Introduction To Econometrics With R

[Othon Sdcd]

Overthe last few years, the statistical programming language R has become an integral part of the curricula of econometrics classes we teach at the University of Duisburg-Essen. We regularly found that a large share of the students, especially in our introductory undergraduate econometrics courses, had not been exposed to any programming language before and thus had difficulty to engage with learning R on their own. With little background in statistics and econometrics, beginners naturally have a hard time understanding the benefits of having R skills for learning and applying econometrics. These particularly include the ability to conduct, document and communicate empirical studies and having the ability to program simulation studies which is helpful, e.g., for comprehending and validating theorems which usually are not easily grasped by mere brooding over formulas. Being applied economists and econometricians, we value and wish to share with our students all of these capabilities.

Instead of confronting students with pure coding exercises and complementary classic literature like the book by Venables and Smith (2010), we figured it would be better to provide interactive learning material that blends R code with the contents of the well-received textbook Introduction to Econometrics by Stock and Watson (2015) which serves as a basis for the lecture. This material is gathered in the present book Introduction to Econometrics with R, an empirical companion to Stock and Watson (2015). It is an interactive script in the style of a reproducible research report that not only enables students to learn how results of case studies can be replicated with R but also strengthens their ability in using the newly acquired skills in other empirical applications.

Monospaced font on gray background indicates R code that can be typed literally by you. It may appear in paragraphs for better distinguishability among executable and non-executable code statements but it will mostly be encountered in shape of large blocks of R code. These blocks are referred to as code chunks.

We thank the Stifterverband fr die Deutsche Wissenschaft e.V. and the Ministry of Culture and Science of North Rhine-Westphalia for their financial support. Also, we are grateful to Alexander Blasberg for proofreading and his effort in helping with programming the exercises.A special thanks goes to Achim Zeileis (University of Innsbruck) and Christian Kleiber (University of Basel) for their advice and constructive criticism. Another thanks goes to Rebecca Arnold from the Mnster University of Applied Sciences for several suggestions regarding the website design and for providing us with her nice designs for the book cover, logos and icons. We are also indebted to all past students of our introductory econometrics courses at the University of Duisburg-Essen for their feedback.

This semester I taught a course on applied econometrics with the R programming language. For this, I created a document that I gave to my students and shared online. This is the kind of document I would have liked to read when I first started using R. I already had some programming experience in C and Pascal but this is not necessarily the case for everyone that is confronted to R when they start learning about econometrics.

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.

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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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.

There are several small JavaScript extensions, e.g., folding of code and it's output and a copy button at the top-right corner of the code chunks which makes it convenient to work with larger code segments.

Quantitative analysis of high dimensional data sets isincreasingly used for problem solving in economics, business, andfinance. However, a skillful analysis requires profound knowledgeof the underlying statistical methods and statistical programmingskills.

The objective of this course is to introduce you tofundamental concepts of econometrics and dataanalysis that form the basis for data driven decisionmaking, empirical analysis of causal relationships, andforecasting. In particular, the concepts that you will learn inthis course will equip you with skills and knowledge necessary toexcel in more advanced econometrics and applied statisticscourses at CBS (e.g., BA-BMECV1031U Econometrics,KAN-COECO1058U Econometrics, KAN-COECO1056U Financial Econometrics,KAN-CMECV1249U Panel Econometrics) and elsewhere. Finally, thiscourse will sharpen your technical skills forproblem solving at workplace and in otherreal-life settings.

Throughout the course, we will learn aboutmatrices and their use in linear regressionanalysis, probability distributions and their rolein carrying out valid data approximations, and estimationmethods and their importance in producing credible resultsof any data analysis.

The course will also introduce you to programming withR, which is the main programminglanguage of statistical computing. We will start out withbasic R operations and then, with time, we will learn about ways towrite our own functions in R. In this way, you will be set on apath of becoming a statisticalprogrammer

Preliminary Assignment: The NordicNine pre-course is foundational for the summer universityand identical for all bachelor courses. Students will receive aninvitation with all details by the end of May. The assignment hastwo parts. 1.) Online lectures and tutorials that student canaccess at their own time and 2.) One synchronous workshop whichwill be offered both online and in-person at several dates andtimes before the official start of the summer university courses.Sign-up is first come first serve. All students are expected tocomplete this assignment before classes begin.

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