Author
David F. Hendry, University of Oxford
Bent Nielsen, University of Oxford
Description
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 ( Details )
Ch 1: The Bernoulli model
Ch 2: Inference in the Bernoulli model
Ch 3: A first regression model
Ch 4: The logit model
Ch 5: The two-variable regression model
Ch 6: The matrix algebra of two-variable regression
Ch 7: The multiple regression model
Ch 8: The matrix algebra of multiple regression
Ch 9: Mis-specification analysis in cross sections
Ch 10: Strong exogeneity
Ch 11: Empirical models and modeling
Ch 12: Autoregressions and stationarity
Ch 13: Mis-specification analysis in time series
Ch 14: The vector autoregressive model
Ch 15: Identification of structural models
Ch 16: Non-stationary time series
Ch 17: Cointegration
Ch 18: Monte Carlo simulation experiments
Ch 19: Automatic model selection
Ch 20: Structural breaks
Ch 21: Forecasting
Ch 22: The way ahead
