Key methods and aspects of empirical research, with an emphasis on applications in economics and finance. Typical issues associated with the application of (linear) regression models such as residual heteroscedasticity and autocorrelation will be covered. Special emphasis will be put on model specification and selection, the omitted variable bias, and endogeneity. Selected topics of time series analysis may also be covered. 

After passing this course participants will have learned to apply econometric methods based on a range of applications, mainly in economics and finance. Participants will have learned econometric theory on a level which is necessary to understand and conduct independent empirical research. They will know how to appropriately use selected econometric techniques depending on the research question.

You are required to present your progress on a regular basis.

I am convinced that learning requires hands-on experience. There are two main elements of the course to facilitate this kind of learning:

1. Participants have to replicate and extend an empirical paper published in a good academic (economics/finance) journal. Ideally, this paper is an important reference of their thesis. The paper has to be extended by adding data to the sample analyzed in the published paper. This implies that the choice of the paper must be made such that (a) the original dataset is available and (b) new/additional data can be collected. As a starting point, participants can inspect and choose papers from the Critical Finance Review on already published replications (http://cfr.ivo-welch.info/home/) or future replication possibilities (http://cfr.ivo-welch.info/home/replication-issues.html). One can also take a look at the special issue on replication in the Strategic Managment Journal (http://onlinelibrary.wiley.com/doi/10.1002/smj.2016.37.issue-11/issuetoc), or The Replication Network (https://replicationnetwork.com/).

• In case you have serious difficulties in finding a suitable paper, you can use one of the papers and datasets from the exercises or examples of the lecture notes (Basic Financial Econometrics; associated files).
• If you find a suitable paper and dataset, but it is not possible to collect additional data, you can use the first and second half (or other segments) of the existing data to check the stability of results (e.g. fit the first half and predict the second half).
• I recommend choosing a paper which requires using methods you are already familiar with. However, it should also expand your horizon; i.e. do something new, something you are not (too) familiar with.
• In the first unit, you have to present a proposal of the paper you want to replicate, and what you are planning to do. During the course, you have to present informal (but informative) reports and results. Most importantly, you will have the chance to ask questions. The scope of topics and problems encountered in the replications will mainly determine the contents of this course.

A proposal for the planned replication has to be uploaded via Learn@WU (see the corresponding assignment "proposal").

2. Participants have to develop a prediction model for a dataset which will be made available after the first unit. Predictions are evaluated on the basis of a holdout dataset. Participants will get feedback whenever they submit a prediction. The submission of predictions, feedback and monitoring are done via a specific website (details will be provided in the first unit). In each unit, the progress made in developing the prediction model will be discussed. I will provide feedback and make recommendations.

Grades are mainly based on my (subjective - what else?) judgment of the participants' contributions during class discussions, on their presentations, on the quality of their replication/extension study, and on the results obtained in the prediction problem. All aspects are equally weighted.

Experience in doing empirical research. Specific (additional) skills may be determined by the replication study. You can use any software you want or need (Excel, EViews, R, Stata, SAS, SPSS, etc. ).