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 bbaltagi@maxwell.syr.edu. My home page is www-cpr.maxwell.syr.edu/faculty/
baltagi.

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

Read More About Stochastic Frontier Models

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

You can request stochastic frontier models using Stata for panel data if you are writing your PhD thesis.

Request SFM using Stata

Stochastic Frontier Models using Stata

Stochastic Frontier Models for Cross Sectional Data

Stata also offers an excellent menu driven system to conduct stochastic frontier modeling for cross sectional data.

Request SFM using Stata

Freelancing and Learning Solutions

AnEc Center for Econometrics Research provides private and instructor led courses in Applied Econometrics, Advanced Econometrics, Financial Econometrics, Statistical Analysis and Quantitative Research. You can also request complete data analysis for thesis writing on freelance terms.

Online Courses

Learn Efficiency Analysis and Stochastic Frontier Models using Stata in a Private and Instructor Led course in Econometrics

Enroll Now

Freelance Offers

Get your data analysed for stochastic frontier models and thesis written with the help of our top econometrics freelancers and consultants

Hire Freelancer

Trainings

Request for a private and customized online training through Virtual Classroom from AnEc Center for Econometrics Research

Get Trained

Examples of SFM Application

Stochastic Frontier Models are applied to investigate issues in production efficiency, cost efficiency and input-output relationship in industrial and organizational setups. The application of SFM provides excellent feedback and insights on related factors and hence entities can develop a strong policy to provide tools and policy suggestions for future growth..

Request Analysis of Data using Stochastic Frontier Models

Firm's production efficiency
Firm's cost efficient
Regional trade efficiency
Technical efficiency
Efficiency in services sectors

Fees and Charges for Research Consultancy

AnEc Center for Econometrics Research offers private and instructor led courses and research consultancy on freelance basis in the area of Data Analysis and Writing of Results Chapters for Thesis and Research Papers

Online Courses

$399/Course
Providing private and instructor led online courses in Applied Econometrics, Applied Statistics, Quantitative Research and Applied Economics to students in Economics and Finance

Enroll Now

Freelance Data Analysis

$299/Project
Producing complete results from your data using Stochastic Frontier Models using Stata and other softwares to help you write an effective research report, PhD Thesis or MSc Dissertation

Hire Freelancer

Training and Workshop

$125/Semester
Creating completely customized learning solutions for students, faculty and researchers in using Stata, Eviews, R, Matlab, RATS or GAUSS for Data Analysis and Research Report Writing

Request Workshop

Testimonials and Feedback

Our students and clients provide regular feedback and remarks on our courses and freelance projects. Read some of the feedback to help you decide easily about quality of our work done.
AnEc freelancers are really top of the list. They work excellent and provide complete solutions. Only problem is to get them start due to the selected areas of work they do in Econometrics. I recommend they should extend their services to writing and editing as many students face problems in writing as well. Overall, I feel 100% satisfied with the work they did for me.
Students Feedback
Jamal ud Din
PhD Student in Economics, UK
Anees worked well. He provided complete data analysis and results writing in given time. Highly recommended freelancers.
Shahid Hakan
PhD Student in Economics, UK
Econometrics is a touch subject for many students in Economics and Finance. I found the explanation by Prof. Anees easy to understand and follow for application to my research project. I am happy to have him as my research mentor.
Javed Barkat
PhD Student in Finance, Germany

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.

Learn Econometrics EasilyCheck our online courses here

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

IDEA IS ALWAYS LEADING A PERSON TOWARDS A GOAL!

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

DATA HAS A HIDDEN OCEAN OF KNOWLEDGE TO BE EXPLORED.

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

LEARNING IS ALWAYS A BLESSING IF YOU ARE WILLING TO LEARN IT

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.

Learn Econometrics Easily

AnEc Center for Econometrics Research provides an amazing learning experience to each candidate following the above understanding. We learn new things following the same approach and we always recommend this technique to our students to follow and hence master the concepts.

We offer many options in form of different kinds of online courses and research training with specialization in Econometrics, Research and Statistics. The students can learn Econometrics easily by doing their own projects for PhD or MSc degrees and learn the subject with mastery.

Enroll for Econometrics Courses here

Financial Econometrics Courses

You will learn Econometrics easily when you enroll for our Financial Econometrics Courses
Applied Econometrics Research Courses

Mastering Econometrics is only possible by enrolling for Applied Econometrics Research
Advanced Econometrics Courses

Participating in our Advanced Econometrics Modeling course is for PhD and MSc students

Learn Econometrics Easily with US

Enroll for one of the following private research courses in Econometrics and how easy it is to learn Econometrics.
Financial Econometrics

Students of MSc Economics or MSc Finance should enroll for Financial Econometrics to master the tools and techniques before they enter the job market to serve on specialist positions like Econometrician.

Apply Here

Applied Econometrics Research

Applied Econometrics is a read intensive module specially designed for PhD students in Economics, Finance and Social Sciences, To learn Econometrics for Research purposes is the core objective for this module.

Apply Here

Advanced Econometrics

Advanced Econometric Modeling is our specialization module for students in Economics and Finance who wish to embark upon their journey as Econometrics Researchers either in Academia and Industry.

Apply Here

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.

Apply For AdmissionSee Course Contents

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
Softwares Use and Programming

Predict and forecast economic and financial with any software using any data
COURSE CONTENTS

Selected Topics Covered In the Course

The following is a tentative list of the topics covered in the course. You can add your own topics to the course contents when you enroll in the private mode of the course to ensure you learn and apply your desired topics to analyse your data and write your reports.
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
Add you desired topics to the list here.
Wavelet Analysis of Economic and Financial Time Series and Economic Variables with High Frequency
ARCH, GARCH, eGARCH, DCC GARCH, CCC GARCH, DECO GARCH, Multivariate GARCH and Causality
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

Who Should Attend This Course?

If you need further information about our course, please click here

Students

Master and PhD Students in Economics, Finance and Social Sciences

Faculty Members

All faculty members in Economics, Finance and Social Sciences

Professionals

All business professionals who are using predictive models for decision making

Certification

Econometrics Research that can be included on their LinkedIn profiles directly from the certificate validation link. The certificates will be published through a private link as well on An Economist to help you add your research skills developed during the class projects to showcase the quality of work and assessment.

Registration Details

Register for the course in Advanced Econometric Modeling today. Read the auto-response and wait for the customized email from Professor Anees. You will be assisted in person to proceed and confirm your admission.

Apply For Admission HereCheck Course Contents

Examination and Assessment of Econometric Modeling Course

Each candidate of the course will be evaluated based on writing a high impact research article or thesis. At the end of the course, it is expected that each student would have been able to write a high quality publishable research papers to be submitted for high quality and higher ranked journals on top ranked publishers only.
Apply for AdmissionCourse Contents

Econometrics Modeling

Feedback and Sample Video Tutorial

Read how our students feel about our courses and see a small video tutorial of the course delivery
I was not confident to select and apply any econometric model. AEM helped me confide in my skills in Econ. Modeling.
Daniel Soberson - PhD Finance, Germany

Submit Application For Admission Now

Apply For Admission Now

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:

Congratulations on running your Cointegration Test Including Multiple Breaks Using GAUSS. For learning specific module in Applied Econometrics Research using GAUSS, please enroll for a course here.

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.

To request private courses in Data Analysis, click here.

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.

Free Sample Freelance Project Proposal

Dear Client,
Before you verify my following resumed profile from any source online, I can attest these through a trial before you decide and buy my proposal. Send me your data and I will create a proposed solution based on the structure of your data. Once you review this solution, I am sure, the following profile will be verifiable from AnEconomist or LinkedIn as well.
I would be happy help you get a 100% satisfaction through the following process of our working:
1. You send your data and objectives.
2. I create a proposed solution for verification of my candidacy.
3. You buy my proposal and I begin the work.
4. I submit regular progress report on each step of the work and you review it before final submission.
5. I modify the previous and future work according to your interim review.
6. I finish the analytics, reports, interpretation and you once again review everything.
7. You get a 100% satisfaction through quick communication
(yes, I got a delay in a project due to an emergency but see, the details, the clienty verify my skills are excellent).
Now more about me:
As a member of Data Science Central (DSC), American Economic Association (AES), Royal Economic Society (RES), International Health Economics Association (iHEA) and The Econometrics Society, I have been working closely with top academics in Economics, Econometrics, Statistics and Research Methods. Also, I am providing supervision in Applied Econometrics and Statistics to PhD candidates in Project Management, Business Management, Finance, Corporate Governance and Social Sciences.
I am professionally trained and the highly recogized online course provider in Stata, Eviews, SPSS, Nvivo10/11, WinRATS, SAS, GAUSS, Gretl, Minitab, C++, JavaScript and Python. I helped more than 300 clients from around the world in applied econometrics and statistics for corporate governance, financial performance, economics research, business evaluation, Value at Risk, Options Pricing, Stock Evaluation, Pairs Trading and Backtesting through the use of above statistical softwares. It is backed by my education in economics, statistics and econometrics from The University of Sheffield, UK.
I have a teaching and academic research experience of more than 11 years at a QS Ranked University. I teach modules in Economics, Statistics, Econometrics and Quantitative Analysis. Key themes and topics of my teaching are Qualitative Data Analysis, Factor Analysis, Principle Component Analysis, Power and Sample Size determination for Survival Studies, Analysis of Open ended surveys and interviews, Multivariate Time Series techniques in VAR/VECM, VARX, SVAR, Multivariate GARCH, ARDL and Bayesian Multivariate Time Series Methods. So far, more than 70 PhD and MS/MRes candidates completed their courses in Applied Econometrics and Applied Statistics under my supervision.
I welcome you to explore the above factors of my skills and experience from Econometricians Club and AnEconomist. I would also like a free demo in any course in the list, a proposed solution for your project using your own data and I will ensure you get the best outcome for your course or project on freelance terms and conditions.
Regards
Muhammad Anees
Assistant Professor
Founder Econometricians Club
Founder AnEconomist
Founder Stata Pro Help

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.