Econometric Modeling:A Likelihood Approach计量经济建模：可能性研究 mobi 下载 网盘 caj lrf pdf txt 阿里云

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.

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

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