A nonparametric bootstrap test of conditional distributions uri icon

abstract

  • This paper proposes a bootstrap test for the correct specification of parametric conditional distributions. It extends Zheng's test (Zheng, 2000, Econometric Theory 16, 667-691) to allow for discrete dependent variables and for mixed discrete and continuous conditional variables. We establish the asymptotic null distribution of the test statistic with data-driven stochastic smoothing parameters. By smoothing both the discrete and continuous variables via the method of cross-validation, our test has the advantage of automatically removing irrelevant variables from the estimate of the conditional density function and, as a consequence, enjoys substantial power gains in finite samples, as confirmed by our simulation results. The simulation results also reveal that the bootstrap test successfully over-comes the size distortion problem associated with Zheng's test. 2006 Cambridge University Press.

published proceedings

  • ECONOMETRIC THEORY

altmetric score

  • 2.5

author list (cited authors)

  • Fan, Y., Li, Q. i., & Min, I.

citation count

  • 17

complete list of authors

  • Fan, Yanqin||Li, Qi||Min, Insik

publication date

  • August 2006