This collection of papers delivered at the fifth international Symposium in Economic Theory and Econometrics in 1988 is devoted to recent advances in the estimation and testing of models that impose relatively weak restrictions on the stochastic behavior of data. Particularly in highly nonlinear models, empirical results are very sensitive to the choice of the parametric form of the distribution of the observable variables, and often nonparametric and semiparametric models are a preferable alternative. Methods and applications that do not require strong parametric assumptions for their validity, that are based on kernels and on series expansions, and methods for independent and dependent observations, are investigated and developed in these essays by renowned econometricians.
|Publisher:||Cambridge University Press|
|Series:||International Symposia in Economic Theory and Econometrics Series , #5|
|Product dimensions:||6.00(w) x 8.90(h) x 1.00(d)|
Table of Contents
Editors' preface; Part I. Methods and Applications Based on Kernels: 1. Semiparametric last squares estimation of multiple index models: single equation estimation Hidehiko Ichimura and Lung-Fei Lee; 2. Nonparametric estimation and the risk premium A. R. Pagan and Y. S. Hong; 3. Nonparametric policy analysis: an application to estimating hazardous waste cleanup benefits James H. Stock; 4. Equivalence of direct, indirect, and slope estimators of average derivatives Thomas M. Stoker; 5. Equivalence of direct, indirect, and slope estimators of average derivatives: a comment T. Scott Thompson; Part II. Methods and Applications Based on Series Expansions: 6. Seminonparametric Bayesian estimation of the asymptotically ideal model: the AIM consumer demand system William A. Barrett, John Geweke, and Piyu Yue; 7. Semiparametric estimation of a regression model with sampling selectivity Stephen R. Cosslett; 8. On fitting a recalcitrant series: the pound/dollar exchange rate, 1974-84 A. Ronald Gallant, David A. Hsieh and George E. Taucher; Part III. Methods for Independent Observations: 9. A nonparametric method-of-moments estimator for the mixture-of-exponentials model and the mixture-of-geometrics model James J. Heckman; 10. Nonparametric estimation of expectations in the analysis of discrete under uncertainty Charles F. Manski; 11. A nonparametric maximum rank correlation estimator Rosa L. Matzkin; 12. Efficient estimation of Tobit models under conditional symmetry Whitney K. Newey; 13. Bracketing methods in statistics and econometrics David Pollard; 14. Estimation of monotonic regression models under quantile restrictions James L. Powell; Part IV. Models for Dependent Observations: 15. Computing semiparametric efficiency bounds for linear time series models Lars Peter Hansen and Kenneth J. Singleton; 16. Spectral regression for cointegrated time series P. C. B. Phillips; 17. Nonparametric function estimation for long memory time series Peter M. Robinson; 18. Some results on sieve estimation with dependent observations Halbert White and Jeffrey M. Wooldridge.