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Econometric Modeling : a Likelihood Approach.

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 approac...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Hendry, David F.
Otros Autores: Nielsen, Bent
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New Jersey : Princeton University Press, 2012.
Temas:
Acceso en línea:Texto completo

MARC

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520 |a 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 cointe. 
505 0 0 |t Frontmatter --  |t Contents --  |t Preface --  |t Data and software --  |t Chapter One. The Bernoulli model --  |t Chapter Two. Inference in the Bernoulli model --  |t Chapter Three. A first regression model --  |t Chapter Four. The logit model --  |t Chapter Five. The two-variable regression model --  |t Chapter Six. The matrix algebra of two-variable regression --  |t Chapter Seven. The multiple regression model --  |t Chapter Eight. The matrix algebra of multiple regression --  |t Chapter Nine. Mis-specification analysis in cross sections --  |t Chapter Ten. Strong exogeneity --  |t Chapter Eleven. Empirical models and modeling --  |t Chapter Twelve. Autoregressions and stationarity --  |t Chapter Thirteen. Mis-specification analysis in time series --  |t Chapter Fourteen. The vector autoregressive model --  |t Chapter Fifteen. Identification of structural models --  |t Chapter Sixteen. Non-stationary time series --  |t Chapter Seventeen. Cointegration --  |t Chapter Eighteen. Monte Carlo simulation experiments --  |t Chapter Nineteen. Automatic model selection --  |t Chapter Twenty. Structural breaks --  |t Chapter Twenty One. Forecasting --  |t Chapter Twenty Two. The way ahead --  |t References --  |t Author index --  |t Subject index 
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