Description 
1 online resource (xxxv, 887 pages) : illustrations 
Series 
Themes in modern econometrics 

Themes in modern econometrics.

Contents 
The maximum likelihood principle  Properties of maximum likelihood estimators  Numerical estimation methods  Hypothesis testing  Linear regression models  Nonlinear regression models  Autocorrelated regression models  Heteroskedastic regression models  Quasimaximum likelihood estimation  Generalized method of moments  Nonparametric estimation  Estimation by stimulation  Linear time series models  Structural vector autoregressions  Latent factor models  Nonstationary distribution theory  Unit root testing  Cointegration  Nonlinearities in mean  Nonlinearities in variance  Discrete time series models 
Summary 
"Maximum likelihood estimation is a general method for estimating the parameters of econometric models from observed data. The principle of maximum likelihood plays a central role in the exposition of this book, since a number of estimators used in econometrics can be derived within this framework. Examples include ordinary least squares, generalized least squares and fullinformation maximum likelihood. In deriving the maximum likelihood estimator, a key concept is the joint probability density function (pdf) of the observed random variables, yt. Maximum likelihood estimation requires that the following conditions are satisfied. (1) The form of the joint pdf of yt is known. (2) The specification of the moments of the joint pdf are known. (3) The joint pdf can be evaluated for all values of the parameters, 9. Parts ONE and TWO of this book deal with models in which all these conditions are satisfied. Part THREE investigates models in which these conditions are not satisfied and considers four important cases. First, if the distribution of yt is misspecified, resulting in both conditions 1 and 2 being violated, estimation is by quasimaximum likelihood (Chapter 9). Second, if condition 1 is not satisfied, a generalized method of moments estimator (Chapter 10) is required. Third, if condition 2 is not satisfied, estimation relies on nonparametric methods (Chapter 11). Fourth, if condition 3 is violated, simulationbased estimation methods are used (Chapter 12). 1.2 Motivating Examples To highlight the role of probability distributions in maximum likelihood estimation, this section emphasizes the link between observed sample data and 4 The Maximum Likelihood Principle the probability distribution from which they are drawn" Provided by publisher 
Bibliography 
Includes bibliographical references and indexes 
Notes 
Print version record; title from online title page (ebrary, viewed Jan. 21, 2014) 
Subject 
Econometric models.


Timeseries analysis.

Form 
Electronic book

Author 
Harris, David, 1969


Hurn, Stan.

ISBN 
113904320X (electronic bk.) 

1139525484 (electronic bk.) 

1139527878 

1139530151 (ebook) 

9781139525480 (electronic bk.) 

9781139043205 (electronic bk.) 

9781139527873 

9781139530156 (ebook) 
