This paper proposes a new set of transformed polynomial functions that provide a flexible setting for nonlinear autoregressive modeling of the conditional mean while at the same time ensuring the strict stationarity, ergodicity, fading memory and existence of moments of the implied stochastic...

A simple graphical approach to presenting results from nonlinear regression models is described. In the face of multiple covariates, 'partial mean' plots may be unattractive. The approach here is portable to a variety of settings and can be tailored to the specific application at hand. A simple...

This paper analyzes the determinants of regional economic growth in the European Union. The nonparametric approach adopted allows us not only to uncover their relevance but also to determine whether or not they exert a linear in uence. We obtain evidence of a nonlinear relationship between...

In this paper, we consider some specification testing problems in nonlinear time series models with nonstationarity. We propose using a nonparametric kernel test for specifying whether the regression function is of a known parametric nonlinear form. The power function of the proposed...

The present study investigates the linear and nonlinear causality between spot and future returns of four notional indices maintained by Multi Commodity Exchange of India (MCX). The data covers two periods June 2005 – June 2008 and July 2008 - November 2011. Apart from the conventional linear...

This paper explores the uniformity of inference for parameters of interest in nonlinear models with endogeneity. The notion of uniformity is fundamental in these models because due to potential endogeneity, the behavior of standard estimators of these parameters is shown to vary with where they...

This paper considers a nonlinear time series model associated with both nonstationarity and endogeneity. The proposed model is then estimated by a nonparametric series method. An asymptotic theory is established in both point-wise and the space metric sense for the estimator. The Monte Carlo...

This paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV) estimation and inference on a structural function h₀ and its functionals. First, we derive sup-norm convergence rates for computationally simple sieve NPIV (series 2SLS)...

In order to discuss nonlinear, it is necessary to know linear regressive as a priori. Without simple regression as the starting point, it would be difficult to understand nonlinear regression. In words, in order to understand the curve and the behavior of a curve, it is necessary to known a...

This paper considers identification and inference of a general latent nonlinear model using two samples, where a covariate contains arbitrary measurement errors in both samples, and neither sample contains an accurate measurement of the corresponding true variable. The primary sample consists of...