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.
- Offers a simple explanation of hard concepts in econometrics with practical hands-on solved exercises using standard software like Stata and EViews
- A companion to the empirical and theoretical exercises given in Baltagi's textbook Econometrics
- Special features include empirical examples using EViews and Stata
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 email@example.com. My home page is www-cpr.maxwell.syr.edu/faculty/
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. 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: has written an excellent paper titled: Tests for cointegration with two unknown regime shifts with an application to financial market integration
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:
- Open GAUSS
- Create a data file as coint.txt which might include one dv and upto 2 DVs.
- Create a new file again and copy the code from: https://ideas.repec.org/c/boc/bocode/g00006.html
- Save the code using a name of your choice.
- Now, change the arguments related to number of observations, number of variables and load the data file in GAUSS
- Then create the relevant matrices and vectors by changing the default arguments to specific to the data.
- Then run the code ensuring the files are in the same folder as in the directory.
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.
Follow these steps to Download Economic Data using Excel from Knoema using Excel.
- Install the word plugin from Office Store.
- The plugin can be downloaded from this link: Office Knoema Data Finder
- Once the Data Finder tool is opened, you can save it to some location on your computer for future use.
- Once plugin is installed in Excel, you should make sure it is allowed for access internet becaude we will need to download the data directly in Excel while the system should be connected to internet to access Knoema website.
- Then open Excel and start the Knoema Data Finder tool.
- It will show a side bar at right side allowing you to search any variable. Many sources will be given to select your data from. Select your preferred source of data on the given variable.
- Click on the link. It will begin processing to download the data on the given variable from the selected source.
- You can see in less than a minute, the required data on the selected variable will be downloaded. To add another variable, we should select the first cell of the empty row at the bottom of previous downloaded data or on a new excel sheet. I personally recommend to use a new sheet so any mixing of data between different sessions can be avoided.
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:
- Before running FMOLS or Cointegration tests, one can test for panel unit root for each of the variables in the system. The Unit roots assumptions for the cointegration Test should considered before production of the estimates.
- Open the data as a work file in Eviews and set the frequency according to the time frequency.
- Left Click on the dependent variables, press controll key and left click on other independent variables one by one.
- Now, right click on any of the selected variables and click on Open as Equation
- A new window appears , select the COINTEG as method. The window will change to a new window giving further options. One can select lags and change method from Nonstrationary Estimations Settings/Options.
- Select FMOLS from this and click on the OK button.
- Fully Modified OLS results will be produced.
- Now, click on the View button of the FMOLS button.
- Click on Cointegration tests
- A small window appears, select the Phillips-Ouliaris from the list.
- Click on OK button of the same small button. Phillips-Ouliaris Cointegration Test results appear on the FMOLS screen.
- The Null hypothesis is that the Series are not cointegrated. The test statistic to reject or accept this null hypothesis is based on Za and Ta statistic. If the p-value is less than 0.05 using the 95% level of confidence, we reject the null hypothesis of no cointegration which verifies the series in the system are cointegrated.
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:
- Open JMulti and click on File, Import Data.
- Locate and Load your data to be in XLS format in excel if not specific JMulti format.
- Desig your time period by specifying the initial time point. We have annual data starting at 1980 so we will set 1980 as the initial time period and type it there.
- Then we have to select the variables to be included in our model. We click one by one on these variables and click on Confirm Selection below the list of variables. This will load the selected variables to the small boxes. Then we proceed with the other steps to estimate SVAR.
- Once variables are selected, we then specify lags for endogenous variables and set out Max Lags as well. Then we confirm the selected lags by clicking on Compute Infocriteria. This produces the selected lags based on AIC and BIC.
- Then we estimate the VAR system. The results can be seen on the JMulti screen. Also, these steps are are outlined in this video tutorial attached below.
- Then we specify the SVAR system, select A and B matrices for conditions and restrictions. Once these matrices are specified, we can compute the estimated coefficients.
- Structural VAR using JMulti will allow us to compute Structural IRF and Structural FEVD. This can be obtained from the window of Structural Analysis of the VAR estimates.
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
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
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 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.