Econometrics Exercises

[Alexia Borson]

Thevolumes in Econometric Exercises are intended to be much more than a collection of several hundred solved exercises. Each book has a coherent and well-organized sequence of exercises in a specific field or sub-field of econometrics. Every chapter of a volume begins with a short technical introduction that emphasizes the main ideas and overviews the most relevant theorems and results, including applications and occasionally computer exercises. They are intended for undergraduates in econometrics with an introductory knowledge of statistics, for first and second year graduate students of econometrics, and for students and instructors from neighboring disciplines (e.g., statistics, political science, psychology and communications) with interests in econometric methods.

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Jan R. Magnus (1948) studied econometrics and philosophy at the University of Amsterdam. He worked at the London School of Economics and Tilburg University before moving to the Vrije Universiteit Amsterdam as Extraordinary Professor.

"This is the first book in econometrics to look at models and problems from the Bayesian point of view. [M]any comparisons of Bayesian and non-Bayesian results are presented. [...] An Introduction to Bayesian Inference in Econometrics will be of value as a guide to Bayesian Econometrics for graduate-level students and as a reference volume for researchers."

I might consider Contemporary Bayesian Econometrics and Statistics by John Geweke. It is relatively brief. The first three chapters cover the sort of foundational stuff you find in any Bayesian analysis book. The next chapter is the linear model with a tad of non-linear regression, followed by latent variables and missing data, then time-series and closed with model comparison and evaluation. There's not very much on panel data or semi/nonparametric estimation.

This web site supports our book, Introductory Econometrics: Using Monte Carlo Simulation with Microsoft Excel, published by Cambridge University Press. Our fundamental strategy is to use clear language and take advantage of recent developments in computers to create concrete, visual explanations of difficult, abstract ideas.

In addition, we've decided to make the Excel add-ins that we've written freely available. We've used these add-ins to greatly improve our teaching and we hope others can benefit from the sophisticated algorithms we've developed. Of course, we ask that you properly credit and cite our work if you use these materials. We'd love to hear from you about how you used our add-ins.

While this web site contains a great deal of information, there's even more material in the book, where we explain how to actually use the Excel workbooks to learn econometrics. The first printing of our book comes with a CD ROM with a full set of answers to all Excel workbook, self-study questions (but not the answers to the exercises in the book itself). The CD also has materials on downloading data from a handful of web sites, including detailed instructions on using the Current Population Survey, and several "how to" documents that offer practical, step-by-step instructions on a particular task. All these files are as of June 2010 available in a 27.7 MB zip file. To get that file, click on this link.

Humberto Barreto

Allen Distinguished University Professor at DePauw University, was born in Camagey, Cuba and earned his B.A. from New College and Ph.D. from the University of North Carolina at Chapel Hill. He manages an email list for the History of Economics Society.

Frank M. Howland,

Professor of Economics at Wabash College, earned an A.B. from Harvard College and a Ph.D. in Economics from Stanford University. He is a native of Boston, Massachusetts. Howland's research focuses on health economics.

This section is based on Wooldridge, J.M. (2013). Introductory econometrics: A modern approach (5thed.). The following links contain examples in the main text of the book and use R to estimate the models. Alternatively, Heiss, F. (2016) Using R for Introductory Econometrics is a standalone textbook, which covers the same topics as Wooldridge (2013) and provides an introduction to R as well.

The data sets are from the wooldridge package, which is a collection of all data sets used in the Wooldridge textbook. Similar to my page, the package also has a vignette which contains a comprehensive collection of the Wooldridge textbook examples.

This textbook teaches some of the basic econometric methods and the underlying assumptions behind them. It also includes a simple and concise treatment of more advanced topics in spatial correlation, panel data, limited dependent variables, regression diagnostics, specification testing and time series analysis. Each chapter has a set of theoretical exercises as well as empirical illustrations using real economic applications. These empirical exercises usually replicate a published article using Stata, Eviews as well as SAS.

This new sixth edition has been fully revised and updated, and includes new material on limited dependent variables and panel data as well as revision of basic topics like heteroskedasticity, endogeneity, over-identification and specification testing. The author also provides more exercises and empirical examples based on published economic applications.

For more than a decade, business economists and analysts have flocked to this popular offering from NABE to acquire the analytical tools needed to produce actionable results for their employers. If your company relies on your analysis for planning and decision-making, earn NABE's Applied Econometrics Certificate and enhance your ability to add value in your workplace.

The Applied Econometrics Certificate Program is not like academic econometric courses, which are often long on theory and short on practical applications. This program emphasizes business applications of statistical techniques and covers cutting-edge developments in economic methodologies and quantitative analysis.

The course covers the essentials of econometric methods and provides practice with hands-on applications using EViews econometric software. Participants will gain experience with this powerful statistical software through empirical exercises involving econometric testing and estimation procedures. Complimentary trial versions of EViews are available to all participants.

The main topics covered in the course are:

At minimum, participants should have a background in economic theory and statistical methods as this course is quite comprehensive. Experience in the use of mathematics, statistics and regression analysis in economic research is also helpful. Empirical examples from economics and finance are employed in the lectures and in student exercises to provide practice with data and problems that are typical in economic and business analysis.

We may fit a regimes model, where separate regression coefficients are calculated for interactions between the municipality department dummies and the included variables; size and dist_metro only retian influence for municipality departments 1 and 2:

The Hausman test compares the OLS and SEM coefficient estimates and their standard errors, assessing whether their distributions overlap sufficiently to suggest the absence of major mis-specification:

The tables are harder to read than the figure, which shows that the coefficient estimates do differ a lot for two variables, somewhat for the intercept, and little for one variable, but where the ML standard error estimate under usual assumptions crosses zero:

Reaching out to the SLX models does not help, because although - as with the SDEM models - the indirect impacts (coefficients on lagged \(X\) variables) are large, so including lagged \(X\) variables especially at the properties level seems sensible, there is serious residual autocorrelation, and now the pre-test strategy points to a missing spatial process in the response:

Turning to estimating the general nested model first, followed by excluding the Durbin (spatially lagged \(X\)) variables, a likelihood ratio test shows that the spatially lagged \(X\) variables should be retained in the model:

We cannot say that the spatial econometrics approach has reached a clear conclusion. When including the upper level variables, we introduce a lot of spatial autocorrelation at the lower level. It is arguable that the MRF random effect at the upper level and including only the properties level variables gets at least as far as the most complex spatial econometrics models. It is fairly clear that mapping the actual green space and museums, and measuring distance from each property to the attractions would remove the scale problem for those variables. Disaggregation of the foreigners, airbnb and population density variables would be highly desirable. With improvements to the properties level data set, including more variables describing the properties themselves, much of the mis-specification should be removed.

Introduction to Econometrics with R is best described as an interactive companion to the well-received textbook Introduction to Econometrics (Stock & Watson, 2015) which serves as a basis for the undergraduate courses in Econometrics we teach at the University of Duisburg-Essen, Germany.

This project aims to provide students with an e-learning arrangement which seamlessly intertwines theoretical core knowledge and empirical skills in undergraduate econometrics. Of course, the focus is on empirical applications with R. Our goal is to enable students not only to learn how results of case studies can be replicated with R but we also intend to strengthen their ability in using the newly acquired skills in other empirical applications. This is supported by interactive end-of-chapter R programming exercises and interactive visualizations.

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