Spatial Econometric Analysis 3 Kuan-Pin Lin Portland State University.

Slides:



Advertisements
Similar presentations
Functional Form and Dynamic Models
Advertisements

Econometric Analysis of Panel Data Panel Data Analysis: Extension –Generalized Random Effects Model Seemingly Unrelated Regression –Cross Section Correlation.
Data organization.
Dynamic Panel Data: Challenges and Estimation Amine Ouazad Ass. Prof. of Economics.
Generalized Method of Moments: Introduction
There are at least three generally recognized sources of endogeneity. (1) Model misspecification or Omitted Variables. (2) Measurement Error.
MACROECONOMETRICS LAB 2 – SIMULTANEOUS MODELS. ROADMAP What do we need simulteneous models for? – What you know from the lecture – Empirical side (w/o.
SOLVED EXAMPLES.
Introduction to Applied Spatial Econometrics Attila Varga DIMETIC Pécs, July 3, 2009.
Nguyen Ngoc Anh Nguyen Ha Trang
19 th Advanced Summer School in Regional Science GIS and spatial econometrics University of Groningen, 4-12 July 2006 “Income and human capital inequalities.
1 Econometrics 1 Lecture 7 Multicollinearity. 2 What is multicollinearity.
Simultaneous Equations Models
Part 2b Parameter Estimation CSE717, FALL 2008 CUBS, Univ at Buffalo.
Estimation of parameters. Maximum likelihood What has happened was most likely.
Lecture 27 Distributed Lag Models
Maximum Likelihood We have studied the OLS estimator. It only applies under certain assumptions In particular,  ~ N(0, 2 ) But what if the sampling distribution.
2. Point and interval estimation Introduction Properties of estimators Finite sample size Asymptotic properties Construction methods Method of moments.
Different chi-squares Ulf H. Olsson Professor of Statistics.
Maximum-Likelihood estimation Consider as usual a random sample x = x 1, …, x n from a distribution with p.d.f. f (x;  ) (and c.d.f. F(x;  ) ) The maximum.
M-Estimation The Model –min E(q(z,  –∑ i=1,…,n q(z i,  )/n → p E(q(z,  –b = argmin ∑ i=1,…,n q(z i,  ) (or divided by n) –Zero-Score: ∑ i=1,…,n.
Economics 310 Lecture 18 Simultaneous Equations There is a two-way, or simultaneous, relationship between Y and (some of) the X’s, which makes the distinction.
Part 7: Regression Extensions [ 1/59] Econometric Analysis of Panel Data William Greene Department of Economics Stern School of Business.
July 3, Department of Computer and Information Science (IDA) Linköpings universitet, Sweden Minimal sufficient statistic.
Part 9: GMM Estimation [ 1/57] Econometric Analysis of Panel Data William Greene Department of Economics Stern School of Business.
Empirical Financial Economics 2. The Efficient Markets Hypothesis - Generalized Method of Moments Stephen Brown NYU Stern School of Business UNSW PhD Seminar,
The Paradigm of Econometrics Based on Greene’s Note 1.
Part 1: Introduction 1-1/22 Econometrics I Professor William Greene Stern School of Business Department of Economics.
Spatial Econometric Analysis Using GAUSS 4 Kuan-Pin Lin Portland State University.
Spatial Econometric Analysis Using GAUSS 1 Kuan-Pin Lin Portland State University.
Random Regressors and Moment Based Estimation Prepared by Vera Tabakova, East Carolina University.
M.Sc. in Economics Econometrics Module I Topic 7: Censored Regression Model Carol Newman.
Spatial Econometric Analysis Using GAUSS
Generalised method of moments approach to testing the CAPM Nimesh Mistry Filipp Levin.
5. Spatial regression models 5.1 Basic types of spatial regression models There are two basic types of spatial regression models which can be chosen subject.
Panel Data Analysis Using GAUSS
Spatial Econometric Analysis Using GAUSS 10 Kuan-Pin Lin Portland State University.
M.Sc. in Economics Econometrics Module I Topic 4: Maximum Likelihood Estimation Carol Newman.
Simultaneous Equations Models A simultaneous equations model is one in which there are endogenous variables which are determined jointly. e.g. the demand-supply.
Economics 310 Lecture 21 Simultaneous Equations Three Stage Least Squares A system estimator. More efficient that two-stage least squares. Uses all information.
Spatial Econometric Analysis Using GAUSS 8 Kuan-Pin Lin Portland State University.
Part 4A: GMM-MDE[ 1/33] Econometric Analysis of Panel Data William Greene Department of Economics Stern School of Business.
The Instrumental Variables Estimator The instrumental variables (IV) estimator is an alternative to Ordinary Least Squares (OLS) which generates consistent.
IV Estimation Instrumental Variables. Implication Estimate model by OLS and by IV, and compare estimates If But test INDIRECTLY using Wu-Hausman.
Spatial Econometric Analysis
Esman M. Nyamongo Central Bank of Kenya
Stat 223 Introduction to the Theory of Statistics
Dynamic Models, Autocorrelation and Forecasting
Spatial Econometric Analysis Using GAUSS
Econometric methods of analysis and forecasting of financial markets
Simultaneous equation system
Econometric Analysis of Panel Data
STOCHASTIC REGRESSORS AND THE METHOD OF INSTRUMENTAL VARIABLES
Charles University Charles University STAKAN III
Serial Correlation and Heteroskedasticity in Time Series Regressions
Spatial Econometric Analysis
POINT ESTIMATOR OF PARAMETERS
مدلسازي تجربي – تخمين پارامتر
Spatial Econometric Analysis Using GAUSS
Chengyuan Yin School of Mathematics
Simultaneous equation models Prepared by Nir Kamal Dahal(Statistics)
Econometrics Chengyuan Yin School of Mathematics.
Spatial Econometric Analysis
Linear Panel Data Models
Spatial Econometric Analysis
Esman M. Nyamongo Central Bank of Kenya
Microeconometric Modeling
Econometrics I Professor William Greene Stern School of Business
Econometric Analysis of Panel Data
Lecture 20 Two Stage Least Squares
Presentation transcript:

Spatial Econometric Analysis 3 Kuan-Pin Lin Portland State University

Model Estimation Spatial Lag Model SPLAG(1) OLS is biased and inconsistent.

Spatial Lag Model IV or 2SLS Estimation Instrumental Variables Two-Stage Least Squares

Spatial Lag Model IV/2SLS with SHAC

Spatial Lag Model GMM Estimation Strong Exogeneity of Instrumental Variables Generalized Method of Moments (GMM)

Spatial Lag Model GMM Estimation Efficient GMM Estimator

Spatial Lag Model Maximum Likelihood Estimation Normal Density Function

Spatial Lag Model Maximum Likelihood Estimation Jacobian Matrix

Spatial Lag Model Maximum Likelihood Estimation Log-Likelihood Function

Spatial Lag Model Maximum Likelihood Estimation Quasi Maximum Likelihood (QML) Estimator

Crime Equation Anselin (1988) Spatial Lag Model (Crime Rate) =  +  (Family Income) +  (Housing Value) +  W (Crime Rate) +  OLS vs. IV Estimator OLS Parameter OLS s.e. IV Parameter IV s.e    R2R

Crime Equation Anselin (1988) Spatial Lag Model (Crime Rate) =  +  (Family Income) +  (Housing Value) +  W (Crime Rate) +  IV with SHAC Estimator IV Parameter IV s.e IV s.e./hc IV s.e/hac    R2R

Crime Equation Anselin (1988) Spatial Lag Model (Crime Rate) =  +  (Family Income) +  (Housing Value) +  W (Crime Rate) +  GMM Estimator GMM-hc Parameter GMM-hc s.e GMM-hac Parameter GMM-hac s.e    R2R

Crime Equation Anselin (1988) Spatial Lag Model (Crime Rate) =  +  (Family Income) +  (Housing Value) +  W (Crime Rate) +  QML vs. GMM Estimator QML Parameter QML s.e GMM-hac Parameter GMM-hac s.e    L

References Kelejian , H. , and I. R. Prucha , A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model. International Economic Review 40 , Kelejian, H., and I. R. Prucha, Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances. Journal of Econometrics, forthcoming. Lee , L. F. , Asymptotic Distributions of Maximum Likelihood Estimators for Spatial Autoregressive Models. Econometrica, 72,