By Badi H. Baltagi
A spouse to Theoretical Econometrics offers a finished connection with the fundamentals of econometrics. This significant other makes a speciality of the principles of the sector and even as integrates renowned issues usually encountered via practitioners. The chapters are written by way of foreign specialists and supply updated study in parts no longer often coated through commonplace econometric texts. specializes in the principles of econometrics. Integrates real-world themes encountered via pros and practitioners. attracts on updated study in components now not lined through common econometrics texts. geared up to supply transparent, obtainable details and element to extra readings.
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Additional resources for A Companion to Theoretical Econometrics (Blackwell Companions to Contemporary Economics)
These models have been used in labor economics to study the duration of individual unemployment spells and in health economics to study the length of hospital stays to mention just two examples. Gourieroux and Jasiak discuss the main duration distribution families including the exponential, gamma, Weibull, and lognormal distributions. They explain what is meant by survivor functions, hazard functions, and duration dependence. Maximum likelihood estimation for the exponential duration model without heterogeneity as well as the gamma distributed heterogeneity model are given.
The latter application has recently been extended to models with AR(1) errors by Baltagi (1999). An accessible discussion of the DLR may be R. G. MACKINNON found in Davidson and MacKinnon (1988). When both the OPG regression and the DLR are available, the finite-sample performance of the latter always seems to be very much better than that of the former. As we remarked earlier, the DLR is not the only artificial regression with 2n artificial observations. In particular, Orme (1995) showed how to construct such a regression for the widely-used tobit model, and Davidson and MacKinnon (1999) provided evidence that Orme’s regression generally works very well.
3), in which E(uu = )ׅΩ, where Ω is an n × n diagonal matrix with tth diagonal element ω 2t . Let V denote an n × n diagonal matrix with the squared residual û 2t as the tth diagonal element. 37) provides an estimator of var(S), which can be used in place of the usual estimator, s2(tׅt)−1. Like the latter, this heteroskedasticity-consistent covariance matrix estimator, or HCCME, can be computed by means of an artificial regression. We will refer to this regression as the heteroskedasticity-robust Gauss–Newton regression, or HRGNR.
A Companion to Theoretical Econometrics (Blackwell Companions to Contemporary Economics) by Badi H. Baltagi