An inference method, called latent backfitting is proposed. It appears well suited for econometric models where the structural relationships of interest define the observed endogenous variables as a known function of unobserved state variables and unknown parameters. This nonlinear state space...

An important class of structural econometric models (nonlinear rational expectations, option pricing, auction models, ...) characterize observable variables as highly nonlinear transforma- tions of some latent variables. These transformations are one-to-one, but they depend on the unknown...

In this paper, we survey some of the recent nonparametric estimation methods which were developed to price derivative contracts. We focus on equity options and start with a so-called model-free approach which involves very little financial theory. Next we discuss nonparametric and...

We develop a novel approach to build checks of parametric regression models when many regressors are present, based on a class of sufficiently rich semiparametric alternatives, namely single-index models. We propose an omnibus test based on the kernel method that performs against a sequence of...

The problem of approximating a general regression function m(x) = E (Y IX = x) is addressed. As in the case of the c1assical L2-type projection pursuit regression considered by Hall (1989), we propose to approximate m(x) through a regression of Y given an index, that is a unidimensional...

This paper considers ML estimation of a diffusion process observed discretely. Since the exact loglikelihood is generally not available, it must be approximated. We review the most effcient approaches in the literature, and point to some drawbacks. We propose to approximate the loglikelihood...

We consider testing the significance of a subset of covariates in a nonparamet- ric regression. These covariates can be continuous and/or discrete. We propose a new kernel-based test that smoothes only over the covariates appearing under the null hypothesis, so that the curse of dimensionality...

We develop a structural econometric model to elicit household-specific expectations about future financial asset returns and risk attitudes by using data on observed portfolio holdings and self-assessed willingness to bear financial risk. Our framework assumes that household portfolios are...

We address the issue of lack-of-fit testing for a parametric quantile regression. We propose a simple test that involves one-dimensional kernel smoothing, so that the rate at which it detects local alternatives is independent of the number of covariates. The test has asymptotically gaussian...

The problem of approximating a general regression function m(x) = E (Y IX = x) is addressed. As in the case of the c1assical L2-type projection pursuit regression considered by Hall (1989), we propose to approximate m(x) through a regression of Y given an index, that is a unidimensional...