A Test for Functional Form Against Nonparametric Alternatives

1992 ◽  
Vol 8 (4) ◽  
pp. 452-475 ◽  
Author(s):  
Jeffrey M. Wooldridge
Keyword(s):  
Alternative Model ◽  
Regression Models ◽  
Functional Form ◽  
Parametric Model ◽  
Finite Sample ◽  
Test Statistic ◽  
Sieve Estimation ◽  
Finite Sample Properties ◽  
Standard Normal ◽  
Nonparametric Alternatives

A test for neglected nonlinearities in regression models is proposed. The test is of the Davidson-MacKinnon type against an increasingly rich set of non-nested alternatives, and is based on sieve estimation of the alternative model. For the case of a linear parametric model, the test statistic is shown to be asymptotically standard normal under the null, while rejecting with probability going to one if the linear model is misspecified. A small simulation study suggests that the test has adequate finite sample properties, but one must guard against over fitting the nonparametric alternative.

A Review of Nonparametric Alternatives To Analysis of Covariance

1985 ◽  
Vol 9 (1) ◽  
pp. 51-83 ◽  
Author(s):  
Stephen F. Olejnik ◽  
James Algina
Keyword(s):  
Analysis Of Covariance ◽  
Nonparametric Alternatives

Limit Theory for Forecasts of Extreme Distortion Risk Measures and Expectiles*

2019 ◽  
Author(s):  
Yannick Hoga
Keyword(s):  
Value At Risk ◽  
Risk Measures ◽  
Limit Theory ◽  
Extreme Risk ◽  
Wide Range ◽  
Scale Models ◽  
Adequate Coverage ◽  
Nonparametric Alternatives ◽  
Do So ◽  
Distortion Risk Measures

Abstract We develop central limit theory for tail risk forecasts in general location–scale models. We do so for a wide range of risk measures, viz. distortion risk measures (DRMs) and expectiles. Two popular members of the class of DRMs are the Value-at-Risk and the Expected Shortfall. The forecasts we consider are motivated by a Pareto-type tail assumption for the innovations and allow for extrapolation beyond the range of available observations. Simulations reveal adequate coverage of the forecast intervals derived from the limit theory. An empirical application demonstrates that our estimators outperform nonparametric alternatives when forecasting extreme risk in sufficiently large samples.

Post Hoc Procedures for Some K-Sample Nonparametric Tests for Scale

1978 ◽  
Vol 3 (3) ◽  
pp. 265-282
Author(s):  
Douglas A. Penfield ◽  
Stephen L. Koffler
Keyword(s):  
Nonparametric Tests ◽  
Population Variability ◽  
Multiple Comparison ◽  
Chi Square ◽  
Bartlett Test ◽  
Nonparametric Alternatives ◽  
Post Hoc ◽  
Multiple Comparison Procedures

Post hoc multiple comparison procedures useful in assessing differences in population variability are formulated for three nonparametric alternatives to the parametric Bartlett test. The three nonparametric tests are the generalized Puri K-sample extensions of the Siegel-Tukey, Mood, and Klotz tests. Theory surrounding the development of these post hoc procedures is outlined and is based upon the chi-square analog to Scheffé’s theorem. An example illustrating an application of the methodology is presented.

scholarly journals Choice Inconsistencies among the Elderly: Evidence from Plan Choice in the Medicare Part D Program: Reply

2016 ◽  
Vol 106 (12) ◽  
pp. 3962-3987 ◽  
Author(s):  
Jason Abaluck ◽  
Jonathan Gruber
Keyword(s):  
Fixed Effects ◽  
Goodness Of Fit ◽  
The Elderly ◽  
Parametric Models ◽  
Part D ◽  
Plan Choice ◽  
Out Of Sample ◽  
Nonparametric Alternatives ◽  
Expected Utility Model

We explore the in- and out-of-sample robustness of tests for choice inconsistencies based on parameter restrictions in parametric models, focusing on tests proposed by Ketcham, Kuminoff, and Powers (2016). We argue that their nonparametric alternatives are inherently conservative with respect to detecting mistakes. We then show that our parametric model is robust to KKP’s suggested specification checks, and that comprehensive goodness of fit measures perform better with our model than the expected utility model. Finally, we explore the robustness of our 2011 results to alternative normative assumptions highlighting the role of brand fixed effects and unobservable characteristics. (JEL D12, H51, I13, I18, J14)

Variance heterogeneity, transformations, and models of species abundance: a cautionary tale

2004 ◽  
Vol 61 (7) ◽  
pp. 1294-1302 ◽  
Author(s):  
Brian H McArdle ◽  
Marti J Anderson
Keyword(s):  
Type I Error ◽  
Species Abundance ◽  
Ecological Systems ◽  
Type I ◽  
Data Set ◽  
Bootstrap Tests ◽  
Inflated Type ◽  
The Mean ◽  
Nonparametric Alternatives ◽  
Mean Variance

Ecological systems have intrinsic heterogeneity. Counts of abundances of species often show heterogeneity of variances among observational groups or populations. This is most often dealt with by using a transformation of the data followed by a traditional statistical analysis that requires homogeneity. Such an approach is extremely useful when the mean–variance relationship is consistent across the data set. In some situations, however, the mean–variance relationship does not stay constant, e.g., the degree of spatial aggregation of organisms can change in space and time. In these cases, transforming the data to "fix" the problem of heterogeneity can result in apparently grossly inflated type I error. The use of a transformation alters the model under test and also has an important effect on the spatial scale of the hypothesis. The use of nonparametric alternatives, such as permutation or bootstrap tests, does not solve this problem. Explicit models of these kinds of distributional changes, where they occur, are necessary.

Sign in / Sign up

Export Citation Format

Share Document