Topic
Park test
About: Park test is a research topic. Over the lifetime, 166 publications have been published within this topic receiving 8498 citations.
Papers published on a yearly basis
Papers
TL;DR: In this paper, a simple test for heteroscedastic disturbances in a linear regression model is developed using the framework of the Lagrangian multiplier test, and the criterion is given as a readily computed function of the OLS residuals.
Abstract: A simple test for heteroscedastic disturbances in a linear regression model is developed using the framework of the Lagrangian multiplier test. For a wide range of heteroscedastic and random coefficient specifications, the criterion is given as a readily computed function of the OLS residuals. Some finite sample evidence is presented to supplement the general asymptotic properties of Lagrangian multiplier tests.
5,010 citations
1,049 citations
TL;DR: The purpose here is to provide appropriate diagnostic techniques to aid in an assessment of the validity of the usual assumption of homoscedasticity when little or no replication is present.
Abstract: SUMMARY For the usual regression model without replication, we provide a diagnostic test for heteroscedasticity based on the score statistic. A graphical procedure to complement the score test is also presented. Some key u'ords: Influence; Linear model; Residual; Score test. Diagnostic methods in linear regression are used to examine the appropriateness of assumptions underlying the modelling process and to locate unusual characteristics of the data that may influence conclusions. The recent literature on diagnostics is dominated by studies of methods for the detection of influential observations. Cook & Weisberg (1982) provide a review. Diagnostics for the relevance of specific assumptions, however, have not received the same degree of attention, even though these may be of equal importance. Our purpose here is to provide appropriate diagnostic techniques to aid in an assessment of the validity of the usual assumption of homoscedasticity when little or no replication is present. Available methods for studying this assumption include both graphical and nongraphical procedures. The usual graphical procedure consists of plotting the ordinary least squares residuals against fitted values or an explanatory variable. A megaphone shaped pattern is taken as evidence that the variance depends on the quantity plotted on tlle abscissa (Weisberg, 1980, Chapter 6). In ? 3 we suggest several ways in which this
900 citations
TL;DR: In this article, two exact tests for testing the hypothesis that the residuals from a least square regression are homoscedastic are presented, one parametric and using the F-statistic, and the other nonparametric and uses the number of peaks in the ordered sequence of unsigned residuals.
Abstract: Two exact tests are presented for testing the hypothesis that the residuals from a least squares regression are homoscedastic. The results can be used to test the hypothesis that a linear [ratio] model explains the relationship between variables as opposed to the alternative that the ratio [linear] specification is correct. The first test is parametric and uses the F-statistic. The second test is nonparametric and uses the number of peaks in the ordered sequence of unsigned residuals. In conclusion, the results of some experimental calculations of the powers of the tests are discussed.
720 citations
TL;DR: In this article, the authors derived the asymptotic distribution of their test under sequences of contiguous alternatives to the null hypothesis of homoscedasticity, and proposed a modification of the test which corrects this defect.
631 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2020 | 1 |
| 2018 | 2 |
| 2017 | 9 |
| 2016 | 5 |
| 2015 | 4 |
| 2014 | 10 |