In this paper, we propose a localized neural network (LNN) model and then develop the LNN based estimation and inferential procedures for dependent data in both cases with quantitative/qualitative outcomes. We explore the use of identification restrictions from a nonparametric regression...

In this paper, we propose three new predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series; and the multi-step time-varying coefficient predictive...

In this paper, a semiparametric single-index model is investigated. The link function is allowed to be unbounded and has unbounded support that answers a pending issue in the literature. Meanwhile, the link function is treated as a point in an infinitely many dimensional function space which...

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...

Estimation theory in a nonstationary environment has been very popular in recent years. Existing studies focus on nonstationarity in parametric linear, parametric nonlinear and nonparametric nonlinear models. In this paper, we consider a partially linear model of nonstationary time series, in...

In this paper, we consider both estimation and testing problems in a nonlinear time series model with nonstationarity. A nonparametric estimation method is proposed to estimate a sequence of nonparametric “distance functions”. We also propose a test statistic to test whether the regression...

This paper establishes several results for uniform convergence of nonparametric kernel density and regression estimates for the case where the time series regressors concerned are nonstationary null-recurrent Markov chains. Under suitable conditions, certain rates of convergence are also...

In this paper, we consider a semiparametric time series regression model and establish a set of identification conditions such that the model under discussion is both identifiable and estimable. We then discuss how to estimate a sequence of local alternative functions nonparametrically when the...

This paper proposes a simple and improved nonparametric unit-root test. An asymptotic distribution of the proposed test is established. Finite sample comparisons with an existing nonparametric test are discussed. Some issues about possible extensions are outlined

This paper introduces an alternative testing procedure to test the distribution of the error term in the Autoregressive Conditional Duration (ACD) class of models. In these models, the error term is normally interpreted as the standardized duration by which its probability distribution may have...