PPP TESTS IN COINTEGRATED PANELS: EVIDENCE FROM ASIAN DEVELOPING COUNTRIES

Size: px
Start display at page:

Download "PPP TESTS IN COINTEGRATED PANELS: EVIDENCE FROM ASIAN DEVELOPING COUNTRIES"

Transcription

1 PPP TESTS IN COINTEGRATED PANELS: EVIDENCE FROM ASIAN DEVELOPING COUNTRIES Syed Abul Basher Department of Economcs York Unversty Toronto, ON M3J 1P3 and Mohammed Mohsn * Department of Economcs Unversty of Tennessee Knoxvlle, TN mmohsn@utk.edu Abstract: Ths paper tests the relatve verson of purchasng power party (PPP) for a set of ten Asan developng countres usng panel contegraton framework. We employ betweendmenson dynamc OLS estmator as proposed by Pedron (2001b). The test results overwhelmngly reject the PPP hypothess. JEL classfcatons: F31, C22, C23 Key Words: Purchasng Power Party, Panel Contegraton, Unt Roots * Correspondng author: Mohammed Mohsn, Assstant Professor, Department of Economcs, Unversty of Tennessee, TN 37996, USA. Tel: ; Fax: ; E-mal: mmohsn@utk.edu

2 1. Introducton The purpose of ths study s to examne the emprcal valdty of relatve purchasng power party (PPP) doctrne n the context of a set of Asan developng economes. In the lterature there has been an nflux of emprcal studes on PPP especally n the 90s wth mxed fndngs. The man concern of these studes s to fnd any possble common stochastc trend(s) between exchange rates and relatve prces n a blateral context by employng a number of dfferent unt root and contegraton tests. Majorty of them use the post Bretton Woods data. Obvously these studes do not have suffcently longer tme seres to overcome the possble problem of small sample dstortons that the tradtonal unt root and contegraton tests encounter. Of course there are other studes that use longer tme span. However, the tradtonal tests for PPP usng longer unvarate tme seres usually overlook the potental problem of structural break as the data set covers both the fxed and floatng exchange rate regmes [Qan and Strauss (2001)]. To crcumvent these problems, researchers of late started recommendng the use of panel contegraton framework to get econometrcally robust fndngs [Baltag and Kao (2000), Banerjee (1999), Pedron(2000, 2001b) and Qan and Strauss (2001)]. A major advantage of ths approach s that t allows one to selectvely pool the long run nformaton contaned n the panel whle permttng the short run dynamcs (and heterogenety) among dfferent members. An mportant consderaton regardng poolng has to do wth the dmenson over whch they are pooled. One can pool across ether the wthn or between dmensons. Pedron concluded that the between dmenson has relatvely lower small sample dstortons. The goal of ths study s to employ ths mproved panel contegraton method to evaluate the PPP doctrne n the context of a set of somewhat homogeneous developng countres. 2. Methodologcal Dscussons Pedron evaluates the asymptotc propertes of three versons of panel estmators. Resdual-FM and the adjusted-fm pooled the data along the wthn dmenson and group-fm pooled the data along the between dmenson. He shows that the group-fm has relatvely lower small sample dstortons and more flexblty n terms of hypothess testng. For example, n the panel unt root regresson Y t = Yt µ + ε t for t = 1,2 T, and = 1,2 N, pooled tests mply H 0 : µ = 1 and H A : µ = µ A < 1 where as grouped mean tests mply 1

3 H 0 : µ = 1 and H A : µ < 1. It s clear that µ under the alternatve hypothess s not requred to be the same among dfferent grouped tests often allow for a greater flexblty. where = α + + βx t µ t t members of the panel. Hence the X X + ε (1) Z t = ( Y t, X t ) ~ I(1) and ξ µ, ε ) ~ I( 0) wth long run covarance matrx Ω L ( L s a lower trangular decomposton of Ω = Ω Consder the followng contegrated system for a panel of = 1,2 N members = L Γ + Γ β * NT Y t = N t = t N = 1 T t = 1 Lˆ (X t X X ) Y * where Y t = ( Yt Y ) X t, τˆ ˆ 0 ( ˆ 0 Γ21 + Ω21 Γ22 + Ω22) Lˆ ˆ 22 L22 K 1 Lˆ ˆ ˆ. The assocated t- * Y t = α + β X + γ X + µ t. (3) t t = ( t t Ω ). In ths case the varables are sad to be contegrated for each member of the panel, wth contegratng vector β. It should be noted that α allows the contegratng relatonshp to nclude member specfc fxed effects. Ths covarance matrx can also be decomposed as sum of autocovarances., where Ω s the contemporaneous covarance and Γ s a weghted The panel FMOLS estmator for the coeffcent β s gven by ) 2 T t = 1 statstc follows standard normal dstrbuton. 1 ( X t * t Tτˆ For the panel DOLS estmaton, we need to augment the contegratng regresson n (1) as follows: k k = K t k (2) where the estmated coeffcent β s gven by N T T * β = N Z Y * DS tzt Zt t (4) = 1 t = 1 t = 1 where Z = X X,... ) s 2(K + 1) x 1 vector of regressors. t ( X t, t K X t + K 1 The detal expresson of the t-statstc s avalable n Pedron (2000). 2

4 3. Results 3.1 Data The panel conssts of 240 monthly (and 80 quarterly) seres of end of perod nomnal U.S. dollar exchange rates (E) and aggregate consumer prce ndex rato (P) for 10 countres coverng the perod from January 1980 through December 1999 (1980:1 to 1999:4 for quarterly data). The sample countres are Inda, Indonesa, South Korea, Malaysa, Nepal, Pakstan, The Phlppnes, Sngapore, Sr Lanka and Thaland. The selecton of these countres s somewhat arbtrary, except that they belong to a set of major Asan developng economes. All data have been taken from IMF s Internatonal Fnancal Statstcs CD-ROM. The requred log-transformaton has been done. The results reported here are only for monthly data The panel unt root and the panel contegraton tests In order to determne the presence of a unt root n ndvdual country specfc data we employ standard ADF test. For a panel unt root we conduct Levn-Ln (1992) and IPS t-bar (1997) tests. Both the panel tests nclude a constant and a heterogeneous tme trend n ther specfcatons. The test results show that the unt root null could not be rejected and hence the seres are generated by an I(1) process. Next we perform contegraton tests for all the sample ndvdual countres by usng Johansen and Juselus (1990) method and for the panel by usng Pedron (1999) procedure. We fnd the evdence of no contegraton from both ndvdual and panel contegraton tests. So, the PPP does not hold n the long-run n ths context. To conserve space we report only panel unt root (upper panel) and panel contegraton (lower panel) results n Table FMOLS and DOLS Table 2 reports the results of ndvdual and panel FMOLS and DOLS. Indvdual FMOLS and DOLS estmates and the respectve t-statstcs for H 0 : β = 1 are provded n the frst 10 entres n Table 2, whle results for the panel estmators wth and wthout common tme dummes are shown at the bottom of the table. Both ndvdual and panel tests overwhelmngly reject the null hypothess of strong PPP. As for the ndvdual countres, 7 out of 10 cases we fnd the rejecton of the null. We should also note that both FMOLS and DOLS test results are n agreement. 2 Smlar results on quarterly data wll be made avalable upon request. 3

5 For the panel tests, all 4 reported tests reject the null at least at 5% level except n the case of wthn-dmenson panel DOLS wthout tme dummes. However, t s mportant to note that the between-dmenson estmators consstently produce larger estmates than the wthn-dmenson estmators. Ths fndng s thus consstent wth Pedron (2001b). Followng hm, we argue that these hgher values to be a more accurate representaton of the average long-run relatonshp between nomnal exchange rates and aggregate prce ratos. 4. Concluson In ths study we employ panel contegraton method for evaluatng the purchasng power party doctrne n a panel of ten Asan developng economes for the post Bretton Woods perod. The emprcal fndngs of ths paper do not support the relatve verson of PPP. The analyss of the ndvdual countres furthermore ndcates that ths falure of the PPP s not drven by the data from only a few countres. Rather, the falure of strong PPP appears to be pervasve n the flexble exchange rate regme. Acknowledgements We would lke to thank Don Bruce, Keya Matra and Peter Predon for suggestons and encouragements. The errors are of course our own. 4

6 References Baltag, B.H. and C. Kao, 2000, Nonstatonary panels, contegraton n panels and dynamc panels: a survey, n B.H. Baltag, eds., Nonstatonary panels, panel contegraton, and dynamc panels, advances n econometrcs, Vol. 15, (JAI) Banerjee, A., 1999, Panel data unt roots and contegraton: an overvew, Oxford Bulletn of Economcs and Statstcs, S1, 61, Im, K.S., M.H. Pesaran, and Y. Shn, 1997, Testng for unt roots n heterogeneous panels, Dscusson paper, Unversty of Cambrdge, December. Johansen, S. and K. Juselus, 1990, Maxmum lkelhood estmaton and nferences on contegraton wth applcatons to the demand for money, Oxford Bulletn of Economcs and Statstcs, 52, Levn, A. and C.F. Ln, 1992, Unt root tests n panel data: asymptotc and fnte sample propertes, Department of Economcs, Unversty of Calforna at San Dego, Dscusson paper no Mark, N. and D. Sul, 1999, A computatonally smple contegraton vector estmator for panel data, manuscrpt, Oho State Unversty. Pedron, P., 1996, Fully modfed OLS for heterogeneous Contegrated panels and the case of purchasng power party, Workng paper , Department of Eocnomcs, Indana Unversty. Pedron, P., 1999, Crtcal values for contegraton tests n heterogeneous panels wth multple regressors, Oxford Bulletn of Economcs and Statstcs, S1, 61, Pedron, P., 2000, Fully modfed OLS for heterogeneous Contegrated panels, n B.H. Baltag, eds., Nonstatonary panels, panel contegraton, and dynamc panels, advances n econometrcs, Vol. 15, (JAI) Pedron, P., 2001a, Asymptotc and fnte sample propertes of pooled tme seres tests wth an applcaton to the PPP hypothess, Indana Unversty Workng Paper. Pedron, P., 2001b, Purchasng power party n contegrated panels, The Revew of Economcs and Statstcs, 83, 4, Qan, H. and J. Strauss, 2001, Panel PPP tests wth unknown cross-sectonal dependence and heteroscedastcty, unpublshed manuscrpt. 5

7 Table 1: Panel Unt Root and Contegraton Test Statstcs Levn-Ln rho-stat Levn-Ln t-rho-stat Levn-Ln ADF-stat Panel Unt Root Tests a,b,c Log of E Log of P IPS ADF-stat Panel Contegraton Tests d Constant Panel v-statstcs Panel ρ-statstcs Panel t-statstcs (non-parametrc) Panel t-statstcs (parametrc) Constant + trend Group ρ-statstcs Group t-statstcs (non-parametrc) Group t-statstcs (parametrc) Notes: a. The crtcal values are from Levn and Ln (1992) Table 3 (wth N=10 and T=250). b. IPS ndcates the Im et al. (1997) test. The crtcal values are taken from Table 4. c. Unt root tests nclude a constant and heterogeneous tme trend n the data. d. The crtcal values for the panel contegraton tests are base on Pedron (2001a). Table 2: Purchasng Power Party Tests Country FMOLS t-stat DOLS t-stat Inda Indonesa Korea Malaysa Nepal Pakstan The Phlppnes Sngapore Sr Lanka Thaland *** -6.19*** *** 20.66*** 8.64*** 3.55*** *** *** -5.99*** *** 6.76*** 3.27*** * 2.42** Panel Results wthout tme dummes wthn a between b * 13.97*** *** wth tme dummes wthn between *** -2.26** *** 2.55** Notes: t-stats are for H 0 : β = 1. ***,**,* ndcate, 1%,5%,10% rejecton level, respectvely. a. wthn-dmenson reports Mark and Sul (1999) unweghted wthn-dmenson DOLS and an analogous unweghted FMOLS. b. between-dmenson reports Pedron (1996) group mean panel FMOLS and the group mean panel DOLS ntroduced n Pedron (2001b). 6

Testing for seasonal unit roots in heterogeneous panels

Testing for seasonal unit roots in heterogeneous panels Testng for seasonal unt roots n heterogeneous panels Jesus Otero * Facultad de Economía Unversdad del Rosaro, Colomba Jeremy Smth Department of Economcs Unversty of arwck Monca Gulett Aston Busness School

More information

2016 Wiley. Study Session 2: Ethical and Professional Standards Application

2016 Wiley. Study Session 2: Ethical and Professional Standards Application 6 Wley Study Sesson : Ethcal and Professonal Standards Applcaton LESSON : CORRECTION ANALYSIS Readng 9: Correlaton and Regresson LOS 9a: Calculate and nterpret a sample covarance and a sample correlaton

More information

A Comparative Study for Estimation Parameters in Panel Data Model

A Comparative Study for Estimation Parameters in Panel Data Model A Comparatve Study for Estmaton Parameters n Panel Data Model Ahmed H. Youssef and Mohamed R. Abonazel hs paper examnes the panel data models when the regresson coeffcents are fxed random and mxed and

More information

Are Health Expenditure and GDP Cointegrated: A Panel Analysis

Are Health Expenditure and GDP Cointegrated: A Panel Analysis Journal of Busness and Economcs ISSN 2155-7950 USA December 2010 Volume 1 No. 1 Academc Star Publshng Company 2010 http://www.academcstar.us Are Health Expendture and GDP Contegrated: A Panel Analyss Engn

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Econometrics of Panel Data

Econometrics of Panel Data Econometrcs of Panel Data Jakub Mućk Meetng # 8 Jakub Mućk Econometrcs of Panel Data Meetng # 8 1 / 17 Outlne 1 Heterogenety n the slope coeffcents 2 Seemngly Unrelated Regresson (SUR) 3 Swamy s random

More information

Question 1 carries a weight of 25%; question 2 carries 20%; question 3 carries 25%; and question 4 carries 30%.

Question 1 carries a weight of 25%; question 2 carries 20%; question 3 carries 25%; and question 4 carries 30%. UNIVERSITY OF EAST ANGLIA School of Economcs Man Seres PGT Examnaton 017-18 FINANCIAL ECONOMETRICS ECO-7009A Tme allowed: HOURS Answer ALL FOUR questons. Queston 1 carres a weght of 5%; queston carres

More information

Statistics for Economics & Business

Statistics for Economics & Business Statstcs for Economcs & Busness Smple Lnear Regresson Learnng Objectves In ths chapter, you learn: How to use regresson analyss to predct the value of a dependent varable based on an ndependent varable

More information

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6 Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.

More information

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications Durban Watson for Testng the Lack-of-Ft of Polynomal Regresson Models wthout Replcatons Ruba A. Alyaf, Maha A. Omar, Abdullah A. Al-Shha ralyaf@ksu.edu.sa, maomar@ksu.edu.sa, aalshha@ksu.edu.sa Department

More information

A Monte Carlo Study for Swamy s Estimate of Random Coefficient Panel Data Model

A Monte Carlo Study for Swamy s Estimate of Random Coefficient Panel Data Model A Monte Carlo Study for Swamy s Estmate of Random Coeffcent Panel Data Model Aman Mousa, Ahmed H. Youssef and Mohamed R. Abonazel Department of Appled Statstcs and Econometrcs, Instute of Statstcal Studes

More information

x i1 =1 for all i (the constant ).

x i1 =1 for all i (the constant ). Chapter 5 The Multple Regresson Model Consder an economc model where the dependent varable s a functon of K explanatory varables. The economc model has the form: y = f ( x,x,..., ) xk Approxmate ths by

More information

Module Contact: Dr Susan Long, ECO Copyright of the University of East Anglia Version 1

Module Contact: Dr Susan Long, ECO Copyright of the University of East Anglia Version 1 UNIVERSITY OF EAST ANGLIA School of Economcs Man Seres PG Examnaton 016-17 ECONOMETRIC METHODS ECO-7000A Tme allowed: hours Answer ALL FOUR Questons. Queston 1 carres a weght of 5%; Queston carres 0%;

More information

Chapter 13: Multiple Regression

Chapter 13: Multiple Regression Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to

More information

Chapter 15 Student Lecture Notes 15-1

Chapter 15 Student Lecture Notes 15-1 Chapter 15 Student Lecture Notes 15-1 Basc Busness Statstcs (9 th Edton) Chapter 15 Multple Regresson Model Buldng 004 Prentce-Hall, Inc. Chap 15-1 Chapter Topcs The Quadratc Regresson Model Usng Transformatons

More information

Statistics II Final Exam 26/6/18

Statistics II Final Exam 26/6/18 Statstcs II Fnal Exam 26/6/18 Academc Year 2017/18 Solutons Exam duraton: 2 h 30 mn 1. (3 ponts) A town hall s conductng a study to determne the amount of leftover food produced by the restaurants n the

More information

Factor models with many assets: strong factors, weak factors, and the two-pass procedure

Factor models with many assets: strong factors, weak factors, and the two-pass procedure Factor models wth many assets: strong factors, weak factors, and the two-pass procedure Stanslav Anatolyev 1 Anna Mkusheva 2 1 CERGE-EI and NES 2 MIT December 2017 Stanslav Anatolyev and Anna Mkusheva

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

Exam. Econometrics - Exam 1

Exam. Econometrics - Exam 1 Econometrcs - Exam 1 Exam Problem 1: (15 ponts) Suppose that the classcal regresson model apples but that the true value of the constant s zero. In order to answer the followng questons assume just one

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have

More information

a. (All your answers should be in the letter!

a. (All your answers should be in the letter! Econ 301 Blkent Unversty Taskn Econometrcs Department of Economcs Md Term Exam I November 8, 015 Name For each hypothess testng n the exam complete the followng steps: Indcate the test statstc, ts crtcal

More information

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

Panel cointegration rank test with cross-section dependence

Panel cointegration rank test with cross-section dependence Panel contegraton rank test wth cross-secton dependence Josep Lluís Carron--Slvestre Laura Surdeanu y AQR-IREA Research Group Department of Econometrcs, Statstcs and Spansh Economy Unversty of Barcelona

More information

PANEL UNIT ROOT TESTS UNDER CROSS-SECTIONAL DEPENDENCE: AN OVERVIEW

PANEL UNIT ROOT TESTS UNDER CROSS-SECTIONAL DEPENDENCE: AN OVERVIEW Journal of Statstcs: Advances n Theory and Applcatons Volume, Number 2, 2009, Pages 7-58 PANEL UNIT ROOT TESTS UNDER CROSS-SECTIONAL DEPENDENCE: AN OVERVIEW LAURA BARBIERI Dpartmento d Scenze Economche

More information

Chapter 11: Simple Linear Regression and Correlation

Chapter 11: Simple Linear Regression and Correlation Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Experment-I MODULE VII LECTURE - 3 ANALYSIS OF COVARIANCE Dr Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Any scentfc experment s performed

More information

Limited Dependent Variables and Panel Data. Tibor Hanappi

Limited Dependent Variables and Panel Data. Tibor Hanappi Lmted Dependent Varables and Panel Data Tbor Hanapp 30.06.2010 Lmted Dependent Varables Dscrete: Varables that can take onl a countable number of values Censored/Truncated: Data ponts n some specfc range

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Experment-I MODULE VIII LECTURE - 34 ANALYSIS OF VARIANCE IN RANDOM-EFFECTS MODEL AND MIXED-EFFECTS EFFECTS MODEL Dr Shalabh Department of Mathematcs and Statstcs Indan

More information

Professor Chris Murray. Midterm Exam

Professor Chris Murray. Midterm Exam Econ 7 Econometrcs Sprng 4 Professor Chrs Murray McElhnney D cjmurray@uh.edu Mdterm Exam Wrte your answers on one sde of the blank whte paper that I have gven you.. Do not wrte your answers on ths exam.

More information

An (almost) unbiased estimator for the S-Gini index

An (almost) unbiased estimator for the S-Gini index An (almost unbased estmator for the S-Gn ndex Thomas Demuynck February 25, 2009 Abstract Ths note provdes an unbased estmator for the absolute S-Gn and an almost unbased estmator for the relatve S-Gn for

More information

Chapter 8 Indicator Variables

Chapter 8 Indicator Variables Chapter 8 Indcator Varables In general, e explanatory varables n any regresson analyss are assumed to be quanttatve n nature. For example, e varables lke temperature, dstance, age etc. are quanttatve n

More information

Testing for PPP in the mean-group panel rgression framework: further evidence. Abstract

Testing for PPP in the mean-group panel rgression framework: further evidence. Abstract Testng for PPP n the mean-group panel rgresson framework: further evdence Abdullah oman School of Busness Studes, Southeast Unversy, Bangladesh Abstract The paper nvestgates the valdy of PPP by usng 15

More information

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition)

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition) Count Data Models See Book Chapter 11 2 nd Edton (Chapter 10 1 st Edton) Count data consst of non-negatve nteger values Examples: number of drver route changes per week, the number of trp departure changes

More information

ECONOMETRICS - FINAL EXAM, 3rd YEAR (GECO & GADE)

ECONOMETRICS - FINAL EXAM, 3rd YEAR (GECO & GADE) ECONOMETRICS - FINAL EXAM, 3rd YEAR (GECO & GADE) June 7, 016 15:30 Frst famly name: Name: DNI/ID: Moble: Second famly Name: GECO/GADE: Instructor: E-mal: Queston 1 A B C Blank Queston A B C Blank Queston

More information

Chapter 5 Multilevel Models

Chapter 5 Multilevel Models Chapter 5 Multlevel Models 5.1 Cross-sectonal multlevel models 5.1.1 Two-level models 5.1.2 Multple level models 5.1.3 Multple level modelng n other felds 5.2 Longtudnal multlevel models 5.2.1 Two-level

More information

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics ECOOMICS 35*-A Md-Term Exam -- Fall Term 000 Page of 3 pages QUEE'S UIVERSITY AT KIGSTO Department of Economcs ECOOMICS 35* - Secton A Introductory Econometrcs Fall Term 000 MID-TERM EAM ASWERS MG Abbott

More information

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding Recall: man dea of lnear regresson Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 8 Lnear regresson can be used to study an

More information

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 008 Recall: man dea of lnear regresson Lnear regresson can be used to study

More information

Lecture 4 Hypothesis Testing

Lecture 4 Hypothesis Testing Lecture 4 Hypothess Testng We may wsh to test pror hypotheses about the coeffcents we estmate. We can use the estmates to test whether the data rejects our hypothess. An example mght be that we wsh to

More information

Chapter 12 Analysis of Covariance

Chapter 12 Analysis of Covariance Chapter Analyss of Covarance Any scentfc experment s performed to know somethng that s unknown about a group of treatments and to test certan hypothess about the correspondng treatment effect When varablty

More information

III. Econometric Methodology Regression Analysis

III. Econometric Methodology Regression Analysis Page Econ07 Appled Econometrcs Topc : An Overvew of Regresson Analyss (Studenmund, Chapter ) I. The Nature and Scope of Econometrcs. Lot s of defntons of econometrcs. Nobel Prze Commttee Paul Samuelson,

More information

EXAMINATION. N0028N Econometrics. Luleå University of Technology. Date: (A1016) Time: Aid: Calculator and dictionary

EXAMINATION. N0028N Econometrics. Luleå University of Technology. Date: (A1016) Time: Aid: Calculator and dictionary EXAMINATION Luleå Unversty of Technology N008N Econometrcs Date: 011-05-16 (A1016) Tme: 09.00-13.00 Ad: Calculator and dctonary Teacher on duty (complete telephone number) Robert Lundmark (070-1735788)

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 14 Multiple Regression Models

Statistics for Managers Using Microsoft Excel/SPSS Chapter 14 Multiple Regression Models Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 14 Multple Regresson Models 1999 Prentce-Hall, Inc. Chap. 14-1 Chapter Topcs The Multple Regresson Model Contrbuton of Indvdual Independent Varables

More information

Chapter 3. Two-Variable Regression Model: The Problem of Estimation

Chapter 3. Two-Variable Regression Model: The Problem of Estimation Chapter 3. Two-Varable Regresson Model: The Problem of Estmaton Ordnary Least Squares Method (OLS) Recall that, PRF: Y = β 1 + β X + u Thus, snce PRF s not drectly observable, t s estmated by SRF; that

More information

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 13 The Smple Lnear Regresson Model and Correlaton 1999 Prentce-Hall, Inc. Chap. 13-1 Chapter Topcs Types of Regresson Models Determnng the Smple Lnear

More information

Chapter 15 - Multiple Regression

Chapter 15 - Multiple Regression Chapter - Multple Regresson Chapter - Multple Regresson Multple Regresson Model The equaton that descrbes how the dependent varable y s related to the ndependent varables x, x,... x p and an error term

More information

DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR. Introductory Econometrics 1 hour 30 minutes

DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR. Introductory Econometrics 1 hour 30 minutes 25/6 Canddates Only January Examnatons 26 Student Number: Desk Number:...... DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR Department Module Code Module Ttle Exam Duraton

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

This column is a continuation of our previous column

This column is a continuation of our previous column Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard

More information

Productivity and Reallocation

Productivity and Reallocation Productvty and Reallocaton Motvaton Recent studes hghlght role of reallocaton for productvty growth. Market economes exhbt: Large pace of output and nput reallocaton wth substantal role for entry/ext.

More information

LM-type tests for slope homogeneity in panel data models

LM-type tests for slope homogeneity in panel data models LM-type tests for slope homogenety n panel data models Jörg Bretung Unversty of Cologne Chrstoph Rolng Deutsche Bundesbank azar Salsh BGSE, Unversty of Bonn. July 4, 206 Abstract Ths paper employs the

More information

STAT 511 FINAL EXAM NAME Spring 2001

STAT 511 FINAL EXAM NAME Spring 2001 STAT 5 FINAL EXAM NAME Sprng Instructons: Ths s a closed book exam. No notes or books are allowed. ou may use a calculator but you are not allowed to store notes or formulas n the calculator. Please wrte

More information

ECONOMETRICS II (ECO 2401S) University of Toronto. Department of Economics. Winter 2017 Instructor: Victor Aguirregabiria

ECONOMETRICS II (ECO 2401S) University of Toronto. Department of Economics. Winter 2017 Instructor: Victor Aguirregabiria ECOOMETRICS II ECO 40S Unversty of Toronto Department of Economcs Wnter 07 Instructor: Vctor Agurregabra SOLUTIO TO FIAL EXAM Tuesday, Aprl 8, 07 From :00pm-5:00pm 3 hours ISTRUCTIOS: - Ths s a closed-book

More information

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands Content. Inference on Regresson Parameters a. Fndng Mean, s.d and covarance amongst estmates.. Confdence Intervals and Workng Hotellng Bands 3. Cochran s Theorem 4. General Lnear Testng 5. Measures of

More information

January Examinations 2015

January Examinations 2015 24/5 Canddates Only January Examnatons 25 DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR STUDENT CANDIDATE NO.. Department Module Code Module Ttle Exam Duraton (n words)

More information

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution Department of Statstcs Unversty of Toronto STA35HS / HS Desgn and Analyss of Experments Term Test - Wnter - Soluton February, Last Name: Frst Name: Student Number: Instructons: Tme: hours. Ads: a non-programmable

More information

Asymptotic Properties of the Jarque-Bera Test for Normality in General Autoregressions with a Deterministic Term

Asymptotic Properties of the Jarque-Bera Test for Normality in General Autoregressions with a Deterministic Term Asymptotc Propertes of the Jarque-Bera est for Normalty n General Autoregressons wth a Determnstc erm Carlos Caceres Nuffeld College, Unversty of Oxford May 2006 Abstract he am of ths paper s to analyse

More information

Linear Regression Analysis: Terminology and Notation

Linear Regression Analysis: Terminology and Notation ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented

More information

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore Sesson Outlne Introducton to classfcaton problems and dscrete choce models. Introducton to Logstcs Regresson. Logstc functon and Logt functon. Maxmum Lkelhood Estmator (MLE) for estmaton of LR parameters.

More information

Economics 130. Lecture 4 Simple Linear Regression Continued

Economics 130. Lecture 4 Simple Linear Regression Continued Economcs 130 Lecture 4 Contnued Readngs for Week 4 Text, Chapter and 3. We contnue wth addressng our second ssue + add n how we evaluate these relatonshps: Where do we get data to do ths analyss? How do

More information

A Wald test of restrictions on the cointegrating space based on Johansen s estimator

A Wald test of restrictions on the cointegrating space based on Johansen s estimator Economcs Letters 59 (998) 83 87 A Wald test of restrctons on the contegratng space based on Johansen s estmator James Davdson* Cardff Busness School, Colum Drve, Cardff CF 3EU, UK Receved 3 July 997; accepted

More information

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9 Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,

More information

UNR Joint Economics Working Paper Series Working Paper No Further Analysis of the Zipf Law: Does the Rank-Size Rule Really Exist?

UNR Joint Economics Working Paper Series Working Paper No Further Analysis of the Zipf Law: Does the Rank-Size Rule Really Exist? UNR Jont Economcs Workng Paper Seres Workng Paper No. 08-005 Further Analyss of the Zpf Law: Does the Rank-Sze Rule Really Exst? Fungsa Nota and Shunfeng Song Department of Economcs /030 Unversty of Nevada,

More information

Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 212. Chapters 14, 15 & 16. Professor Ahmadi, Ph.D. Department of Management

Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 212. Chapters 14, 15 & 16. Professor Ahmadi, Ph.D. Department of Management Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 1 Chapters 14, 15 & 16 Professor Ahmad, Ph.D. Department of Management Revsed August 005 Chapter 14 Formulas Smple Lnear Regresson Model: y =

More information

Aid, Debt Interest Repayments and Dutch Disease Effects in a Real Exchange Rate. Model for a Panel of African Countries ABSTRACT

Aid, Debt Interest Repayments and Dutch Disease Effects in a Real Exchange Rate. Model for a Panel of African Countries ABSTRACT Ad, Debt Interest Repayments and Dutch Dsease Effects n a Real Exchange Rate Model for a Panel of Afrcan Countres ABSTRACT The nternatonal compettveness of developng countres s an mportant factor n ther

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

Stock Market Development and Economic Growth an Empirical Analysis

Stock Market Development and Economic Growth an Empirical Analysis Amercan Journal of Economcs and Busness Admnstraton, 202, 4 (2), 35-43 ISSN: 945-5488 202 Scence Publcaton do:0.3844/ajebasp.202.35.43 Publshed Onlne 4 (2) 202 (http://www.thescpub.com/ajeba.toc) Stock

More information

Adaptive Estimation of Heteroscedastic Linear Regression Models Using Heteroscedasticity Consistent Covariance Matrix

Adaptive Estimation of Heteroscedastic Linear Regression Models Using Heteroscedasticity Consistent Covariance Matrix ISSN 1684-8403 Journal of Statstcs Volume 16, 009, pp. 8-44 Adaptve Estmaton of Heteroscedastc Lnear Regresson Models Usng Heteroscedastcty Consstent Covarance Matrx Abstract Muhammad Aslam 1 and Gulam

More information

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA 4 Analyss of Varance (ANOVA) 5 ANOVA 51 Introducton ANOVA ANOVA s a way to estmate and test the means of multple populatons We wll start wth one-way ANOVA If the populatons ncluded n the study are selected

More information

Basically, if you have a dummy dependent variable you will be estimating a probability.

Basically, if you have a dummy dependent variable you will be estimating a probability. ECON 497: Lecture Notes 13 Page 1 of 1 Metropoltan State Unversty ECON 497: Research and Forecastng Lecture Notes 13 Dummy Dependent Varable Technques Studenmund Chapter 13 Bascally, f you have a dummy

More information

Statistics for Business and Economics

Statistics for Business and Economics Statstcs for Busness and Economcs Chapter 11 Smple Regresson Copyrght 010 Pearson Educaton, Inc. Publshng as Prentce Hall Ch. 11-1 11.1 Overvew of Lnear Models n An equaton can be ft to show the best lnear

More information

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results.

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results. Neural Networks : Dervaton compled by Alvn Wan from Professor Jtendra Malk s lecture Ths type of computaton s called deep learnng and s the most popular method for many problems, such as computer vson

More information

Basic Business Statistics, 10/e

Basic Business Statistics, 10/e Chapter 13 13-1 Basc Busness Statstcs 11 th Edton Chapter 13 Smple Lnear Regresson Basc Busness Statstcs, 11e 009 Prentce-Hall, Inc. Chap 13-1 Learnng Objectves In ths chapter, you learn: How to use regresson

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 31 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 6. Rdge regresson The OLSE s the best lnear unbased

More information

Credit Card Pricing and Impact of Adverse Selection

Credit Card Pricing and Impact of Adverse Selection Credt Card Prcng and Impact of Adverse Selecton Bo Huang and Lyn C. Thomas Unversty of Southampton Contents Background Aucton model of credt card solctaton - Errors n probablty of beng Good - Errors n

More information

Online Appendix to: Axiomatization and measurement of Quasi-hyperbolic Discounting

Online Appendix to: Axiomatization and measurement of Quasi-hyperbolic Discounting Onlne Appendx to: Axomatzaton and measurement of Quas-hyperbolc Dscountng José Lus Montel Olea Tomasz Strzaleck 1 Sample Selecton As dscussed before our ntal sample conssts of two groups of subjects. Group

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

Nonlinear IV unit root tests in panels with cross-sectional dependency

Nonlinear IV unit root tests in panels with cross-sectional dependency Journal of Econometrcs 11 (22) 261 292 www.elsever.com/locate/econbase Nonlnear IV unt root tests n panels wth cross-sectonal dependency Yoosoon Chang Department of Economcs-MS22, Rce Unversty, 61 Man

More information

BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu

BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS M. Krshna Reddy, B. Naveen Kumar and Y. Ramu Department of Statstcs, Osmana Unversty, Hyderabad -500 007, Inda. nanbyrozu@gmal.com, ramu0@gmal.com

More information

Mean Reversion of Inflation Rates: Evidence from 13 OECD countries. Hsiu-Yun Lee and Jyh-Lin Wu *

Mean Reversion of Inflation Rates: Evidence from 13 OECD countries. Hsiu-Yun Lee and Jyh-Lin Wu * Mean Reverson of Inflaton Rates: Evdence from 3 OECD countres Hsu-Yun Lee and Jyh-Ln Wu Department of Economcs Natonal Chung Cheng Unversy Cha-Y 62, Tawan Abstract The statonary of nflaton has several

More information

Discussion of Extensions of the Gauss-Markov Theorem to the Case of Stochastic Regression Coefficients Ed Stanek

Discussion of Extensions of the Gauss-Markov Theorem to the Case of Stochastic Regression Coefficients Ed Stanek Dscusson of Extensons of the Gauss-arkov Theorem to the Case of Stochastc Regresson Coeffcents Ed Stanek Introducton Pfeffermann (984 dscusses extensons to the Gauss-arkov Theorem n settngs where regresson

More information

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 9: Statistical Inference and the Relationship between Two Variables Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,

More information

The Granular Origins of Aggregate Fluctuations : Supplementary Material

The Granular Origins of Aggregate Fluctuations : Supplementary Material The Granular Orgns of Aggregate Fluctuatons : Supplementary Materal Xaver Gabax October 12, 2010 Ths onlne appendx ( presents some addtonal emprcal robustness checks ( descrbes some econometrc complements

More information

Tests of Single Linear Coefficient Restrictions: t-tests and F-tests. 1. Basic Rules. 2. Testing Single Linear Coefficient Restrictions

Tests of Single Linear Coefficient Restrictions: t-tests and F-tests. 1. Basic Rules. 2. Testing Single Linear Coefficient Restrictions ECONOMICS 35* -- NOTE ECON 35* -- NOTE Tests of Sngle Lnear Coeffcent Restrctons: t-tests and -tests Basc Rules Tests of a sngle lnear coeffcent restrcton can be performed usng ether a two-taled t-test

More information

where I = (n x n) diagonal identity matrix with diagonal elements = 1 and off-diagonal elements = 0; and σ 2 e = variance of (Y X).

where I = (n x n) diagonal identity matrix with diagonal elements = 1 and off-diagonal elements = 0; and σ 2 e = variance of (Y X). 11.4.1 Estmaton of Multple Regresson Coeffcents In multple lnear regresson, we essentally solve n equatons for the p unnown parameters. hus n must e equal to or greater than p and n practce n should e

More information

Lecture 2: Prelude to the big shrink

Lecture 2: Prelude to the big shrink Lecture 2: Prelude to the bg shrnk Last tme A slght detour wth vsualzaton tools (hey, t was the frst day... why not start out wth somethng pretty to look at?) Then, we consdered a smple 120a-style regresson

More information

On the testing of heterogeneity effects in dynamic unbalanced panel data models

On the testing of heterogeneity effects in dynamic unbalanced panel data models Economcs Letters 58 (1998) 157 163 On the testng of heterogenety effects n dynamc unbalanced panel data models Serg Jmenez-Martn* Unversdad Carlos III de Madrd epartment of Economcs, Av. Madrd, 16, 8903

More information

Household Size, Economies of Scale and Public Goods in Consumption: A Proposal to resolve the Food Share Paradox

Household Size, Economies of Scale and Public Goods in Consumption: A Proposal to resolve the Food Share Paradox 1 Household Sze, Economes of Scale and Publc Goods n Consumpton: A Proposal to resolve the Food Share Paradox Ferdoon Kooh-Kamal 2014* ferdoon.kooh@emory.edu, Department of Economcs, Emory Unversty 1602

More information

USE OF DOUBLE SAMPLING SCHEME IN ESTIMATING THE MEAN OF STRATIFIED POPULATION UNDER NON-RESPONSE

USE OF DOUBLE SAMPLING SCHEME IN ESTIMATING THE MEAN OF STRATIFIED POPULATION UNDER NON-RESPONSE STATISTICA, anno LXXV, n. 4, 015 USE OF DOUBLE SAMPLING SCHEME IN ESTIMATING THE MEAN OF STRATIFIED POPULATION UNDER NON-RESPONSE Manoj K. Chaudhary 1 Department of Statstcs, Banaras Hndu Unversty, Varanas,

More information

Chapter 14 Simple Linear Regression

Chapter 14 Simple Linear Regression Chapter 4 Smple Lnear Regresson Chapter 4 - Smple Lnear Regresson Manageral decsons often are based on the relatonshp between two or more varables. Regresson analss can be used to develop an equaton showng

More information

Statistical Evaluation of WATFLOOD

Statistical Evaluation of WATFLOOD tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth

More information

Here is the rationale: If X and y have a strong positive relationship to one another, then ( x x) will tend to be positive when ( y y)

Here is the rationale: If X and y have a strong positive relationship to one another, then ( x x) will tend to be positive when ( y y) Secton 1.5 Correlaton In the prevous sectons, we looked at regresson and the value r was a measurement of how much of the varaton n y can be attrbuted to the lnear relatonshp between y and x. In ths secton,

More information

Negative Binomial Regression

Negative Binomial Regression STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...

More information

A Robust Method for Calculating the Correlation Coefficient

A Robust Method for Calculating the Correlation Coefficient A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal

More information

STAT 3008 Applied Regression Analysis

STAT 3008 Applied Regression Analysis STAT 3008 Appled Regresson Analyss Tutoral : Smple Lnear Regresson LAI Chun He Department of Statstcs, The Chnese Unversty of Hong Kong 1 Model Assumpton To quantfy the relatonshp between two factors,

More information

A Simple Panel Unit Root Test in the Presence of Cross Section Dependence

A Simple Panel Unit Root Test in the Presence of Cross Section Dependence A Smple Panel Unt Root est n the Presence of Cross Secton Dependence M. Hashem Pesaran Cambrdge Unversty & USC September 3, Revsed January 5 Abstract A number of panel unt root tests that allow for cross

More information

Now we relax this assumption and allow that the error variance depends on the independent variables, i.e., heteroskedasticity

Now we relax this assumption and allow that the error variance depends on the independent variables, i.e., heteroskedasticity ECON 48 / WH Hong Heteroskedastcty. Consequences of Heteroskedastcty for OLS Assumpton MLR. 5: Homoskedastcty var ( u x ) = σ Now we relax ths assumpton and allow that the error varance depends on the

More information