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Econometric Modelling with Time Series: Specification, Estimation and Testing,9780521139816
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Econometric Modelling with Time Series: Specification, Estimation and Testing

by
ISBN13:

9780521139816

ISBN10:
0521139813
Format:
Paperback
Pub. Date:
12/28/2012
Publisher(s):
Cambridge University Press
List Price: $94.99

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Questions About This Book?

What version or edition is this?
This is the edition with a publication date of 12/28/2012.
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  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any CDs, lab manuals, study guides, etc.

Summary

This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.

Table of Contents

Maximum Likelihood:
The maximum likelihood principle
Properties of maximum likelihood estimators
Numerical estimation methods
Hypothesis testing
Regression Models:
Linear regression models
Nonlinear regression models
Autocorrelated regression models
Heteroskedastic regression models
Other Estimation Methods:
Quasi-maximum likelihood estimation
Generalized method of moments
Nonparametric estimation
Estimation by stimulation
Stationary Time Series:
Linear time series models
Structural vector autoregressions
Latent factor models
Non-Station Time Series:
Nonstationary distribution theory
Unit root testing
Cointegration
Nonlinear Time Series:
Nonlinearities in mean
Nonlinearities in variance
Discrete time series models
Change in variable in probability density functions
The lag operator
FIML estimation of a structural model
Additional nonparametric results
Table of Contents provided by Publisher. All Rights Reserved.


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