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...
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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.
The Bernoulli model -- Inference in the Bernoulli model -- A first regression model -- The logit model -- The two-variable regression model -- The matrix algebra of two-variable regression -- The multiple regression model -- The matrix algebra of multiple regression -- Mis-specification analysis in cross sections -- Strong exogeneity -- Empirical models and modeling -- Autoregressions and stationarity -- Mis-specification analysis in time series -- The vector autoregressive model -- Identification of structural models -- Non-stationary time series -- Cointegration -- Monte Carlo simulation experiments -- Automatic model selection -- Structural breaks -- Forecasting -- The way ahead.
