The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Non-parametric Structural Models

  • Rosa L. Matzkin
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2163

Abstract

Nonparametric structural models facilitate the analysis of counterfactuals without making use of parametric assumptions. Such methods make use of the behavioural and equilibrium assumptions specified in economic models to define a mapping between the distribution of the observable variables and the primitive functions and distributions that are used in the model. Using these methods, one can infer elements of the model, such as utility and production functions, that are not directly observed. We review some of the latest works that have dealt with the identification and estimation of nonparametric structural models.

Keywords

Additivity Average derivative methods Control functions Convergence Curse of dimensionality Endogeneity Estimation Identification Instrumental variables Maximum likelihood Nonadditivity Nonparametric structural models Nonseparable models Observable and unobservable explanatory variables Partial integration methods Quantile structural functions Simultaneous equations 
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Bibliography

  1. Ai, C., and X. Chen. 2003. Efficient estimation of models with conditional moments restrictions containing unknown functions. Econometrica 71: 1795–1843.CrossRefGoogle Scholar
  2. Altonji, J.G., and H. Ichimura. 2000. Estimating derivatives in nonseparable models with limited dependent variables. Mimeo, Northwestern University.Google Scholar
  3. Altonji, J.G., and R.L. Matzkin. 2001. Panel data estimators for nonseparable models with endogenous regressors. NBER Working paper T0267.Google Scholar
  4. Altonji, J.G., and R.L. Matzkin. 2005. Cross section and panel data estimators for nonseparable models with endogenous regressors. Econometrica 73: 1053–1102.CrossRefGoogle Scholar
  5. Athey, S., and G. Imbens. 2006. Identification and inference in nonlinear difference-in-differences models. Econometrica 74: 431–497.CrossRefGoogle Scholar
  6. Blundell, R., and J.L. Powell. 2003. Endogeneity in nonparametric and semiparametric regression models. In Advances in economics and econometrics, theory and applications, eighth world congress, ed. M. Dewatripont, L.P. Hansen, and S.J. Turnovsky, vol. 2. Cambridge: Cambridge University Press.Google Scholar
  7. Brown, D.J, and R.L. Matzkin. 1998. Estimation of nonparametric functions in simultaneous equations models, with an application to consumer demand. Discussion paper no. 1175, Cowles Foundation, Yale University.Google Scholar
  8. Chen, X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of econometrics, ed. E. Leamer and J.J. Heckman, vol. 6. Amsterdam: North-Holland.Google Scholar
  9. Chernozhukov, V., and C. Hansen. 2005. An IV model of quantile treatment effects. Econometrica 73: 245–261.CrossRefGoogle Scholar
  10. Chernozhukov, V., G. Imbens, and W. Newey. 2007. Instrumental variable estimation of nonseparable models. Journal of Econometrics 139: 4–14.CrossRefGoogle Scholar
  11. Chesher, A. 2003. Identification in nonseparable models. Econometrica 71: 1404–1441.CrossRefGoogle Scholar
  12. Darolles, S., J.P. Florens, and E.Renault. 2003. Nonparametric instrumental regression. IDEI Working paper no. 228.Google Scholar
  13. Fan, Y., and Q. Li. 1996. Consistent model specification tests: Omitted variables and semiparametric functional forms. Econometrica 64: 4.CrossRefGoogle Scholar
  14. Florens, J.P. 2003. Inverse problems and structural econometrics: The example of instrumental variables. In Advances in economics and econometrics, theory and applications, ed. M. Dewatripont, L.P. Hansen, and S. Turnovsky, vol. 2. Cambridge: Cambridge University Press.Google Scholar
  15. Hall, P., and J.L. Horowitz. 2003. Nonparametric methods for inference in the presence of instrumental variables. Working paper no. 102/03, CMMD.Google Scholar
  16. Härdle, W., and O. Linton. 1994. Applied nonparametric methods. In Handbook of econometrics, ed. R.F. Engel and D.F. McFadden, vol. 4. Amsterdam: North-Holland.Google Scholar
  17. Heckman, J.J., and R. Robb. 1985. Alternative methods for evaluating the impact of interventions. In Longitudinal analysis of labor market data, ed. J.J. Heckman and B. Singer. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  18. Hong, Y., and H. White. 1995. Consistent specification testing via nonparametric series regression. Econometrica 63: 1133–1159.CrossRefGoogle Scholar
  19. Imbens, G.W., and W.K. Newey. 2003. Identification and estimation of triangular simultaneous equations models without additivity. Mimeo, Massachusetts Institute of Technology.Google Scholar
  20. Koenker, R.W. 2005. Quantile regression. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  21. Linton, O.B., and J.B. Nielsen. 1995. A kernel method of estimating structured nonparametric regression based on marginal integration. Biometrika 82: 93–100.CrossRefGoogle Scholar
  22. Matzkin, R.L. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of econometrics, ed. R.F. Engel and D.L. McFadden, vol. 4. Amsterdam: North-Holland.Google Scholar
  23. Matzkin, R.L. 1999. Nonparametric estimation of nonadditive random functions. Mimeo, Northwestern University.Google Scholar
  24. Matzkin, R.L. 2003. Nonparametric estimation of nonadditive random functions. Econometrica 71: 1339–1375.CrossRefGoogle Scholar
  25. Matzkin, R.L. 2004. Unobservable instruments. Mimeo, Northwestern University.Google Scholar
  26. Matzkin, R.L. 2005. Identification in nonparametric simultaneous equations. Mimeo, Northwestern University.Google Scholar
  27. Matzkin, R.L. 2006. Estimation of nonparametric simultaneous equations. Mimeo, Northwestern University.Google Scholar
  28. Matzkin, R.L. 2007a. Heterogenous choice. Invited lecture, ninth world congress of the econometric society. In Advanced in economics and econometrics, theory and applications, ninth world congress, ed. R. Blundell, W. Newey, and T. Persson, vol. 3. Cambridge: Cambridge University Press.Google Scholar
  29. Matzkin, R.L. 2007b. Nonparametric identification. In Handbook of econometrics, ed. E. Leamer and J.J. Heckman, vol. 6. Amsterdam: North-Holland.Google Scholar
  30. McFadden, D. 1974. Conditional logit analysis of qualitative choice behavior. In Frontiers in econometrics, ed. P. Zarembka. New York: Academic Press.Google Scholar
  31. Newey, W.K. 1994. Kernel estimation of partial means and a general variance estimator. Econometric Theory 10: 233–253.Google Scholar
  32. Newey, W., and J. Powell. 1989. Instrumental variables estimation of nonparametric models. Mimeo, Princeton University.Google Scholar
  33. Newey, W., and J. Powell. 2003. Instrumental variables estimation of nonparametric models. Econometrica 71: 1565–1578.CrossRefGoogle Scholar
  34. Newey, W.K., J.L. Powell, and F. Vella. 1999. Nonparametric estimation of triangular simultaneous equations models. Econometrica 67: 565–603.CrossRefGoogle Scholar
  35. Ng, S., and J. Pinkse. 1995. Nonparametric two-step estimation of unknown regression functions when the regressors and the regression error are not independent. Mimeo, CIREQ.Google Scholar
  36. Olley, G.S., and A. Pakes. 1996. The dynamics of productivity in the telecommunications equipment industry. Econometrica 64: 1263–1297.CrossRefGoogle Scholar
  37. Pagan, A., and A. Ullah. 1999. Nonparametric econometrics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  38. Pinkse, J. 2000. Nonparametric two-step regression estimation when regressors and errors are dependent. Canadian Journal of Statistics 28: 289–300.CrossRefGoogle Scholar
  39. Powell, J.L., J.H. Stock, and T.M. Stoker. 1989. Semiparametric estimation of index coefficients. Econometrica 51: 1403–1430.CrossRefGoogle Scholar
  40. Prakasa Rao, B.L.S. 1983. Nonparametric functional estimation. New York: Academic Press.Google Scholar
  41. Roehrig, C.S. 1988. Conditions for identification in nonparametric and parametric models. Econometrica 56: 433–447.CrossRefGoogle Scholar
  42. Wooldridge, J. 1992. Nonparametric regression tests based on an infinite dimensional least squares procedure. Econometric Theory 8: 435–451.CrossRefGoogle Scholar
  43. Yatchew, A.J. 1992. Nonparametric regression tests based on an infinite dimensional least squares procedure. Econometric Theory 8: 435–451.CrossRefGoogle Scholar

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© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Rosa L. Matzkin
    • 1
  1. 1.