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Nonparametric and Semiparametric Methods in Econometrics and Statistics

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  • Barnett,William A.
  • Powell,James
  • Tauchen,George E.

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Abstract

This collection of papers delivered at the Fifth International Symposium in Economic Theory and Econometrics in 1988 is devoted to the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data. Particularly in highly non-linear 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 string 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.

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Bibliographic Info

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This book is provided by Cambridge University Press in its series Cambridge Books with number 9780521424318 and published in 1991.

Order: http://www.cambridge.org/uk/catalogue/catalogue.asp?isbn=9780521424318
Handle: RePEc:cup:cbooks:9780521424318

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Web page: http://www.cambridge.org

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Cited by:
  1. J. M. C. Santos Silva & Silvana Tenreyro, 2009. "Trading Partners and Trading Volumes: Implementing the Helpman-Melitz-Rubinstein Model Empirically," CEP Discussion Papers dp0935, Centre for Economic Performance, LSE.
  2. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly, 2012. "Inference on counterfactual distributions," CeMMAP working papers CWP05/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Hussinger, Katrin, 2003. "R&D and Subsidies at the Firm Level: An Application of Parametric and Semi-Parametric Two-Step Selection Models," ZEW Discussion Papers 03-63, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  4. Klein, Roger & Shen, Chan & Vella, Francis, 2011. "Semiparametric Selection Models with Binary Outcomes," IZA Discussion Papers 6008, Institute for the Study of Labor (IZA).
  5. Kyoo il Kim, 2006. "Uniform Convergence Rate of the SNP Density Estimator and Testing for Similarity of Two Unknown Densities," Labor Economics Working Papers 22451, East Asian Bureau of Economic Research.
  6. Insik Min & Sheng jang Sheu & Zijun Wang, 2003. "A Monte Carlo Comparison of Various Semiparametric Type-3 Tobit Estimators," Annals of Economics and Finance, Society for AEF, vol. 4(1), pages 125-136, May.
  7. Song Song, 2011. "Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach," Papers 1106.3921, arXiv.org, revised Jun 2011.
  8. Yingcun Xia & Wolfgang Härdle & Oliver Linton, 2009. "Optimal Smoothing for a Computationally and Statistically Efficient Single Index Estimator," SFB 649 Discussion Papers SFB649DP2009-028, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  9. Daniela Palma & Alessandro Zini, 2005. "Technological change and industry competitiveness through the evolution of localised comparative advantages - The case of Italy," ERSA conference papers ersa05p641, European Regional Science Association.
  10. George J. Jiang & Pieter J. van der Sluis, 1998. "Pricing Stock Options under Stochastic Volatility and Stochastic Interest Rates with Efficient Method of Moments Estimation," Tinbergen Institute Discussion Papers 98-067/4, Tinbergen Institute.
  11. Feng Zhu, 2005. "The fragility of the Phillips curve: A bumpy ride in the frequency domain," BIS Working Papers 183, Bank for International Settlements.

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