Spatial Econometric Analysis

Slides:



Advertisements
Similar presentations
Autocorrelation Functions and ARIMA Modelling
Advertisements

Dummy Variables. Introduction Discuss the use of dummy variables in Financial Econometrics. Examine the issue of normality and the use of dummy variables.
Market Potential, MAUP, NUTS and other spatial mysteries Fernando Bruna Jesus Lopez-Rodriguez Andres Faina 11th International Workshop Spatial Econometrics.
Dates for term tests Friday, February 07 Friday, March 07
Spatial Regression Modeling
Vector Autoregressive Models
Unit Roots & Forecasting
SPATIAL DATA ANALYSIS Tony E. Smith University of Pennsylvania Point Pattern Analysis Spatial Regression Analysis Continuous Pattern Analysis.
Spatial Econometric Analysis Using GAUSS 9 Kuan-Pin Lin Portland State University.
Introduction to Applied Spatial Econometrics Attila Varga DIMETIC Pécs, July 3, 2009.
19 th Advanced Summer School in Regional Science GIS and spatial econometrics University of Groningen, 4-12 July 2006 “Income and human capital inequalities.
1Prof. Dr. Rainer Stachuletz Simultaneous Equations y 1 =  1 y 2 +  1 z 1 + u 1 y 2 =  2 y 1 +  2 z 2 + u 2.
Statistics 350 Lecture 16. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
Lecture 27 Distributed Lag Models
Econometric Analysis of Panel Data
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.
Why Geography is important.
Spatial Methods in Econometrics Daniela Gumprecht Department for Statistics and Mathematics, University of Economics and Business Administration, Vienna.
Econometrics I Summer 2011/2012 Course Guarantor: prof. Ing. Zlata Sojková, CSc., Lecturer: Ing. Martina Hanová, PhD.
Tse-Chuan Yang, Ph.D The Geographic Information Analysis Core Population Research Institute Social Science Research Institute Pennsylvania State University.
12 Autocorrelation Serial Correlation exists when errors are correlated across periods -One source of serial correlation is misspecification of the model.
Chapter 11 Simple Regression
Spatial Econometric Analysis Using GAUSS 4 Kuan-Pin Lin Portland State University.
Section 4.2 Regression Equations and Predictions.
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.
Spatial and non spatial approaches to agricultural convergence in Europe Luciano Gutierrez*, Maria Sassi** *University of Sassari **University of Pavia.
LECTURE 1 - SCOPE, OBJECTIVES AND METHODS OF DISCIPLINE "ECONOMETRICS"
Spatial Econometric Analysis Using GAUSS
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.
Problems with the Durbin-Watson test
Panel Data Analysis Using GAUSS
Spatial Econometric Analysis Using GAUSS 10 Kuan-Pin Lin Portland State University.
Chap 5 The Multiple Regression Model
M.Sc. in Economics Econometrics Module I Topic 4: Maximum Likelihood Estimation Carol Newman.
Spatial Econometric Analysis Using GAUSS 8 Kuan-Pin Lin Portland State University.
Lecturer: Ing. Martina Hanová, PhD. Business Modeling.
Lec. 19 – Hypothesis Testing: The Null and Types of Error.
Spatial Econometric Analysis 3 Kuan-Pin Lin Portland State University.
Spatial Econometric Analysis
QMT 3033 ECONOMETRICS QMT 3033 ECONOMETRIC.
REGRESSION DIAGNOSTIC III: AUTOCORRELATION
Luciano Gutierrez*, Maria Sassi**
Dynamic Models, Autocorrelation and Forecasting
Spatial Econometric Analysis Using GAUSS
Spatial Modeling Lee Rivers Mobley, Ph.D..
Large and Small Sample Properties of the MESS Specification
Kakhramon Yusupov June 15th, :30pm – 3:00pm Session 3
REGRESSION DIAGNOSTIC II: HETEROSCEDASTICITY
The Sensitivity of Investment to the changes Rate of Interest: Evidence from Iraq Sazan Taher Saeed 2017.
Econometric methods of analysis and forecasting of financial markets
Fundamentals of regression analysis 2
Introduction to Econometrics
Serial Correlation and Heteroskedasticity in Time Series Regressions
Lecturer Dr. Veronika Alhanaqtah
Spatial Autocorrelation
T test.
Spatial Econometric Analysis Using GAUSS
Serial Correlation and Heteroscedasticity in
Tutorial 1: Misspecification
Econometrics Analysis
Spatial Econometric Analysis
Spatial Econometric Analysis
Lecturer Dr. Veronika Alhanaqtah
Lecturer Dr. Veronika Alhanaqtah
Cases. Simple Regression Linear Multiple Regression.
Statistics 101 CORRELATION Section 3.2.
Serial Correlation and Heteroscedasticity in
Regression and Correlation of Data
BOX JENKINS (ARIMA) METHODOLOGY
Presentation transcript:

Spatial Econometric Analysis 2 Kuan-Pin Lin Portland State University

Spatial Econometric Models Spatial Exogenous Model Spatial Lag Model Spatial Mixed Model Spatial Error Model Spatial AR(1) Spatial MA(1) Spatial ARMA(1,1) Spatial Error Components Model

Spatial Exogenous Model Lagged Explanatory Variables The Model

Spatial Lag Model Lagged Dependent Variable The Model

Spatial Mixed Model The Model

Spatial Error Models Spatial AR(1) Spatial MA(1) Spatial ARMA(1,1)

Spatial Error Components Model The Model

Spatial Econometric Models The General Model: SARAR(1,1) Allowing spatial weights matrix to be different in the regression and in the error. The special case is W = M.

Spatial Model Specification Tests Moran Test Moran’s I Test Statistic Asymptotic Theory Bootstrap Method LM Test and Robust LM Test Spatial Error Model Spatial Lag Model

Hypothesis Testing The Basic Model

Moran-Based Test Statistics Moran’s I Index Can not distinguish between spatial lag or spatial error

LM-Based Test Statistics LM Test Statistic for Spatial Error Can not distinguish between spatial AR or spatial MA

LM-Based Test Statistics LM Test Statistic for Spatial Lag

LM-Based Test Statistics Robust LM Test Statistic for Spatial Error Robust LM Test Statistic for Spatial Lag

LM-Based Test Statistics Joint LM Test for Spatial Correlation (Spatial Lag and Spatial Error)

Hypothesis Testing Example Crime Equation (Crime Rate) = a + b (Family Income) + g (Housing Value) + e (numbers in parentheses are p-values of the tests) Moran-I LM-err LM-lag Robust LM-err Robust Hetero. Crime Rate 5.6753 (0.000) 26.902 Family Income 4.6624 17.841 Housing Value 2.1529 (0.031) 3.3727 (0.066) e 2.954 (0.003) 5.723 (0.017) 9.363 (0.002) 0.0795 (0.778) 3.72 (0.054) 1.058 (0.589)

References L. Anselin, and A. K. Bera, R. J.G.M. Florax, and M. Yoon (1996), “Simple Diagnostic Tests for Spatial Dependence,” Regional Science and Urban Economics, 26, 77-104. L. Anselin, and H. Kelejian (1997), “Testing for Spatial Autocorrelation in the Presence of Endogenous Regressors,” International Regional Science Review, 20, 153–182. L. Anselin, and S. Rey (1991), “Properties of Tests for Spatial Dependence in Linear Regression Models,” Geographical Analysis, 23, 112-131. H. Kelejian, and I.R. Prucha (2001)., “On the Asymptotic Distribution of Moran I Test Statistic with Applications,” Journal Econometrics, 104, 219-257.