Principles Of Econometrics With R

[Joke Grinman]

Economistsdevelop economic models to explain consistently recurring relationships. Their models link one or more economic variables to other economic variables. For example, economists connect the amount individuals spend on consumer goods to disposable income and wealth, and expect consumption to increase as disposable income and wealth increase (that is, the relationship is positive).

There are often competing models capable of explaining the same recurring relationship, called an empirical regularity, but few models provide useful clues to the magnitude of the association. Yet this is what matters most to policymakers. When setting monetary policy, for example, central bankers need to know the likely impact of changes in official interest rates on inflation and the growth rate of the economy. It is in cases like this that economists turn to econometrics.

Certain features of economic data make it challenging for economists to quantify economic models. Unlike researchers in the physical sciences, econometricians are rarely able to conduct controlled experiments in which only one variable is changed and the response of the subject to that change is measured. Instead, econometricians estimate economic relationships using data generated by a complex system of related equations, in which all variables may change at the same time. That raises the question of whether there is even enough information in the data to identify the unknowns in the model.

The first step is to suggest a theory or hypothesis to explain the data being examined. The explanatory variables in the model are specified, and the sign and/or magnitude of the relationship between each explanatory variable and the dependent variable are clearly stated. At this stage of the analysis, applied econometricians rely heavily on economic theory to formulate the hypothesis. For example, a tenet of international economics is that prices across open borders move together after allowing for nominal exchange rate movements (purchasing power parity). The empirical relationship between domestic prices and foreign prices (adjusted for nominal exchange rate movements) should be positive, and they should move together approximately one for one.

The main tool of the fourth stage is hypothesis testing, a formal statistical procedure during which the researcher makes a specific statement about the true value of an economic parameter, and a statistical test determines whether the estimated parameter is consistent with that hypothesis. If it is not, the researcher must either reject the hypothesis or make new specifications in the statistical model and start over.

If all four stages proceed well, the result is a tool that can be used to assess the empirical validity of an abstract economic model. The empirical model may also be used to construct a way to forecast the dependent variable, potentially helping policymakers make decisions about changes in monetary and/or fiscal policy to keep the economy on an even keel.

Econometrics, by design, can yield correct predictions on average, but only with the help of sound economics to guide the specification of the empirical model. Even though it is a science, with well-established rules and procedures for fitting models to economic data, in practice econometrics is an art that requires considerable judgment to obtain estimates useful for policymaking.

Join MIT professor Josh Angrist and learn to master the econometrics "Furious Five": random assignment,regression, instrumental variables, regression discontinuity designs, and differences-in-differences methods.

Think econometrics is boring? So it was once, but will be no more! Skipping theoretical tedium, we use real empirical questions to bring the numbers to life. Does a private university education pay off with higher earnings? Do lower drinking ages cost lives? Econometrics uncovers the answers.

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Using Stata for Principles of Econometrics, Fourth Edition, by Lee C. Adkins and R. Carter Hill, is a companion to the introductory econometrics textbook Principles of Econometrics, Fourth Edition. Together, the two books provide a very good introduction to econometrics for undergraduate students and first-year graduate students.

The main textbook takes a learn-by-doing approach to econometric analysis, and this companion book illustrates the "doing" part using Stata. Adkins and Hill briefly show how to use Stata's menu system and command line before delving into their many examples.

Using Stata for Principles of Econometrics, Fourth Edition shows how to use Stata to reproduce the examples in the main textbook and how to interpret the output. The current edition has been updated to include features introduced in Stata 11, such as the margins command to compute elasticities. Together with Principles of Econometrics, Fourth Edition, the reader will not only learn econometrics but also gain the confidence needed to perform his or her own work using Stata.

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The master of science program in economics combines the analytical framework of modern economic theory and the quantitative methods of applied econometrics with special emphasis on the following areas: coastal resources and environmental economics; economic forecasting, financial economics; health care; and issues of poverty and regional development.

The program provides an ideal background for work in industry, government and nonprofit positions requiring the combination of analytical skills and institutional background. Although it is an excellent preparation for further graduate work in economics, it was conceived specifically to provide graduates with up-to-date and marketable skills. One of the strengths of the program is in the small class sizes and a close working relationship with the faculty. Faculty who are active contributors to the areas in which they teach conducts all graduate field courses.

The structure of the program reflects the research interests of the faculty. Some of the faculty conduct research and teach in areas of macroeconomics, but the preponderant emphasis within the department is on neoclassical microeconomics applied to areas such as coastal and marine resources, environmental economics, health care, labor economics, regional development and income distribution.

The program design includes a healthy measure of theory and econometric technique. The structure of the program reflects the conviction that people best learn economics by doing economics: our elective courses are taught as applications of the theory and technique courses and encourage students to define all research questions in a manner that will permit empirical testing.

A successful working economist must not only master the statistical techniques of modern empirical research, but must also be able to convey the results of this research in a persuasive and articulate manner. Our program accordingly gives students ample opportunity to develop necessary analytical and presentation skills. The department also conducts regular economics seminars at which our faculty and invited external speakers present their latest research.

As an ECU student, you can research your future career in Steppingblocks. Explore real-world stats about your major, your interests, and your dream job title with data-powered career exploration tools designed for doers like you.

Applicants to the quantitative economics and econometrics degree program must meet the admissions requirements of the Graduate School, submit three letters of recommendation, make an acceptable score on the general portion of the Graduate Record Examination (GRE), and have had at least one undergraduate course each in introductory statistics and differential calculus. Nonnative speakers must make an acceptable score on the TOEFL. Undergraduate courses in intermediate microeconomics and macroeconomics are strongly recommended.

In my statistical teaching, I encounter some stubborn ideas/principles relating to statistics that have become popularised, yet seem to me to be misleading, or in some cases utterly without merit. I would like to solicit the views of others on this forum to see what are the worst (commonly adopted) ideas/principles in statistical analysis/inference. I am mostly interested in ideas that are not just novice errors; i.e., ideas that are accepted and practiced by some actual statisticians/data analysts. To allow efficient voting on these, please give only one bad principle per answer, but feel free to give multiple answers.

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