Empirical Methods
 Lecture:
 Oliver Kirchkamp, Thu. 1214, KU Hörsaal, Bachstraße 18K , [Friedolin]
 Exercises:
 Dr. Olexandr Nikolaychuk,
Fri., 1012, Carl Zeiss Str. 3, HS 8.
Tue., 1214, Carl Zeiss Str. 3, HS 4, [Friedolin].  Discussion board:
 Please use the discussion board of this course to ask questions and discuss issues related to the lecture.
 Topics:

Week Topic 42 Review of probability and statistics 43 Dies academicus (no lecture, only exercise) 44 Review of frequentist inference 45 Review of linear Regression 46 Review of models with multiple regressors 47 Midterm exam (1/3) (no lecture or exercise in this week) 48 Bootstrap 49 Bayesian methods (Resit of Midterm) 50 Bayes in practice 51 Binary choice 2 More on discrete choice 3 Mixed effects models 4 Instrumental variables 5 Model comparison 6 Summary, revision  Exams:
 To get a credit for the course students have to pass the midterm (1/3 of the total grade) and the final exam (2/3 of the total grade, please keep in mind that grades for the course must be rounded!).
You can take the final exam even if you have failed the midterm. You pass the course only once you have passed both parts. (E.g. you can pass the final in one year and the midterm in another year to get the credit.) You must be registered for the exam in Friedolin to get a credit for the course.
 Midterm (1/3):
 Thu., 22.11.2018, 18:00, Carl Zeiss Str. 3, HS 1[Friedolin]
 To register for the midterm students register for the course at least 10 days before the midterm exam through Friedolin. Please register for the course and for the exam! (If it is for some reason impossible to register for the exam during the first weeks of the term you can still take the midterm if you only register for the course. However, to take the final exam you have to register in Friedolin for the exam!)
 Exchange students who can not register for the course with Friedolin complete this form on their computer.
Students will also receive the midterm results (and, in case they failed, their seat for the resit) at their unijena address. Students who send their public PGP key to oliver.kirchkamp@unijena.de before the midterm will receive their results encrypted to their key.
Candidates who have earlier passed either the midterm or the final but not both parts have two options for the exams they take this term:
 They register for the exam, but show up only for the part they did not pass in the past. In this case I will credit the result of the previous attempt of the other part for this term.
 They register for the exam, and show up for both parts, midterm and final. In this case the result of this term counts for the final result. (This holds even if this term's result is a fail!)
 Instructions for the midterm.
 Resit of the midterm: Thu., 6.12.2018, 18:00, Carl Zeiss Str. 3, HS 3. [Friedolin]
 Students who took the midterm and who failed are automatically registered for the resit. No extra registration is needed in this case. (Please note that for the resit of the final exam you must register with the examination office.)
 Students who missed the first take because they were ill and who present a certificate from their physician within three days after the missed exam will participate in the resit. These students complete this form on their computer.
 Students who passed the first take can't take the resit.
 Students who did not show up for the first take and who were not ill don't take the resit.
 Final exam (2/3):
 18.02.2019, 1012, Carl Zeiss Str. 3, HS 1.
You must register in Friedolin for the exam (not simply for the course), even if you have passed the midterm earlier.
Exchange students who can not register for the course with Friedolin complete this form on their computer.
 Questions and answers, without any guarantee
 Grades of the final exam: 18.02.2019:
1 1.3 1.7 2 2.3 2.7 3 3.3 3.7 4 5 Min. points: 86 83 77 71 66 62 56 51 46 41 34 Count: 4 3 8 11 13 2 2 8 2 5 3 Percent: 7 5 13 18 21 3 3 13 3 8 5  Resit of the final exam: Thursday, 16.4.2019, 18:00, Carl Zeiss Str. 3, HS 3.
Students who want to take the resit of the final exam will have to register with the examination office.
 Other material:
 Handout (contains all the slides):
 Previous exam questions
 Handout (contains all the slides):
 Requirements:
 Basic mathematical and statistical methods as, e.g., in BW24.1.
 Literature:

To learn more, I recommend the following textbooks. You find further
recommendations at the end of each chapter in the handout. (If you find not enough copies of the books you need in the library, please tell the librarians.
If you do not tell them, nothing will change.)
 William H. Greene. “Econometric Analysis”. Pearson, 2012.
 James H. Stock and Mark W. Watson. “Introduction to Econometrics”. Pearson, 2011.
 John Kruschke. “Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan”. Academic Press, 2nd Edition, 2014.
 Examples from the lecture:
 In the lecture I will often use practical examples as illustrations.
I recommend that you try these examples on your own. To do this, open R and type the following:
data(Caschool,package="Ecdat")
from now on all commands refer to this dataset, e.g.:
attach(Caschool)plot(str,testscr)
, or
large<factor(str>20)
A brief documentation of the variables of this dataset can be obtained with
t.test(testscr~large)
help(Caschool)
 Software:

For our practical examples (during the entire course) we will use the software environment R. I think that it is helpful to coordinate on one environment and R has the advantage of being free and rather powerful.
For the Bayesian parts we will use JAGS. It helps if you have installed R and JAGS on your computer when we start the course.
 Documentation for R is
provided via the built in help system but also through the
R Homepage.
Useful are
 The R Guide, Jason Owen (Easy to read, explains R with the help of examples from basic statistics)
 Simple R, John Verzani (Explains R with the help of examples from basic statistics)
 Einführung in R, Günther Sawitzki (In German. Rather compact introduction.)
 Econometrics in R, Grant V. Farnsworth (The introduction to R is rather compact and pragmatic.)
 An Introduction to R, W. N. Venables und D. M. Smith (The focus is more on R as a programming language)
 The R language definition (Concentrates only on R as a programming language.)
 On the JAGS Homepage you go to the files pages, then to Manuals, to find the JAGS user manual.
 We will use the following packages:
AER, boot, bootstrap, calibrate, car, coda, Ecdat, geepack, Hmisc, lattice, latticeExtra, lme4, lmtest, MASS, memisc, nnet, plotrix, plyr, relaimpo, runjags, Sleuth2
. If, e.g., the commandlibrary(Ecdat)
generates an error message (Error in library(Ecdat): There is no package called 'Ecdat'
), you have to install the package. Installing packages with Microsoft Windows:
 Start
Rgui.exe
and install packages from the menuPackages / Install Packages
).  Installing packages from advanced operating systems:
 From within R use the command
install.packages("Ecdat")
, e.g., to install the packageEcdat
 In the lecture we will use RStudio as a front end.
For the Bayesian part we will use the library
runjags
and the softwareJAGS
 Documentation for R is
provided via the built in help system but also through the
R Homepage.
Useful are