisticians, and in complex graduate econometrics classes. for additional info at the Handbooks in Economics sequence, please see our domestic web page on http://www.elsevier.nl/locate/hes
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Additional info for Handbook of Econometrics, Volume 2
The test is constructed by first searching over the possible values of p to find the maximum LM test statistic, and then finding the limiting distribution of the test to determine the critical value. A Monte Carlo evaluation of the test showed it to work reasonably well except for values of p close to unity when the limiting distribution was well approximated only for quite large samples. Several other applications of this result occur in econometrics. In factor analytical models, the number of parameters varies with the number of factors so testing the number of factors may involve such a problem.
A s an example, consider testing for a liquidity trap i n the demand for money. Several studies have examined this hypothesis. Pifer (1969), White (1972) and Eisner (1971) test for a liquidity trap in logarithmic or Box-Cox functional forms while Konstas and Khouja (1969) (K-K) use a linear specification. Most studies find maximum likelihood estimates of the interest rate floor to be about 2% but they differ on whether this figure is significantly different from zero. Pifer says it is not significant, Eisner corrects his likelihood ratio test and says it is, White generalizes the form using a Box-Cox transformation and concludes that it is not different from zero.
Ii1 _ ,), or equivalently, on xn Z1 (y_ 1 , . . , y_ ). , Now if x includes lags, the test must be very slightly modified. The matrix z will, in this case, include variables which are already in the model and thus the auxiliary regression will see a data set with perfect multicollinearity. The solution is to eliminate the redundant elements of z as these are not testable in any case. The test statistic will have a correspondingly reduced number of degrees of freedom. A more complicated case occurs when it is desired to test that the correlation is of order r against the alternative that it is of order r - 1.
Handbook of Econometrics, Volume 2 by Z. Griliches, M.D. Intriligator