Econometric Modeling : : A Likelihood Approach / / David F. Hendry, Bent Nielsen.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013
• VerfasserIn: Hendry, David F.,; Nielsen, Bent,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2012]; ©2007
• Year of Publication: 2012
• Language: English
• Physical Description: 1 online resource (384 p.) :; 50 line illus.

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.

Table of Contents

Frontmatter -- Contents -- Preface -- Data and software -- Chapter One. The Bernoulli model -- Chapter Two. Inference in the Bernoulli model -- Chapter Three. A first regression model -- Chapter Four. The logit model -- Chapter Five. The two-variable regression model -- Chapter Six. The matrix algebra of two-variable regression -- Chapter Seven. The multiple regression model -- Chapter Eight. The matrix algebra of multiple regression -- Chapter Nine. Mis-specification analysis in cross sections -- Chapter Ten. Strong exogeneity -- Chapter Eleven. Empirical models and modeling -- Chapter Twelve. Autoregressions and stationarity -- Chapter Thirteen. Mis-specification analysis in time series -- Chapter Fourteen. The vector autoregressive model -- Chapter Fifteen. Identification of structural models -- Chapter Sixteen. Non-stationary time series -- Chapter Seventeen. Cointegration -- Chapter Eighteen. Monte Carlo simulation experiments -- Chapter Nineteen. Automatic model selection -- Chapter Twenty. Structural breaks -- Chapter Twenty One. Forecasting -- Chapter Twenty Two. The way ahead -- References -- Author index -- Subject index