A simple consistent test of conditional symmetry in symmetrically trimmed tobit models
Tao Chen and
Gautam Tripathi
Journal of Econometrics, 2017, vol. 198, issue 1, 29-40
Abstract:
We propose a “weighted and sample-size adjusted” Kolmogorov–Smirnov type statistic to test the assumption of conditional symmetry maintained in the symmetrically trimmed least-squares (STLS) approach of Powell (1986b), which is widely used to estimate censored or truncated regression models without making distributional assumptions. Our statistic is consistent and does not require any nonparametric smoothing, although we test the validity of a conditional feature. We also propose a bootstrap procedure to obtain the p-values and critical values that are required to carry out the test in practical applications. Results from a simulation study suggest that our test can work very well even in small to moderately sized samples. As an empirical illustration, we apply our test to two datasets that have been used in the literature to estimate censored regression models using Powell’s STLS approach, to check whether the assumption of conditional symmetry is supported by these datasets.
Keywords: Conditional symmetry; Consistent test; Symmetric trimming; Tobit models (search for similar items in EconPapers)
JEL-codes: C12 C14 C24 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407617300118
Full text for ScienceDirect subscribers only
Related works:
Working Paper: A simple consistent test of conditional symmetry in symmetrically trimmed tobit models (2014) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:198:y:2017:i:1:p:29-40
DOI: 10.1016/j.jeconom.2016.12.003
Access Statistics for this article
Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson
More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().