This paper proposes a new GMM estimator for spatial regression models with moving average errors. Monte Carlo results are given indicating that the GMM estimates are close to expectation and robust to non-normality, and the Bootstrap method is suggested as a way of testing the significance of the moving average parameter. The estimator is applied in a model of English real estate prices, in whi...

Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is...

Here, a new method is recommended to characterize continuous distribution class, named the generalized alpha exponent power family of distributions (GAEPFDs). A particular sub-model presented for exemplifying objective. The basic statistical properties, such as ordinary moments, probability weighted mode, quantile, order statistics, entropy measures, and moment generating functions, etc., were ...

‎consider an estimation problem in a one-parameter non-regular distribution when both endpoints of the support depend on a single parameter‎. ‎in this paper‎, ‎we give sufficient conditions for a generalized bayes estimator of a parametric function to be admissible‎. ‎some examples are given‎. ‎

This paper studies the non-Gaussian pseudo maximum likelihood (PML) estimation of a spatial autoregressive (SAR) model with SAR disturbances. If weights matrix $M_{n}$ for disturbances is normalized to have row sums equal 1 or reduces no process disturbances, PML estimator (NGPMLE) parameters except intercept term and variance $\sigma _{0}^{2}$ independent identically distributed (i.i.d.) innov...

We propose a new finite sample corrected variance estimator for the linear generalized method of moments (GMM) including one-step, two-step, and iterated estimators. Our formula also corrects over-identification bias in estimation on top commonly used correction Windmeijer (2005), which from estimating efficient weight matrix, so is doubly corrected. An important feature proposed double that it...

"Estimation of the value of a regression function at a point of continuity using a kernel-type estimator is discussed and improvements of the technique by a generalized jackknife estimator are presented. It is shown that the generalized jackknife technique produces estimators with faster bias rates. In a small example it is investigated, if the generalized jackknife method works for all choices...