Solutions Manual For Econometrics

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Solutions Manual for EconometricsSecond Edition

Badi H. BaltagiSolutions Manualfor EconometricsSecond Edition123

Professor Badi H. BaltagiCenter for Policy ResearchSyracuse University426 Eggers HallSyracuse, NY 13244-1020USAbbaltagi@maxwell.syr.eduISBN 978-3-642-03382-7e-ISBN 978-3-642-03383-4DOI: 10.1007/978-3-642-03383-4Springer Heidelberg Dordrecht London New YorkLibrary of Congress Control Number: 2009938020 Springer-Verlag Berlin Heidelberg 2010This work is subject to copyright. All rights are reserved, whether the whole or part of the materialis concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplicationof this publication or parts thereof is permitted only under the provisions of the German CopyrightLaw of September 9, 1965, in its current version, and permissions for use must always be obtainedfrom Springer. Violations are liable for prosecution under the German Copyright Law.The use of general descriptive names, registered names, trademarks, etc. in this publication doesnot imply, even in the absence of a specific statement, that such names are exempt from the relevantprotective laws and regulations and therefore free for general use.Cover design: WMXDesign GmbH, Heidelberg, GermanyPrinted on acid-free paperSpringer is part of Springer Science Business Media (www.springer.com)

PrefaceThis manual provides solutions to selected exercises from each chapter of the 4thedition of Econometrics by Badi H. Baltagi. Eviews and Stata as well as SASr programs are provided for the empirical exercises. Some of the problems and solutionsare obtained from Econometric Theory (ET) and these are reprinted with the permission of Cambridge University Press. I would like to thank Peter C.B. Phillips, andthe editors of the Problems and Solutions section, Alberto Holly, Juan Dolado andPaolo Paruolo for their useful service to the econometrics profession. I would alsolike to thank my colleague James M. Griffin for providing many empirical problemsand data sets. I have also used three empirical data sets from Lott and Ray (1992).The reader is encouraged to apply these econometric techniques to their own datasets and to replicate the results of published articles. Instructors and students areencouraged to get other data sets from the internet or journals that provide backupdata sets to published articles. The Journal of Applied Econometrics and the Journalof Business and Economic Statistics are two such journals. In fact, the Journal ofApplied Econometrics has a replication section for which I am serving as an editor.In my course I require my students to replicate an empirical paper.I would like to thank my students Wei-Wen Xiong, Ming-Jang Weng and KiseokNam who solved several of these exercises. I benefited from teaching this materialat Texas A&M University and Syracuse University as well as a visiting professor atUC-San Diego and University of Zurich.Please report any errors, typos or suggestions to: Badi H. Baltagi, Center for PolicyResearch and Department of Economics, Syracuse University, Syracuse, New York13244-1020, Telephone (315) 443-1630, Fax (315) 443-1081, or send Email to bbaltagi@ maxwell.syr.edu. My home page is www-cpr.maxwell.syr.edu/faculty/baltagi.DataThe data sets used in this text can be downloaded from the Springer website.The address is: http://www.springer.com/978-3-540-76515-8. Please check the link“Samples & Suplements” from the right hand column. There is also a readme filethat describes the contents of each data set and its source.v

ContentsChapter 1What is Econometrics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1Chapter 2A Review of Some Basic Statistical Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5Chapter 3Simple Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Chapter 4Multiple Regression Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Chapter 5Violations of the Classical Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Chapter 6Distributed Lags and Dynamic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119Chapter 7The General Linear Model: The Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Chapter 8Regression Diagnostics and Specification Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179Chapter 9Generalized Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207Chapter 10Seemingly Unrelated Regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223Chapter 11Simultaneous Equations Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247Chapter 12Pooling Time-Series of Cross-Section Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299vii

viiiContentsChapter 13Limited Dependent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317Chapter 14Time-Series Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343

CHAPTER 1What is Econometrics?This chapter emphasizes that an econometrician has to be a competent mathematicianand statistician who is an economist by training. It is the unification of statistics,economic theory and mathematics that constitutes econometrics. Each view point, byitself is necessary but not sufficient for a real understanding of quantitative relationsin modern economic life, see Frisch (1933).Econometrics aims at giving empirical content to economic relationships. The threekey ingredients are economic theory, economic data, and statistical methods. Neither‘theory without measurement’, nor ‘measurement without theory’ are sufficient forexplaining economic phenomena. It is as Frisch emphasized their union that is thekey for success in the future development of econometrics.Econometrics provides tools for testing economic laws, such as purchasing powerparity, the life cycle hypothesis, the wage curve, etc. These economic laws orhypotheses are testable with economic data. As David F. Hendry (1980) emphasized“The three golden rules of econometrics are test, test and test.”Econometrics also provides quantitative estimates of price and income elasticitiesof demand, returns to scale in production, technical efficiency in cost functions, wageelasticities, etc. These are important for policy decision making. Raising the tax ona pack of cigarettes by 10%, how much will that reduce consumption of cigarettes?How much will it generate in tax revenues?Econometrics also provides predictions or forecasts about future interest rates,unemployment, or GNP growth. As Lawrence Klein (1971) emphasized: “Econometrics should give a base for economic prediction beyond experience if it is to beuseful.”Data in economics are not generated under ideal experimental conditions as in aphysics laboratory. This data cannot be replicated and is most likely measured witherror. Most of the time the data collected are not ideal for the economic question athand. Griliches (1986, p.1466) describes economic data as the world that we wantto explain, and at the same time the source of all our trouble. The data’s imperfections makes the econometrician’s job difficult and sometimes impossible, yet theseimperfections are what gives econometricians their legitimacy.

2Badi BaltagiEven though economists are increasingly getting involved in collecting their dataand measuring variables more accurately and despite the increase in data sets anddata storage and computational accuracy, some of the warnings given by Griliches(1986, p. 1468) are still valid today:econometricians want too much from the data and hence tend to be disappointed by the answers, because the data are incomplete and imperfect. Inpart it is our fault, the appetite grows with eating. As we get larger samples,we keep adding variables and expanding our models, until on the margin, wecome back to the same insignificance levels.Pesaran (1990, pp. 25–26) also summarizes some of the limitations of econometrics:There is no doubt that econometrics is subject to important limitations, whichstem largely from the incompleteness of the economic theory and the nonexperimental nature of economic data. But these limitations should not distractus from recognizing the fundamental role that econometrics has come to playin the development of economics as a scientific discipline. It may not be possible conclusively to reject economic theories by means of econometric methods,but it does not mean that nothing useful can be learned from attempts at testingparticular formulations of a given theory against (possible) rival alternatives.Similarly, the fact that econometric modelling is inevitably subject to the problem of specification searches does not mean that the whole activity is pointless.Econometric models are important tools for forecasting and policy analysis,and it is unlikely that they will be discarded in the future. The challenge is torecognize their limitations and to work towards turning them into more reliableand effective tools. There seem to be no viable alternatives.Econometrics have experienced phenomenal growth in the past 50 years. There aresix volumes of the Handbook of Econometrics, most of it dealing with post 1960’sresearch. A lot of the recent growth reflects the rapid advances in computing technology. The broad availability of micro data bases is a major advance which facilitatedthe growth of panel data methods (see Chapter 12) and microeconometric methodsespecially on sample selection and discrete choice (see Chapter 13) and that alsolead to the award of the Nobel Prize in Economics to James Heckman and DanielMcFadden in 2000. The explosion in research in time series econometrics which

Chapter 1: What is Econometrics?3lead to the development of ARCH and GARCH and cointegration (see Chapter 14)which also lead to the award of the Nobel Prize in Economics to Clive Granger andRobert Engle in 2003.The challenge for the 21st century is to narrow the gap between theory and practice. Many feel that this gap has been widening with theoretical research growingmore and more abstract and highly mathematical without an application in sight ora motivation for practical use. Heckman (2001) argues that econometrics is usefulonly if it helps economists conduct and interpret empirical research on economicdata. He warns that the gap between econometric theory and empirical practice hasgrown over the past two decades. Theoretical econometrics becoming more closelytied to mathematical statistics. Although he finds nothing wrong, and much potentialvalue, in using methods and ideas from other fields to improve empirical work ineconomics, he does warn of the risks involved in uncritically adopting the methodsand mind set of the statisticians:Econometric methods uncritically adapted from statistics are not useful inmany research activities pursued by economists. A theorem-proof format ispoorly suited for analyzing economic data, which requires skills of synthesis,interpretation and empirical investigation. Command of statistical methods isonly a part, and sometimes a very small part, of what is required to do firstclass empirical research.The recent entry by Geweke, Horowitz, and Pesaran (2008) in the The NewPalgrave Dictionary provides the following recommendations for the future:Econometric theory and practice seek to provide information required forinformed decision-making in public and private economic policy. This processis limited not only by the adequacy of econometrics, but also by the development of economic theory and the adequacy of data and other information.Effective progress, in the future as in the past, will come from simultaneous improvements in econometrics, economic theory, and data. Research thatspecifically addresses the effectiveness of the interface between any two ofthese three in improving policy — to say nothing of all of them — necessarily transcends traditional subdisciplinary boundaries within economics. But itis precisely these combinations that hold the greatest promise for the socialcontribution of academic economics.

4Badi BaltagiFor a recent world wide ranking of econometricians as well as academic institutionsin the field of econometrics, see Baltagi (2007).ReferencesBaltagi, B.H. (2007), “Worldwide Econometrics Rankings: 1989–2005,” Econometric Theory, 23: 952–1012.Frisch, R. (1933), “Editorial,” Econometrica, 1: 1–14.Geweke, J., J. Horowitz, and M. H. Pesaran (2008), “Econometrics,” The NewPalgrave Dictionary of Economics. Second Edition. Eds. Steven N. Durlauf andLawrence E. Blume. Palgrave Macmillan.Griliches, Z. (1986), “Economic Data Issues,” in Z. Griliches and M.D. Intriligator(eds), Handbook of Econometrics Vol. III (North Holland: Amsterdam).Heckman, J.J. (2001), “Econometrics and Empirical Economics,” Journal ofEconometrics, 100: 3–5.Hendry, D.F. (1980), “Econometrics - Alchemy or Science?” Economica, 47: 387–406.Klein, L.R. (1971), “Whither Econometrics?” Journal of the American StatisticalAssociation, 66: 415–421.Pesaran, M.H. (1990), “Econometrics,” in J. Eatwell, M. Milgate and P. Newman;The New Palgrave: Econometrics (W.W. Norton and Company: New York).

CHAPTER 2A Review of Some Basic Statistical Concepts2.1 Variance and Covariance of Linear Combinations of Random Variables.a. Let Y D a C bX, then E.Y/ D E.a C bX/ D a C bE.X/. Hence,var.Y/ D EŒY E.Y/ 2 D EŒa C bX a bE.X/ 2 D EŒb.X E.X// 2D b2 EŒX E.X/ 2 D b2 var.X/.Only the multiplicative constant b matters for the variance, not the additiveconstant a.b. Let Z D a C bX C cY, then E.Z/ D a C bE.X/ C cE.Y/ andvar.Z/ D EŒZ E.Z/ 2 D EŒa C bX C cY a bE.X/ cE.Y/ 2D EŒb.X E.X// C c.Y E.Y// 2D b2 EŒX E.X/ 2 Cc2 EŒY E.Y/ 2 C2bc EŒX E.X/ ŒY E.Y/ D b2 var.X/ C c2 var.Y/ C 2bc cov.X, Y/.c. Let Z D aCbXCcY, and W D dCeXCfY, then E.Z/ D aCbE.X/CcE.Y/E.W/ D d C eE.X/ C fE.Y/andcov.Z, W/ D EŒZ E.Z/ ŒW E.W/ D EŒb.X E.X//Cc.Y E.Y// Œe.X E.X//Cf.Y E.Y// D be var.X/ C cf var.Y/ C .bf C ce/ cov.X, Y/.2.2 Independence and Simple Correlation.a. Assume that X and Y are continuous random variables. The proof is similarif X and Y are discrete random variables and is left to the reader. If X andY are independent, then f.x, y/ D f1 .x/f2 .y/ where f1 .x/ is the marginalprobability density function (p.d.f.) of X and f2 .y/ is the marginal p.d.f. ofY. In this case,’’E.XY/ D xyf.x, y/dxdy D xyf1 .x/f2 .y/dxdyRRD . xf1 .x/dx/. yf2 .y/dy/ D E.X/E.Y/

6Badi BaltagiHence,cov.X, Y/ D EŒX E.X/ ŒY E.Y/ D E.XY/ E.X/E.Y/D E.X/E.Y/ E.X/E.Y/ D 0.b. If Y D a C bX, then E.Y/ D a C bE.X/ and cov.X, Y/ D EŒX E.X/ ŒY E.Y/ D EŒX E.X/ Œa C bX a bE.X/ D b var.X/ which takes thesign of b since var(X) is always positive. Hence,cov.X, Y/b var.X/correl.X, y/ D ¡xy D pD pvar.X/var.Y/var.X/var.Y/but var.Y/ D b2 var.X/ from problem 2.1a. Hence, ¡XY D p b2var.X/ 1 depending on the sign of b.2.3 Zero Covariance Does Not Necessarily Imply Independence.P(X)X 2 1/5 1 1/50 1/51 1/52 1/52XE.X/ DXP.X/ DXD 2E.X2 / D2X1Œ. 2/ C . 1/ C 0 C 1 C 2 D 05X2 P.X/ DXD 21Œ4 C 1 C 0 C 1 C 4 D 25and var.X/ D 2. For Y D X2 , E.Y/ D E.X2 / D 2 andE.X3 / D2XXD 2X3 P.X/ D1Œ. 2/3 C . 1/3 C 0 C 13 C 23 D 05In fact, any odd moment of X is zero. Therefore,E.YX/ D E.X2 .X/ D E.X3 / D 0b .var.X//2D

Chapter 2: A Review of Some Basic Statistical Concepts7andcov.Y, X/ D E.X E.X//.Y E.Y// D E.X 0/.Y 2/D E.XY/ 2E.X/ D E.XY/ D E.X3 / D 0Hence, ¡XY Dp cov.X,Y/var.X/var.Y/D 0.2.4 The Binomial Distribution.a. PrŒX D 5 or 6 D PrŒX D 5 C PrŒX D 6 D b.n D 20, X D 5, D 0.1/ C b.n D 20, X D 6, D 0.1/!!2020515D.0.1/ .0.9/ C.0.1/6 .0.9/1456D 0.0319 C 0.0089 D 0.0408.This can be easily done with a calculator, on the computer or using theBinomial tables, see Freund (1992).!!nnnŠnŠb.D .n X/Š.n nCX/Š D .n X/ŠXŠ DXn XHence,!nb.n, n X, 1 / D.1 /n X .1 1 C /n nCXn XnD .1 /n X X D b.n, X, /.Xc. Using the MGF for the Binomial distribution given in problem 2.14a, we getMX .t/ D Œ.1 / C et n .Differentiating with respect to t yields M0X .t/ D nŒ.1 / C et n 1 et .Therefore, M0X .0/ D n D E.X/.Differentiating M0X .t/ again with respect to t yieldsM00X .t/ D n.n 1/Œ.1 / C et n 2 . et /2 C nŒ.1 / C et n 1 et .Therefore M00X .0/ D n.n 1/ 2 C n D E.X2 /.

8Badi BaltagiHence var.X/ D E.X2 / .E.X//2 D n C n2 2 n 2 n2 2 D n .1 /.nPXb.n, X, /. This entails factorialAn alternative proof for E.X/ DXD0moments and the reader is referred to Freund (1992).d. The likelihood function is given bynPXin nPXi.1 /L. / D f .X1 , ., Xn ; / D nnPPso that log L. / DXi log C n Xi log.1 /iD1iD1nP@ log L. /D@ n XiiD1 iD1nPiD1 XiiD1D 0.1 /Solving for one getsnXXi iD1nXXi n C iD1nXXi D 0iD1nPNXi n D X.so that O mle DN De. E.X/nPiD1E.Xi / n D n n D .iD1N D var.Xi / n D .1 / nHence, XN is unbiased for . Also, var.X/which goes to zero as n ! 1. Hence, the sufficient condition for XN to beconsistent for is satisfied.f. The joint probability function in part d can be written asNNN /g.X1 , : : : , Xn /f.X1 , ., Xn; / D nX .1 /n nX D h.X,N / D nXN .1 /n nXN and g.X1 , ., Xn / D 1 for all Xi 0 s. The latterwhere h.X,function is independent of in form and domain. Hence, by the factorizationN is sufficient for .theorem, XN was shown to be MVU for for the Bernoulli case in Example 2 ing. Xthe text.nPh. From part (d), L.0.2/ D .0.2/.0.4/n nPiD1XiiD1Xi.0.8/with the likelihood ration L.0.2/L.0.6/nPXinPwhile L.0.6/ D .0.6/nnPP 1 Xi n XiD 3 iD1 2 iD1iD1iD1Xi

Chapter 2: A Review of Some Basic Statistical Concepts9The uniformly most powerful critical region C of size ’ 0.05 is given bynnPP 1 Xi n XiiD1iD12 k inside C. Taking logarithms of both sides3!nnXX Xi .log 3/ C n Xi log 2 log kiD1solving nX!iD1Xi log 6 K0oriD1nXXi KiD1where K is determined by making the size of C D ’ 0.05. In this case,nnPPXi b.n, / and under Ho ; D 0.2. Therefore,Xi b.n D 20, DiD1iD10.2/. Hence, ’ D PrŒb.n D 20, D 0.2/ K 0.05.From the Binomial tables for n D 20 and D 0.2, K D 7 gives PrŒb.n D 20,nPXi 7 is our required critical region. D 0.2/ 7 D 0.0322. Hence,iD1i. The likelihood ratio test isnPXin nPXi.0.2/iD1 .0.8/ iD1L.0.2/DnnPPL. O mle / Xi n XiNNiD1iD1X1 Xso that LR D 2 log L.0.2/ C 2 log L. O mle /!##"" nnXXNN 2 n .Xi .log 0.2 log X/Xi .log 0.8 log.1 X//D 2iD1iD1This is given in Example 5 in the text for a general o . The Wald statistic isN 0.2/2.Xgiven by W DN X/ nNX.1N 0.2/2.Xand the LM statistic is given by LM D.0.2/.0.8/ nAlthough, the three statistics, LR, LM and W look different, they are allN 2j k and for a finite n, the same exact critical value couldbased on jXbe obtained from the binomial distribution.2.5 d. The Wald, LR, and LM Inequality. This is based on Baltagi (1995). Thelikelihood is given by equation (2.1) in the text.nP .1 2 2 /.Xi /2 iD1L , 2 D .1 2 2/n 2 e(1)

10Badi BaltagiIt is easy to show that the score is given by01Nn.X /2 nA,S , 2 D @ P.Xi /2 n 2(2)iD12 4N and O 2 DO DXand setting S. , 2 / D 0 yields Q DnXN 2 n. Under.Xi X/iD1Q D 0 andHo , 2nP.Xi 0 /2 n.iD1Therefore, nnnlog L ,Q Q 2 D log Q 2 log 2 222(3)and nnnlog L ,O O 2 D

Chapter 1: What is Econometrics? 3 lead to the development of ARCH and GARCH and cointegration (see Chapter 14) which also lead to the award of the Nobel Prize in Economics to Clive Granger and RobertEngle in 2003. The challenge for the 21st century is to narrow the gap between theory and prac-tice.

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