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Economics

ADVANCED TEXTS IN ECONOMETRICS

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.

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Detail Information
Series Title
ADVANCED TEXTS IN ECONOMETRICS
Call Number
-
Publisher
New York : Oxford University Press Inc.,
Collation
1-241
Language
English
ISBN/ISSN
0-19-877447-8
Classification
NONE
Content Type
-
Media Type
-
Carrier Type
-
Edition
1st Edtion
Subject(s)
Economics
Specific Detail Info
-
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