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Asymptotic Properties Of Estimators In Non Linear Regression Models With Autoregressive Disturbance Terms
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Book Synopsis Asymptotic Properties of Estimators in Non-linear Regression Models with Autoregressive Disturbance Terms by : Friedrich Schmid
Download or read book Asymptotic Properties of Estimators in Non-linear Regression Models with Autoregressive Disturbance Terms written by Friedrich Schmid and published by . This book was released on 1982 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic properties of estimators in non-linear regression models with autoregressive disturbance terms by : Friedrich Schmid
Download or read book Asymptotic properties of estimators in non-linear regression models with autoregressive disturbance terms written by Friedrich Schmid and published by . This book was released on 1982 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of S Estimators for Nonlinear Regression Models with de by : Shinichi Sakata
Download or read book Asymptotic Properties of S Estimators for Nonlinear Regression Models with de written by Shinichi Sakata and published by . This book was released on 1994 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Maximum Likelihood Estimators in a Nonlinear Regression Model with Unknown Parameters in the Disturbance Covariance Matrix by : R. D. H. Heijmans
Download or read book Asymptotic Properties of Maximum Likelihood Estimators in a Nonlinear Regression Model with Unknown Parameters in the Disturbance Covariance Matrix written by R. D. H. Heijmans and published by . This book was released on 1977 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent Nor Identically Distributed by : R. D. H. Heijmans
Download or read book Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent Nor Identically Distributed written by R. D. H. Heijmans and published by . This book was released on 1982 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent for Identically Distributed by : R. D. H. Heijmans
Download or read book Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent for Identically Distributed written by R. D. H. Heijmans and published by . This book was released on 1982 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors by : Hermanus Josephus Bierens
Download or read book Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors written by Hermanus Josephus Bierens and published by . This book was released on 1977 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Estimators for the Linear Panel Regression Model with Individual Effects and Serially Correlated Errors by : Badi H. Baltagi
Download or read book Asymptotic Properties of Estimators for the Linear Panel Regression Model with Individual Effects and Serially Correlated Errors written by Badi H. Baltagi and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies the asymptotic properties of standard panel data estimators in a simple panel regression model with error component disturbances. Both the regressor and the remainder disturbance term are assumed to be autoregressive and possibly non-stationary. Asymptotic distributions are derived for the standard panel data estimators including ordinary least squares, fixed effects, first-difference, and generalized least squares (GLS) estimators when both T and n are large. We show that all the estimators have asymptotic normal distributions and have different convergence rates dependent on the non-stationarity of the regressors and the remainder disturbances. We show using Monte Carlo experiments that the loss in efficiency of the OLS, FE and FD estimators relative to true GLS can be substantial.
Book Synopsis Asymptotic Properties of Nonlinear Least Squares Estimators in a Replicated Time Series Model by : Jeremy Sin-hing Wu
Download or read book Asymptotic Properties of Nonlinear Least Squares Estimators in a Replicated Time Series Model written by Jeremy Sin-hing Wu and published by . This book was released on 1982 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Some Estimators in Moving Average Models by : Stanford University. Department of Statistics
Download or read book Asymptotic Properties of Some Estimators in Moving Average Models written by Stanford University. Department of Statistics and published by . This book was released on 1975 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: The author considers estimation procedures for the moving average model of order q. Walker's method uses k sample autocovariances (k> or = q). Assume that k depends on T in such a way that k nears infinity as T nears infinity. The estimates are consistent, asymptotically normal and asymptotically efficient if k = k (T) dominates log T and is dominated by (T sub 1/2). The approach in proving these theorems involves obtaining an explicit form for the components of the inverse of a symmetric matrix with equal elements along its five central diagonals, and zeroes elsewhere. The asymptotic normality follows from a central limit theorem for normalized sums of random variables that are dependent of order k, where k tends to infinity with T. An alternative form of the estimator facilitates the calculations and the analysis of the role of k, without changing the asymptotic properties.
Book Synopsis Optimal Asymptotic Properties of Maximum Likelihood Estimators of Parameters of Some Econometric Models by : Mary Kathleen Vickers
Download or read book Optimal Asymptotic Properties of Maximum Likelihood Estimators of Parameters of Some Econometric Models written by Mary Kathleen Vickers and published by . This book was released on 1977 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Four theorems are proven, which simplify the application to econometric models of Weiss's theorem on asymptotic properties of maximum likelihood estimators in nonstandard cases. The theorems require, roughly: the uniform convergence in any compact sets of the unknown parameters of the expection of the Hessian matrix of the log likelihood function; and the uniform convergence to 0 in the same sense of the variance of the same quantities. The fourth theorem allows one to conclude that the optimal properties hold on an image set of the parameters when the map satisfies certain smoothness conditions, and the first three theorems are satisfied for the original parameter set. These four theorems are applied to autoregressive models, nonlinear models, systems of equations, and probit and logit models to infer optimal asymptotic properties. (Author).
Book Synopsis Asymptotic Properties in Space and Time of an Estimator in Non-linear Functional Errors-in-variables Model by : István Fazekas
Download or read book Asymptotic Properties in Space and Time of an Estimator in Non-linear Functional Errors-in-variables Model written by István Fazekas and published by . This book was released on 1998 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Analysis for Nonlinear Spatial and Network Econometric Models by : Xingbai Xu
Download or read book Asymptotic Analysis for Nonlinear Spatial and Network Econometric Models written by Xingbai Xu and published by . This book was released on 2016 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial econometrics has been obtained more and more attention in the recent years. The spatial autoregressive (SAR) model is one of the most widely used and studied models in spatial econometrics. So far, most studies have been focused on linear SAR models. However, some types of spatial or network data, for example, censored data or discrete choice data, are very common and useful, but not suitable to study by a linear SAR model. That is why I study an SAR Tobit model and an SAR binary choice model in this dissertation. Chapter 1 studies a Tobit model with spatial autoregressive interactions. We consider the maximum likelihood estimation (MLE) for this model and analyze asymptotic properties of the estimator based on the spatial near-epoch dependence (NED) of the dependent variable process generated from the model structure. We show that the MLE is consistent and asymptotically normally distributed. Monte Carlo experiments are performed to verify finite sample properties of the estimator. Chapter 2 extends the MLE estimation of the SAR Tobit model studied in Chapter 1 to distribution-free estimation. We examine the sieve MLE of the model, where the disturbances are i.i.d. with an unknown distribution. This model can be applied to spatial econometrics and social networks when data are censored. We show that related variables are spatial NED. An important contribution of this chapter is that I develop some exponential inequalities for spatial NED random fields, which are also useful in other semiparametric studies when spatial correlation exists. With these inequalities, we establish the consistency of the estimator. Asymptotic distributions of structural parameters of the model are derived from a functional central limit theorem and projection. Simulations show that the sieve MLE can improve the finite sample performance upon misspecified normal MLEs, in terms of reduction in the bias and standard deviation. As an empirical application, we examine the school district income surtax rates in Iowa. Our results show that the spatial spillover effects are significant, but they may be overestimated if disturbances are restricted to be normally distributed. Chapter 3 studies the method of simulated moments (MSM) estimation of a binary choice game model with network links, where the network peer effects are non-negative, and there might be only one or few networks in the sample. The proposed estimation method can be applied to studies with binary dependent variables in the fields of empirical IO, social network and spatial econometrics. The model might have multiple Nash equilibria. We assume that the maximum Nash equilibrium, which always exists and is strongly coalition-proof and Pareto optimal, is selected. The challenging econometric issues are the possible correlation among all dependent variables and the discontinuous functional form of our simulated moments. We overcome these challenges via the empirical process theory and derive the spatial NED of the dependent variable. We establish a criterion for an NED random field to be stochastically equicontinuous and we apply it to develop the consistency and asymptotic normality of the estimator. We examine computational issues and finite sample properties of the MSM by some Monte Carlo experiments.
Book Synopsis Estimators for Persistent and Possibly Non-Stationary Data with Classical Properties by : Yuriy Gorodnichenko
Download or read book Estimators for Persistent and Possibly Non-Stationary Data with Classical Properties written by Yuriy Gorodnichenko and published by . This book was released on 2011 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers a moments based non-linear estimator that is root-T consistent and uniformly asymptotically normal irrespective of the degree of persistence of the forcing process. These properties hold for linear autoregressive models, linear predictive regressions, as well as certain non-linear dynamic models. Asymptotic normality is obtained because the moments are chosen so that the objective function is uniformly bounded in probability and that a central limit theorem can be applied Critical values from the normal distribution can be used irrespective of the treatment of the deterministic terms. Simulations show that the estimates are precise, and the t-test has good size in the parameter region where the least squares estimates usually yield distorted inference -- National Bureau of Economic Research web site.
Book Synopsis Asymptomatic Properties of the Maximum Likelihood and Non-linear Least Squares Estimators for Noninvertible Moving Average Models by : Katsuto Tanaka
Download or read book Asymptomatic Properties of the Maximum Likelihood and Non-linear Least Squares Estimators for Noninvertible Moving Average Models written by Katsuto Tanaka and published by . This book was released on 1987 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Behavior of the Fixed Effects Estimator in Nonlinear Models by : William H. Greene
Download or read book The Behavior of the Fixed Effects Estimator in Nonlinear Models written by William H. Greene and published by . This book was released on 2008 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: The nonlinear fixed effects models in econometrics has often been avoided for two reasons one practical, one methodological. The practical obstacle relates to the difficulty of estimating nonlinear models with possibly thousands of coefficients. In fact, in a large number of models of interest to practitioners, estimation of the fixed effects model is feasible even in panels with very large numbers of groups. The more difficult, methodological question centers on the incidental parameters problem that raises questions about the statistical properties of the estimator. There is very little empirical evidence on the behavior of the fixed effects estimator. In this note, we use Monte Carlo methods to examine the small sample bias in the binary probit and logit models, the ordered probit model, the tobit model, the Poisson regression model for count data and the exponential regression model for a nonnegative random variable. We find three results of note: A widely accepted result that suggests that the probit estimator is actually relatively well behaved appears to be incorrect. Perhaps to some surprise, the tobit model, unlike the others, appears largely to be unaffected by the incidental parameters problem, save for a surprising result related to the disturbance variance estimator. Third, as apparently unexamined previously, the estimated asymptotic estimators for fixed effects estimators appear uniformly to be downward biased.
Book Synopsis Spatial Econometrics: Methods and Models by : L. Anselin
Download or read book Spatial Econometrics: Methods and Models written by L. Anselin and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial econometrics deals with spatial dependence and spatial heterogeneity, critical aspects of the data used by regional scientists. These characteristics may cause standard econometric techniques to become inappropriate. In this book, I combine several recent research results to construct a comprehensive approach to the incorporation of spatial effects in econometrics. My primary focus is to demonstrate how these spatial effects can be considered as special cases of general frameworks in standard econometrics, and to outline how they necessitate a separate set of methods and techniques, encompassed within the field of spatial econometrics. My viewpoint differs from that taken in the discussion of spatial autocorrelation in spatial statistics - e.g., most recently by Cliff and Ord (1981) and Upton and Fingleton (1985) - in that I am mostly concerned with the relevance of spatial effects on model specification, estimation and other inference, in what I caIl a model-driven approach, as opposed to a data-driven approach in spatial statistics. I attempt to combine a rigorous econometric perspective with a comprehensive treatment of methodological issues in spatial analysis.
