Some of the research papers presented at an national conference held in 2005 in Beijing that was themed 'Nonlinear and Nonparametric Methods in Econometrics' are discussed. Su and Jin's paper, 'Profile quasi-maximum likelihood estimation of spatial autoregressive models', proposes estimating a partially linear spatial autoregressive model using a quasi-maximum likelihood estimation method. Kelejian and Prucha's paper, 'Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances', considers the problem of estimating a spatial model containing spatial lags in the dependent variables, exogenous variables and the disturbance terms. Chen, Fan, Pouzo and Ying's paper, 'Estimation and model se...
Non- or semiparametric estimation and lag selection methods are proposed for three seasonal nonlinea...
The papers appearing in this special issue of Papers in Regional Science, which is devoted to spatia...
This dissertation consists of three chapters that focus on the nonparametric method on time-varying ...
E ¢ cient semiparametric and parametric estimates are developed for a spatial autoregressive model, ...
Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, ...
We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are par...
We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are par...
This paper considers a flexible semiparametric spatial autoregressive (mixed-regressive) model in wh...
Developments in the vast and growing literatures on nonparametric and semiparametric statistical est...
This dissertation consists of two essays on the estimation and inference for spatial economic models...
We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are par...
Large spatial time-series data with complex structures collected at irregularly spaced sampling loca...
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appe...
The contributors to this volume include many of the distinguished researchers in this area. Many of ...
Non- or semiparametric estimation and lag selection methods are proposed for three seasonal nonlinea...
The papers appearing in this special issue of Papers in Regional Science, which is devoted to spatia...
This dissertation consists of three chapters that focus on the nonparametric method on time-varying ...
E ¢ cient semiparametric and parametric estimates are developed for a spatial autoregressive model, ...
Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, ...
We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are par...
We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are par...
This paper considers a flexible semiparametric spatial autoregressive (mixed-regressive) model in wh...
Developments in the vast and growing literatures on nonparametric and semiparametric statistical est...
This dissertation consists of two essays on the estimation and inference for spatial economic models...
We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are par...
Large spatial time-series data with complex structures collected at irregularly spaced sampling loca...
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appe...
The contributors to this volume include many of the distinguished researchers in this area. Many of ...
Non- or semiparametric estimation and lag selection methods are proposed for three seasonal nonlinea...
The papers appearing in this special issue of Papers in Regional Science, which is devoted to spatia...
This dissertation consists of three chapters that focus on the nonparametric method on time-varying ...