Solutions Manual for Econometrics by Baltagi, Badi

The Econometrics Solution Manual from Baltagi is our recommended solution manual for students of Econometrics who study with our instructors for online courses in Econometrics. This tutorial introductions the the Third Edition updates the "Solutions Manual for Econometrics" to match the Fifth Edition of the Econometrics textbook by Baltagi. It adds problems and solutions using latest software versions of Stata and EViews. Special features include empirical examples using EViews and Stata. The book offers rigorous proofs and treatment of difficult econometrics concepts in a simple and clear way, and it provides the reader with both applied and theoretical econometrics problems along with their solutions. You can request complete Econometrics Solution Manuals for any book in Econometrics from our top freelancers in Econometrics. Submit your questions here for Answers.

About the Author of Solutions Manual for Econometrics

Badi H. Baltagi is distinguished Professor of Economics and Senior Research Associate at the Center for Policy Research, Syracuse University. He received his Ph.D. in Economics at the University of Pennsylvania in 1979. Before joining Syracuse University, he served on the faculty at the University of Houston and Texas A & M University. He is a fellow of the Journal of Econometrics and a recipient of the Multa and Plura Scripsit Awards from Econometric Theory.

Preface for Econometrics Solution Manual

This Econometrics Solution Manual provides solutions to selected exercises from each chapter of the fifth edition of Econometrics by Badi H. Baltagi.1 Eviews and Stata as well as SASr programs are provided for the empirical exercises. Some of the problems and solutions are 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, and past editors of the Problems and Solutions section, Alberto Holly, Juan Dolado and Paolo Paruolo for their useful service to the econometrics profession. I would also like to thank my colleague (from Texas A&M) James M. Griffin for providing many empirical problems and 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 data sets and to replicate the results of published articles. Instructors and students are encouraged to get other data sets from the Internet or journals that provide
backup data sets to published articles. The Journal of Applied Econometrics and the American Economic Review are two such journals. In fact, the Journal of Applied 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, Kiseok Nam, Dong Li, Gustavo Sanchez, Long Liu and Liu Tian who solved several of the exercises. I would also like to thank Martina Bihn at Springer for her continuous support and professional editorial help.

Please report any errors, typos or suggestions to: Badi H. Baltagi, Center for Policy Research and Department of Economics, Syracuse University, Syracuse, New York 13244-1020, Telephone (315) 443-1630, Fax (315) 443-1081, or send Email to My home page is

Stochastic frontier models using Stata

Stochastic Frontier Models using Stata

How to run Stochastic Frontiers Models using Stata has been a core question commonly asked around in addition to the basic question of when to apply Stochastic Frontier Models. In this simple tutorial and related freelance project support, we help our students to run stochastic frontier models using Stata

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Stochastic Frontier Models using Stata

We can estimate stochastic frontier models based on panel data and cross sectional data using Stata. There variety of menu driven tools in Stata and user written ado's in Stata to help us estimate stochastic frontier models. In the following, we describe the most common tools.
Stochastic Frontier Models using Stata

Stochastic Frontier Models for Panel Data

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Stochastic Frontier Models using Stata

Stochastic Frontier Models for Cross Sectional Data

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Learn Econometrics Easily

Learn Econometrics Easily

We will share our tips and tricks to help our students and community to learn econometrics easily. The tips include how to learn, how to practice and how to find answers to any question more specifically and then comprehend technical details with simple bits of comprehension.

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6 Steps to Master Econometrics

Econometrics becomes too complex when the students lack technical background in mathematics and statistics. Also, it becomes hard to apply when basic learning of economics is weaker. It is therefore, our top recommendation to begin basic mathematical functions, data types, variables and economic theory to deduce hypothesis.

Select Your Objectives For Learning


If we begin reading randomly, the learning becomes less interesting. It is therefore highly recommended that begin developing a clear idea is to why we should learn Econometrics or that specific topic within the subject.

Prepare your tables of data beforehand


It is very helpful if you read your data carefully about what it contains in terms of variables and their nature without any care for formal types of variables we usually have to learn so just keep a good eye on data you have to use.

List down your specific learning outcomes


Listing down what you need to learn and what you already know about everything you are going to explore about is a golden rule that can help you minimize the time you need to learn everything more precisely and easily.
Explore the resources, books and notes to readREADING BOOKS IS A NEW WORLD AND ONLY INSPIRATION CONQUER IT

Once you determine your objectives and the list of what do you not know about anything, one can select a book, a chapter for reading or any other notes to quickly read and learn it. Econometrics can also be learnt this way easily.

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Advanced Econometric Modeling

Advanced Econometric Modeling

Advanced Econometric Modeling is an online and instructor led course by Muhammad Anees, Assistant Professor and Senior Econometrician at An Economist. The course aims at introducing the recent and recently appearing trends in Econometrics Methods and Application with real world examples to enable our young PhD scholars and faculty members to adopt and apply the methods.

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Course Objectives

Econometric Modeling

Understanding the mathematical theory behind modern econometric methods
Problems Solving

Identification of issues and problems in traditional econometric methods
Skills Development

Developing a research skill set to apply advanced econometric modeling
Writing Effectively

Writing effective research reports based on the evidence from world data analysis.
Statistical Softwares

Learning new software beyond conventional econometric tools like RATS, GAUSS and JMulti.
Independent Research

Become independent researchers in the area of Economics and Finance

Learning Outcomes

Understand Econometric Models

Application and Understanding of complex econometric methods to real world cases
Apply Econometrics Theory

Develop independent research skills based on application of relevant econometric modeling.
Estimation without Help

Estimate models with complexity without needs for further guidance and notes
Effective and High Impact Writing

Write effective research reports based on the estimated econometric models
Research Publication

Learn best practices in publication of high impact research and policy papers
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Predict and forecast economic and financial with any software using any data

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Selected Topics in Advanced Econometric Modeling
Unit Root, Structural Breaks, VAR, VECM, Structural VAR, Structural VECM and Structural Cointegration.
Nonlinear Unit Roots, Cointegration with Multiple Known Breaks, Nonlinear Cointegration
Asymmetric Unit Root, Causality with I(2) Variables and Causality and Causality with Structural VAR
Cointegration with Multiple Unknown Breaks, Causality Test with Multiple Unknown Breaks
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Wavelet Analysis of Economic and Financial Time Series and Economic Variables with High Frequency
Frequency Domain Analysis of Financial and Economic Time Series Data
Generalized SEM for Panel Data, Nonlinear SUREG, 3SLS Models and Nonlinear Equations and DSFE using Stata

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Cointegration Test Including Multiple Breaks Using GAUSS

In this tutorial on Cointegration Test Including Multiple Breaks Using GAUSS, we will show usng GUASS how to estimate cointegration tests with multiple breaks. Abdulnasser Hatemi-J has written an excellent paper titled: Tests for cointegration with two unknown regime shifts with an application to financial market integration which describes the methodology of Cointegration Test Including Multiple Breaks and we have used his written code available here. The abstract of the paper is quoted here:

It is widely agreed in empirical studies that allowing for potential structural change in economic processes is an important issue. In existing literature, tests for cointegration between time series data allow for one regime shift. This paper extends three residual-based test statistics for cointegration to the cases that take into account two possible regime shifts. The timing of each shift is unknown a priori and it is determined endogenously. The distributions of the tests are non-standard. We generate new critical values via simulation methods. The size and power properties of these test statistics are evaluated through Monte Carlo simulations, which show the tests have small size distortions and very good power properties. The test methods introduced in this paper are applied to determine whether the financial markets in the US and the UK are integrated.

Cointegration Test Including Multiple Breaks Using GAUSS can be replicated using the following video tutorial or simply follow the steps:

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Video on Cointegration Test Including Multiple Breaks Using GAUSS

Download Economic Data using Excel

In this video tutorial, we will explain how to Download Economic Data using Excel in one minute. The data can be downloaded from various sources like World Bank, International Monetary Fund and other data providers through Knoema Addin in Excel. The Knoema addin is one of the best and most easy tool to Download Economic Data using Excel. You can download economic, financial, trade, political and data on other related topics.

Download Economic Data using Excel

Follow these steps to Download Economic Data using Excel from Knoema using Excel.

The following video tutorial is only recorded to help you do the above practical in simple sessions. You can request us for custom download of any Economic and Financial data. We are happy to provide both free and paid support in making data availability from different sources. Watch the video tutorial now.

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Phillips-Ouliaris Cointegration Test

In this short tutorial in Eviews, we would explain the basic steps of conduting Phillips-Ouliaris Cointegration Test. To run Phillips-Ouliaris Cointegration Test in Eviews, we will need to ender the data as dated frequency and panel structure. Theoretically, the Phillips-Ouliaris Cointegration Test can found well explained in P. C. B. Phillips and S. Ouliaris (1990): Asymptotic Properties of Residual Based Tests for Cointegration. Econometrica 58, 165–193 which can be downloaded here.

Phillips-Ouliaris Cointegration Test

To run Phillips-Ouliaris Cointegration Test in Eviews, open Eviews and follow the steps below:

The following video tutorial explains the above steps to help replication be more convenient. If you would like to learn more about Applied Econometrics Research using Eviews, check our private and instructor led courses here.

Structural VAR using JMulti

Structural VAR using JMulti is A Video Tutorial By Econometrician during the online course in Applied Econometrics Research. In this video tutorial, we demonstrate the key steps in running Structural VAR using JMulti. The estimation and setting up of structural var is one of the main problem for all who wish to explore VAR models with structural changes in the system. Before following the steps, download JMulti here.

Structural VAR using JMulti

To estimate Structural VAR using JMulti, follow the steps:

It is assumed that basic theory of SVAR is clear to the users. To learn more and details of theory, one can request a complete Instructor Led Private Course in Applied Econometrics Research Here.

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Tests for Autocorrelated Errors

In ordinary least square regression model, we specify the equation as
y = b0 + b1 x1 + b2 x2 + b3 x3 + b4 x4 + ut
and we can test the assumption of autocorrelation or we can test whether the disturbances are autocorrelated.
To test the autocorrelation, we can follow the steps below:
(i) Estimate the regression model above using ordinary least square approach/OLS:
sysuse auto, clear
gen t=_n
tsset t
reg price rep78 trunk length
. reg price rep78 trunk length
Source |       SS           df       MS      Number of obs   =        69
-------------+----------------------------------   F(3, 65)        =      6.42
Model |   131790806         3  43930268.8   Prob > F        =    0.0007
Residual |   445006152        65   6846248.5   R-squared       =    0.2285
-------------+----------------------------------   Adj R-squared   =    0.1929
Total |   576796959        68  8482308.22   Root MSE        =    2616.5
price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
rep78 |   578.7949   348.6211     1.66   0.102    -117.4495    1275.039
trunk |  -31.78264   108.8869    -0.29   0.771    -249.2447    185.6794
length |   70.17701   22.01136     3.19   0.002     26.21729    114.1367
_cons |  -8596.181   3840.351    -2.24   0.029    -16265.89   -926.4697

(ii)  Now calculate the residuals from the above regression:
predict errors, res
(iii)  Run another regression by inserting lagged residuals or the lag values of error terms, (errors) as predicted from above regression model into a regression model of the residuals as a dependent variable. Our regression model will be estimated through the following regression code in Stata: reg errors rep78 trunk length l.errors. We can consider this regression as auxiliary regression. The results from this auxiliary regression is given below
. reg errors rep78 trunk length l.errors
Source |       SS           df       MS      Number of obs   =        63
-------------+----------------------------------   F(4, 58)        =      4.66
Model |   104717963         4  26179490.9   Prob > F        =    0.0025
Residual |   325759819        58   5616548.6   R-squared       =    0.2433
-------------+----------------------------------   Adj R-squared   =    0.1911
Total |   430477782        62  6943190.04   Root MSE        =    2369.9
errors |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
rep78 |  -47.23696   332.1243    -0.14   0.887    -712.0559     617.582
trunk |   62.68025   103.8779     0.60   0.549    -145.2539    270.6144
length |   9.373147   20.97416     0.45   0.657     -32.6112    51.35749
errors |
L1. |   .5273932   .1225638     4.30   0.000      .282055    .7727313
_cons |  -2375.671   3741.339    -0.63   0.528    -9864.774    5113.432

(iv) Using the estimated results from auxiliary regression above, note the the R-squared value and multiply it by the number of included observations:
scalar N=_result(1)
scalar R2=_result(7)
scalar NR2= N*R2
scalar list N  R2  NR2
N =         63
R2 =  .24325986
NR2 =  15.325371

(v)  Now, the null hypothesis of BP test is that there is no autocorrelation, we can use the standard Chi-Square distribution to find the tabulated values of the Chi-Square to check if the null hypothesis of no autocorrelation needs to be rejected. According to theory, the Chi-Square statistic calculated using the NRSquare approach above, the test statistic NR2 converges asymptotically where degrees of freedom for the test is s which the number of lags of the residuals included in the auxiliary regression and we have included 1 lagged value of errors/residuals so degrees of freedom in this case is 1. We can use Stata conventional functions for distribution to determine the tabulated values at 5% level of significance using the following code:
scalar chi151=invchi2tail(1, .05)
scalar list chi151
chi15 =  3.8414598
We got from the above tutorial in tests for autocorrelation, NR2 = 15.325371 > 3.84 = Chi-Square (1, 5%). As the calculated value of Chi2 is greater than tabulated values of Chi2, so we reject the null hypothesis of no autocorrelation on the disturbances.