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ADVANCED TEXTS IN ECONOMETRICS
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Over the last five decades, significant advances in the estimation and inference of various econometric models have taken place. This includes the classical linear model where the explanatory variables are nonstochastic (fixed) and the error is normally distributed, and the non-classical models, where these classical assumptions are violated. These models are frequently used in applied work, such as the simultaneous equation models, models with heteroskedasticity and/or serial correlation, limited dependent variable models, panel data models, and a large class of time series models. Many of these models may also be nonlinear, explanatory variables can be stochastic and errors follow non-normal distributions. While the classical linear model is often estimated by the ordinary least squares (LS) or generalized least squares (GLS) estimators, the non-classical models have largely used the maximum likelihood (ML), the method of moments, the instrumental variable, and the extremum estimation techniques. Within this setup, establishing the properties of estimators in the classical linear model are straightforward for samples of any size and they are well presented in econometrics textbooks. For the non-classical models, however, textbooks have mostly presented large sample theory results despite the existing finite sample analytical results. One explanation of this may be the technical difficulties in developing the existing finite sample results and the complexities of their expressions.
Aman Ullah - Personal Name
1st Edtion
0-19-877447-8
NONE
ADVANCED TEXTS IN ECONOMETRICS
Economics
English
Oxford University Press Inc
2014
New York
1-241
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