Results 1  10
of
44
Large Sample Sieve Estimation of SemiNonparametric Models
 Handbook of Econometrics
, 2007
"... Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method o ..."
Abstract

Cited by 88 (14 self)
 Add to MetaCart
Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method of sieves provides one way to tackle such complexities by optimizing an empirical criterion function over a sequence of approximating parameter spaces, called sieves, which are significantly less complex than the original parameter space. With different choices of criteria and sieves, the method of sieves is very flexible in estimating complicated econometric models. For example, it can simultaneously estimate the parametric and nonparametric components in seminonparametric models with or without constraints. It can easily incorporate prior information, often derived from economic theory, such as monotonicity, convexity, additivity, multiplicity, exclusion and nonnegativity. This chapter describes estimation of seminonparametric econometric models via the method of sieves. We present some general results on the large sample properties of the sieve estimates, including consistency of the sieve extremum estimates, convergence rates of the sieve Mestimates, pointwise normality of series estimates of regression functions, rootn asymptotic normality and efficiency of sieve estimates of smooth functionals of infinite dimensional parameters. Examples are used to illustrate the general results.
Tests of conditional predictive ability
 Econometrica
, 2006
"... We argue that the current framework for predictive ability testing (e.g.,West, 1996) is not necessarily useful for realtime forecast selection, i.e., for assessing which of two competing forecasting methods will perform better in the future. We propose an alternative framework for outofsample com ..."
Abstract

Cited by 46 (1 self)
 Add to MetaCart
We argue that the current framework for predictive ability testing (e.g.,West, 1996) is not necessarily useful for realtime forecast selection, i.e., for assessing which of two competing forecasting methods will perform better in the future. We propose an alternative framework for outofsample comparison of predictive ability which delivers more practically relevant conclusions. Our approach is based on inference about conditional expectations of forecasts and forecast errors rather than the unconditional expectations that are the focus of the existing literature. We capture important determinants of forecast performance that are neglected in the existing literature by evaluating what we call the forecasting method (the model and the parameter estimation procedure), rather than just the forecasting model. Compared to previous approaches, our tests are valid under more general data assumptions (heterogeneity rather than stationarity) and estimation methods, and they can handle comparison of both nested and nonnested models, which is not currently possible. To illustrate the usefulness of the proposed tests, we compare the forecast performance of three leading parameterreduction methods for macroeconomic forecasting using a large number of predictors: a sequential model selection approach,
Testing for Linearity
 Journal of Economic Surveys
, 1999
"... Abstract. The problem of testing for linearity and the number of regimes in the context of selfexciting threshold autoregressive (SETAR) models is reviewed. We describe leastsquares methods of estimation and inference. The primary complication is that the testing problem is nonstandard, due to th ..."
Abstract

Cited by 35 (1 self)
 Add to MetaCart
Abstract. The problem of testing for linearity and the number of regimes in the context of selfexciting threshold autoregressive (SETAR) models is reviewed. We describe leastsquares methods of estimation and inference. The primary complication is that the testing problem is nonstandard, due to the presence of parameters which are only defined under the alternative, so the asymptotic distribution of the test statistics is nonstandard. Simulation methods to calculate asymptotic and bootstrap distributions are presented. As the sampling distributions are quite sensitive to conditional heteroskedasticity in the error, careful modeling of the conditional variance is necessary for accurate inference on the conditional mean. We illustrate these methods with two applications Ð annual sunspot means and monthly U.S. industrial production. We find that annual sunspots and monthly industrial production are SETAR(2) processes. Keywords. SETAR models; Thresholds; Nonstandard asymptotic theory; Bootstrap
Improved Rates and Asymptotic Normality for Nonparametric Neural Network Estimators
, 1997
"... Barron (1993) obtained a deterministic approximation rate (in L2norm) of rm for a class of single hid den layer feedforward artificial neural networks (ANN) with r hidden units and sigmoid activation func tions when the target function satisfies certain smoothness conditions. Hornik, Stinchcom ..."
Abstract

Cited by 25 (8 self)
 Add to MetaCart
Barron (1993) obtained a deterministic approximation rate (in L2norm) of rm for a class of single hid den layer feedforward artificial neural networks (ANN) with r hidden units and sigmoid activation func tions when the target function satisfies certain smoothness conditions. Hornik, Stinchcombe, White, and Auer (HSWA, 1994) extended Barron's result to a class of ANNs with possibly nonsigmoid activation approximating the target function and its derivatives simultaneously. Recently Makovoz (1996) obtained an improved degree of approximation rate ro+/a for Barron's ANNs with sigmoid activation func tion where d is the dimension of the domain of the target function.
Predictive density evaluation
, 2005
"... This chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed, and a variety of different specification and model evaluation tests due to various ..."
Abstract

Cited by 25 (1 self)
 Add to MetaCart
This chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed, and a variety of different specification and model evaluation tests due to various
Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes
, 2005
"... We introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for outofsample n ..."
Abstract

Cited by 14 (5 self)
 Add to MetaCart
We introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for outofsample nonlinear Granger causality, and in the other we outline a test for selecting amongst multiple alternative forecasting models, all of which are possibly misspecified. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric bootstrap due to Kilian (1999); within the context of encompassing and predictive accuracy tests. In the empirical illustration, it is found that unemployment has nonlinear marginal predictive content for inflation.
Approximate nonlinear forecasting methods
 Handbook of Economic Forecasting
, 2006
"... We review key aspects of forecasting using nonlinear models. Because economic models are typically misspecified, the resulting forecasts provide only an approximation to the best possible forecast. Although it is in principle possible to obtain superior approximations to the optimal forecast using n ..."
Abstract

Cited by 12 (6 self)
 Add to MetaCart
We review key aspects of forecasting using nonlinear models. Because economic models are typically misspecified, the resulting forecasts provide only an approximation to the best possible forecast. Although it is in principle possible to obtain superior approximations to the optimal forecast using nonlinear methods, there are some potentially serious practical challenges. Primary among these are computational difficulties, the dangers of overfit, and potential difficulties of interpretation. In this chapter we discuss these issues in detail. Then we propose and illustrate the use of a new family of methods (QuickNet) that achieves the benefits of using a forecasting model that is nonlinear in the predictors while avoiding or mitigating the other challenges to the use of nonlinear forecasting methods. 1.
Goodnessoffit tests for Markovian time series models
, 2005
"... New goodnessoffit tests for Markovian models in time series analysis are developed which are based on the difference between a fully nonparametric estimate of the onestep transition distribution function of the observed process and that of the model class postulated under the null hypothesis. The ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
New goodnessoffit tests for Markovian models in time series analysis are developed which are based on the difference between a fully nonparametric estimate of the onestep transition distribution function of the observed process and that of the model class postulated under the null hypothesis. The model specification under the null allows for Markovian models the transition mechanism of which depends on an unknown vector of parameters and an unspecified distribution of i.i.d. innovations. Asymptotic properties of the test statistic are derived and the critical values of the test are found using appropriate bootstrap schemes. General properties of the bootstrap for Markovian processes are derived. A new central limit theorem for triangular arrays of weakly dependent random variables is obtained. For the proof of stochastic equicontinuity of multidimensional empirical processes, we use a simple approach based on an anisotropic tiling of the space. The finite sample behavior of the test proposed is illustrated by some numerical examples and a real data application is given. 2000 Mathematics Subject Classification. Primary 62M02; secondary 62G09, 62G10.
Consistent Specification Tests for Semiparametric/Nonparametric Models Based on Series . . .
 JOURNAL OF ECONOMETRICS
, 2003
"... This paper considers the problem of consistent model specification tests using series estimation methods. The null models we consider in this paper all contain some nonparametric components. A leading case we consider is to test for an additive partially linear model. The null distribution of the ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
This paper considers the problem of consistent model specification tests using series estimation methods. The null models we consider in this paper all contain some nonparametric components. A leading case we consider is to test for an additive partially linear model. The null distribution of the test statistic is derived using a central limit theorem for Hilbert valued random arrays. The test statistic is shown to be able to detect local alternatives that approach the null models at the order of O p (n 1/2 ). We suggest to use the wild bootstrap method to approximate the critical values of the test. A small Monte Carlo simulation is reported to examine the finite sample performance of the proposed test. We also show