Results 1  10
of
116
Generalized Likelihood Ratio Statistics And Wilks Phenomenon
, 2000
"... this paper. We introduce the generalized likelihood statistics to overcome the drawbacks of nonparametric maximum likelihood ratio statistics. New Wilks phenomenon is unveiled. We demonstrate that a class of the generalized likelihood statistics based on some appropriate nonparametric estimators are ..."
Abstract

Cited by 78 (22 self)
 Add to MetaCart
this paper. We introduce the generalized likelihood statistics to overcome the drawbacks of nonparametric maximum likelihood ratio statistics. New Wilks phenomenon is unveiled. We demonstrate that a class of the generalized likelihood statistics based on some appropriate nonparametric estimators are asymptotically distribution free and follow
The bootstrap
 In Handbook of Econometrics
, 2001
"... The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one’s data. It amounts to treating the data as if they were the population for the purpose of evaluating the distribution of interest. Under mild regularity conditions, the bootstrap yields an a ..."
Abstract

Cited by 75 (1 self)
 Add to MetaCart
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one’s data. It amounts to treating the data as if they were the population for the purpose of evaluating the distribution of interest. Under mild regularity conditions, the bootstrap yields an approximation to the distribution of an estimator or test statistic that is at least as accurate as the
Consistent Specification Testing With Nuisance Parameters Present Only Under The Alternative
, 1995
"... . The nonparametric and the nuisance parameter approaches to consistently testing statistical models are both attempts to estimate topological measures of distance between a parametric and a nonparametric fit, and neither dominates in experiments. This topological unification allows us to greatly ex ..."
Abstract

Cited by 55 (10 self)
 Add to MetaCart
. The nonparametric and the nuisance parameter approaches to consistently testing statistical models are both attempts to estimate topological measures of distance between a parametric and a nonparametric fit, and neither dominates in experiments. This topological unification allows us to greatly extend the nuisance parameter approach. How and why the nuisance parameter approach works and how it can be extended bears closely on recent developments in artificial neural networks. Statistical content is provided by viewing specification tests with nuisance parameters as tests of hypotheses about Banachvalued random elements and applying the Banach Central Limit Theorem and Law of Iterated Logarithm, leading to simple procedures that can be used as a guide to when computationally more elaborate procedures may be warranted. 1. Introduction In testing whether or not a parametric statistical model is correctly specified, there are a number of apparently distinct approaches one might take. T...
Bootstrap Methods in Econometrics: Theory and Numerical Performance
 Eds.), Advances in Economics and Econometrics: Theory and Applications, Seventh World Congress, Vol. III
, 1997
"... 1. ..."
Consistent Model Specification Tests
 Journal of Econometrics
, 1982
"... This paper reviews the literature on tests for the correct specification of the functional form of parametric conditional expectation and conditional distribution models. In particular I will discuss various versions of the Integrated Conditional Moment (ICM) test and the ideas behind them. 1 ..."
Abstract

Cited by 49 (10 self)
 Add to MetaCart
This paper reviews the literature on tests for the correct specification of the functional form of parametric conditional expectation and conditional distribution models. In particular I will discuss various versions of the Integrated Conditional Moment (ICM) test and the ideas behind them. 1
Semiparametric Estimates and Tests of BaseIndependent Equivalence
 Scales?"Journal of Econometrics
, 1999
"... Previous papers estimate baseindependent equivalence scales and test baseindependence using strict parametric assumptions on Engel curves and equivalence scale functions. These parametric tests reject the hypothesis of base independence. I construct a semiparametric estimator of a household equiva ..."
Abstract

Cited by 32 (6 self)
 Add to MetaCart
Previous papers estimate baseindependent equivalence scales and test baseindependence using strict parametric assumptions on Engel curves and equivalence scale functions. These parametric tests reject the hypothesis of base independence. I construct a semiparametric estimator of a household equivalence scale under the assumption of base independence without putting any further restrictions on the shape of household Engel curves. This estimator uses crossequation restrictions on a system of estimated nonparametric engel curves to identify equivalence scale parameters. I test the hypothesis of base independence against a fully nonparametric alternative and find that preferences are consistent with the existence of a baseindependent equivalence scale for some
Linearity Testing using Local Polynomial Approximation
 Journal of Statistical Planning and Inference
, 1996
"... this paper we examine its potential in linearity testing. For example it is convenient to look at derivatives of nonparametric estimates in this framework, and one can construct new tests of linearity exploiting that the first order derivative is a constant, and the second order derivative is zero f ..."
Abstract

Cited by 28 (4 self)
 Add to MetaCart
this paper we examine its potential in linearity testing. For example it is convenient to look at derivatives of nonparametric estimates in this framework, and one can construct new tests of linearity exploiting that the first order derivative is a constant, and the second order derivative is zero for a linear model. It is also easier to look at the transition between parametric and nonparametric modeling. This transition is intimately connected to the choice of bandwidth. Choosing the bandwidth is a very important aspect of nonparametric linearity testing, but it was virtually neglected in Hjellvik and Tjøstheim (1995,1996). In the present paper it is studied in some detail and both data driven and theoretically determined bandwidths are investigated. In contrast to Hjellvik and Tjøstheim (1995,1996) we present a fair amount of asymptotic theory. One reason for this is that the asymptotic theory yields useful input to the problem of choosing the bandwidth. Also the asymptotic theory is of interest in itself, and in Appendix 1 we extend some results on degenerate Ustatistics, which has hitherto only been proved for iid
Asymptotic Distribution Theory for Nonparametric Entropy Measures of Serial Dependence
 Measures of Serial Dependence” Unpublished Manuscript
, 2004
"... Entropy is a classical statistical concept with appealing properties. Establishing asymptotic distribution theory for smoothed nonparametric entropy measures of dependence has so far proved challenging. In this paper, we develop an asymptotic theory for a class of kernelbased smoothed nonparametric ..."
Abstract

Cited by 25 (1 self)
 Add to MetaCart
Entropy is a classical statistical concept with appealing properties. Establishing asymptotic distribution theory for smoothed nonparametric entropy measures of dependence has so far proved challenging. In this paper, we develop an asymptotic theory for a class of kernelbased smoothed nonparametric entropy measures of serial dependence in a time series context. We use this theory to derive the limiting distribution of Granger and Lins (1994) normalized entropy measure of serial dependence, which was previously not available in the literature. We also apply our theory to construct a new entropybased test for serial dependence, providing an alternative to Robinsons (1991) approach. To obtain accurate inferences, we propose and justify a consistent smoothed bootstrap procedure. The naive bootstrap is not consistent for our test. Our test is useful in, for example, testing the random walk hypothesis, evaluating density forecasts, and identifying important lags of a time series. It is asymptotically locally more powerful than Robinsons (1991) test, as is confirmed in our simulation. An application to the daily S&P 500 stock price index illustrates our approach.
Consistent Bootstrap Tests Of Parametric Regression Functions
, 2000
"... This paper introduces specification tests of parametric meanregression models. The null hypothesis of interest is that the parametric regression function is correctly specified. The proposed tests are generalizations of the KolmogorovSmirnov and Cramervon Mises tests to the regression framework. ..."
Abstract

Cited by 20 (7 self)
 Add to MetaCart
This paper introduces specification tests of parametric meanregression models. The null hypothesis of interest is that the parametric regression function is correctly specified. The proposed tests are generalizations of the KolmogorovSmirnov and Cramervon Mises tests to the regression framework. They are consistent against all alternatives to the null hypothesis, powerful against 1= p n local alternatives, not dependent on any smoothing parameters and simple to compute. A wildbootstrap procedure is suggested to obtain critical values for the tests and is justified asymptotically. A small scale Monte Carlo experiment shows that our tests (especially Cramervon Mises test) have outstanding small sample performance compared to some of the existing tests.
GoodnessofFit Tests for Parametric Regression Models
 JOUR. AMERI. STATIST. ASSOC
, 2001
"... Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from parametric ts is negligible by using the adaptive Neyman test and other methods. The testing procedures ..."
Abstract

Cited by 19 (5 self)
 Add to MetaCart
Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from parametric ts is negligible by using the adaptive Neyman test and other methods. The testing procedures formalize the traditional model diagnostic tools based on residual plots. We examine the rates of contiguous alternatives that can be detected consistently by the adaptive Neyman test. Applications of the procedures to the partially linear models are thoroughly discussed. Our simulation studies show that the new testing procedures are indeed powerful and omnibus. The power of the proposed tests is comparable to the Ftest statistic even in the situations where F test is known to be suitable and can be far more powerful than the Ftest statistic in other situations. An application to testing linear models versus additive models are discussed.