Ch6 Dummy Variable Regression Models

Slides:



Advertisements
Similar presentations
Dummy Variables. Introduction Discuss the use of dummy variables in Financial Econometrics. Examine the issue of normality and the use of dummy variables.
Advertisements

F-tests continued.
Dummy variables Hill et al chapter 9. Parameters that vary between observations Assumption MR1 The parameters are the same for all observations. k= the.
Multiple Regression and Model Building
Tests of Significance for Regression & Correlation b* will equal the population parameter of the slope rather thanbecause beta has another meaning with.
Correlation Correlation is the relationship between two quantitative variables. Correlation coefficient (r) measures the strength of the linear relationship.
Econ 140 Lecture 151 Multiple Regression Applications Lecture 15.
Chapter 15 (Ch. 13 in 2nd Can.) Association Between Variables Measured at the Interval-Ratio Level: Bivariate Correlation and Regression.
1 Lecture 4:F-Tests SSSII Gwilym Pryce
1 Module II Lecture 4:F-Tests Graduate School 2004/2005 Quantitative Research Methods Gwilym Pryce
Chapter 13 Multiple Regression
© 2010 Pearson Prentice Hall. All rights reserved Least Squares Regression Models.
Ekonometrika AL MUIZZUDDIN F 1 Pengembangan Model Regresi (BAGIAN 2)
Chapter 12 Multiple Regression
CHAPTER 4 ECONOMETRICS x x x x x Multiple Regression = more than one explanatory variable Independent variables are X 2 and X 3. Y i = B 1 + B 2 X 2i +
Econ 140 Lecture 171 Multiple Regression Applications II &III Lecture 17.
Business Statistics - QBM117 Interval estimation for the slope and y-intercept Hypothesis tests for regression.
CHAPTER 6 ECONOMETRICS x x x x x Dummy Variable Regression Models Dummy, or indicator, variables take on values of 0 or 1 to indicate the presence or absence.
Dummy variable Up till now we have dealt exclusively with the variables which can measures in quantitative terms. But sometimes variables which we consider.
Quantitative Business Analysis for Decision Making Multiple Linear RegressionAnalysis.
Lecture 16 Correlation and Coefficient of Correlation
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Simple Linear Regression Analysis Chapter 13.
AM Recitation 2/10/11.
QMS 6351 Statistics and Research Methods Regression Analysis: Testing for Significance Chapter 14 ( ) Chapter 15 (15.5) Prof. Vera Adamchik.
Lecturer: Kem Reat, Viseth, PhD (Economics)
Multiple Regression and Model Building Chapter 15 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.
Relationship between two variables Two quantitative variables: correlation and regression methods Two qualitative variables: contingency table methods.
Scatterplot and trendline. Scatterplot Scatterplot explores the relationship between two quantitative variables. Example:
Chapter 5 Demand Estimation Managerial Economics: Economic Tools for Today’s Decision Makers, 4/e By Paul Keat and Philip Young.
Economics 310 Lecture 11 Dummy variables and Varying Parameters models.
1 Hypothesis Tests & Confidence Intervals (SW Ch. 7) 1.For a single coefficient 2.For multiple coefficients 3.Other types of hypotheses involving multiple.
Multiple Regression BPS chapter 28 © 2006 W.H. Freeman and Company.
Copyright © 2006 The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Dummy Variable Regression Models chapter ten.
Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.
© Copyright McGraw-Hill Correlation and Regression CHAPTER 10.
Chapter 16 Data Analysis: Testing for Associations.
1 AAEC 4302 ADVANCED STATISTICAL METHODS IN AGRICULTURAL RESEARCH Chapter 14: F Tests.
Multiple Regression. Simple Regression in detail Y i = β o + β 1 x i + ε i Where Y => Dependent variable X => Independent variable β o => Model parameter.
Click to edit Master title style Midterm 3 Wednesday, June 10, 1:10pm.
Testing Regression Coefficients Prepared by: Bhakti Joshi February 06, 2012.
The general linear test approach to regression analysis.
9.1 Chapter 9: Dummy Variables A Dummy Variable: is a variable that can take on only 2 possible values: yes, no up, down male, female union member, non-union.
Inferences Concerning Variances
1 1 Slide The Simple Linear Regression Model n Simple Linear Regression Model y =  0 +  1 x +  n Simple Linear Regression Equation E( y ) =  0 + 
Another way to select sample size The sample R-squared method.
26134 Business Statistics Week 4 Tutorial Simple Linear Regression Key concepts in this tutorial are listed below 1. Detecting.
Chow Test Kevin Ardon Colt Van Eaton Dayana Zhappassova.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Multiple Regression Chapter 14.
Lecturer: Ing. Martina Hanová, PhD.. Regression analysis Regression analysis is a tool for analyzing relationships between financial variables:  Identify.
Inference about the slope parameter and correlation
Lecture #25 Tuesday, November 15, 2016 Textbook: 14.1 and 14.3
ECONOMETRICS EC331 Prof. Burak Saltoglu
F-tests continued.
AP Statistics Chapter 14 Section 1.
Dr. Amjad El-Shanti MD, PMH,Dr PH University of Palestine 2016
Chow test.
THE LINEAR REGRESSION MODEL: AN OVERVIEW
Multiple Regression Lecture 13 Lecture 12.
Multiple Regression Analysis with Qualitative Information
Hypothesis Tests: One Sample
I271B Quantitative Methods
Correlation and Regression
ביצוע רגרסיה לוגיסטית. פרק ה-2
LIMITED DEPENDENT VARIABLE REGRESSION MODELS
Tutorial 1: Misspecification
Chapter 8: DUMMY VARIABLE (D.V.) REGRESSION MODELS
Chapter 9 Dummy Variables Undergraduated Econometrics Page 1
Financial Econometrics Fin. 505
Introduction to Regression
Presentation transcript:

Ch6 Dummy Variable Regression Models ---The Dummy Variable Technique

1. Basics 1. Quantitative variables vs. Qualitative variables 2.Dummy variables: variables that assume such 0 and 1 values are called dummy variables.

Structural or Parameter Stability of regression Models When we use a regression model involving time series data, it may happen that there is a structural change in the relationship between the regressand and the regrssors. By structural change, we mean that the values of the parameters of the model do not retain the same through the entire time period.

2. Testing for Structural Stability How do we find out that a structural change has in fact occurred? 1). The Chow Test 2). The Dummy Variable Technique

2. The weakness of the Chow test 1. The mechanics of the Chow test 2. The weakness of the Chow test

3. The Chow Test The Chow test shows that under the null hypothesis the regressions (the restricted model and unrestricted model) are statistically the same (i.e., no structural change or break) and the F ratio given above follows the F distribution with k and (n1+n2-2k) df in the numerator and denominator, respectively. But the Chow test does not tell us whether the difference in the two regressions is because of differences in the intercept terms or the slop coefficients or both.

4.The Dummy Variable Alternative to the Chow Test is the differential intercept and is the differential slope coefficient ( also called the slope drifter) The use of dummy variable D enables us to differentiate between the intercepts and between slope coefficients of the two periods