# Econometric Modeling: A Likelihood Approach

Princeton University Press, Mar 25, 2007 - Business & Economics - 365 pages

Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques.

David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointegrated systems. In each setting, a statistical model is constructed to explain the observed variation in the data, with estimation and inference based on the likelihood function. Substantive issues are always addressed, showing how both statistical and economic assumptions can be tested and empirical results interpreted. Important empirical problems such as structural breaks, forecasting, and model selection are covered, and Monte Carlo simulation is explained and applied.

Econometric Modeling is a self-contained introduction for advanced undergraduate or graduate students. Throughout, data illustrate and motivate the approach, and are available for computer-based teaching. Technical issues from probability theory and statistical theory are introduced only as needed. Nevertheless, the approach is rigorous, emphasizing the coherent formulation, estimation, and evaluation of econometric models relevant for empirical research.

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### Contents

 Inference in the Bernoulli model 14 A first regression model 28 The logit model 47 The twovariable regression model 66 The matrix algebra of twovariable regression 88 The multiple regression model 98 The matrix algebra of multiple regression 121 Misspecification analysis in cross sections 127
 Misspecification analysis in time series 190 The vector autoregressive model 203 Identification of structural models 217 Nonstationary time series 240 Cointegration 254 Monte Carlo simulation experiments 270 Automatic model selection 286 Structural breaks 302

 Strong exogeneity 140 Empirical models and modeling 154 Autoregressions and stationarity 175