Econometric Modeling:A Likelihood Approach计量经济建模：可能性研究
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作者： David F. Hendry 著
出 版 社：
出版时间： 2007-3-1字数：版次： 1页数： 365印刷时间： 2007/03/01开本： 16开印次： 1纸张： 胶版纸I S B N ： 9780691130897包装： 平装内容简介
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.
目录
Preface
Data and software
Chapter 1 The BGernoulli model
Chapter 2 Inference in the Bernoulli model
Chapter 3 A first regression model
Chapter 4 THe logit model
Chapter 5 The two-variable regression model
Chapter 6 The matrix algebra of two-variable regression
Chapter 7 The multiple regression model
Chapter 8 The matrix algebra of multiple regression
Chapter 9 Mis-specification analysis in cross sections
Chapter 10 Strong exogeneity
Chapter 11 Empirical models and modeling
Chapter 12 Autoregressions and stationarity
Chapter 13 Mis-specification analysis in time series
Chapter 14 The vector autoregressive model
Chapter 15 Identification of structural models
Chapter 16 Non-stationary time series
Chapter 17 Cointegration
Chapter 18 Monte Carlo simulation experiments
Chapter 19 Automatic model seleciton
Chapter 20 Structural breaks
Chapter 21 Forecasting
Chapter 22 The way ahead
References
Author index
Subject index
