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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Inference in the Bernoulli model
A first regression model
The logit model
The twovariable regression model
The matrix algebra of twovariable regression
The multiple regression model
The matrix algebra of multiple regression
Misspecification analysis in cross sections
Misspecification analysis in time series
The vector autoregressive model
Identification of structural models
Nonstationary time series
Monte Carlo simulation experiments
Automatic model selection
Empirical models and modeling
Autoregressions and stationarity