TY  - BOOK
AU  - Gourieroux,Christian
AU  - Monfort,Alain
TI  - Statistics and econometric models : : volume 1: general concepts, estimation, prediction and algorithms / 
T2  - Themes in Modern Econometrics 
SN  - 0521405513
U1  - 330.015195 20 
PY  - 1995///
CY  - Cambridge ; , New York
PB  - Cambridge University Press, 
KW  - Modelos econométricos
N1  - Incluye bibliografías e índice; 1. Models: 1.1. Modelling ; 1.2. Statiscal models ; 1.3. Intermediate ; 1.4. Conditional models ; 1.5. Dynamic models ; 1.6. Exercises -- 2. Statistical problems and decision theory: 2.1. Examples of statistical problems ; 2.2. Decision rules ; 2.3. Ordering decision rules ; 2.4. An example ; 2.5. Other orderings of decision rules ; 2.6. Exercises -- 3. Statistical information: classical approach: 3.1. Sufficiency ; 3.2. Ancillarity ; 3.3. Information measures ; 3.4. Identification ; 3.5. Exercises -- 4. Bayesian interpretations of sufficiency, Ancillarity and identification: 4.1. Sufficiency ; 4.2. Ancillarity ; 4.3. Identification ; 4.4. Exercises -- 5. Elements of estimation theory: 5.1. Consequences of decision theory ; 5.2. Estimation principles ; 5.3. Search for good estimators ; 5.4. Exercises -- 6. Unbiased estimation: 6.1. Definitions ; 6.2. FDRC inequality ; 6.3. Best unbiased estimators ; 6.4. Best invariant unbiased estimators ; 6.5. Biased and unbiased estimators ; 6.6. Exercises -- 7. Maximum likelihood estimation: 7.1. Principle ; 7.2. Likelihood equations ; 7.3. Finite sample properties ; 7.4. Asymptotic properties ; 7.5. Marginal and conditional ml estimation ; 7.6. Exercises -- 8. M-estimation: 8.1. Definition and asymptotic properties ; 8.2. Nonlinear regression models of order 1 and 2 ; 8.3. Nonlinear least squares ; 8.4. Pseudo maximum likelihood estimation ; 8.5. Estimation of a conditional median ; 8.6. Appendix ; 8.7. Exercises -- 9. Methods of moments and their generalizations: 9.1. Asymptotic least squares ; 9.2. Examples ; 9.3. Seemingly linear models ; 9.4. Instrumental variable estimation ; 9.5. Generalized methods of moments ; 9.6. Exercises -- 10. Estimation under equality constraints: 10.1. Constraints ; 10.2. Least squares under linear constraints ; 10.3. Asymptotic properties ; 10.4. Constrained two-step estimation ; 10.5. Examples ; 10.6. Exercises -- 11. Prediction: 11.1. General concepts ; 11.2. Examples ; 11.3. Residuals ; 11.4. Appendix ; 11.5. Exercises -- 12.  Bayesian estimation: 12.1. The Bayesian approach ; 12.2. Conjugate priors ; 12.3. Asymptotic results ; 12.4. Diffuse priors ; 12.5. Best linear unbiased Bayesian estimation ; 12.6. Approximate determination of posterior distributions ; 12.7. Appendix ; 12.8. Exercises -- 13.  Numerical procedures: 13.1. Numerical optimization ; 13.2. Fixed point methods ; 13.3. EM Algorithm ; 13.4. Kalman filter ; 13.5. Prediction and smoothing in state space models ; 13.6. Recursive least squares and recursive residuals ; 13.7. Exercises
ER  -