Statistics, econometrics, data, analysis

These lecture notes are for Economics PhD students at the Corvinus University of Budapest, but can be used equally by any graduate student interested in modern econometrics and its relationship to general statistics. It is divided into five main sections. The first introduces some general concepts of theoretical statistics, including Bayesian ideas. Many of these ideas appear in econometrics textbooks, but some of them is ominously missing. Basic (philosophical) questions of statistics are usually not treated in those, though some appreciation of them should be useful for any practicing econometrician. The next two sections cover material that can be found in most (non-time series) econometrc textbooks. Here I stress the di¤erence between two apparoaches: the data description style of classical regression analysis and the causal estimation centered econometric approach. The following section introduces statistical learning, an area little known for most economists at the present. My conviction is that its knowledge will be more and more crucial in the future. The final section is assigned to time series analysis, mostly dealing with traditional time domain approaches, but making an unusual, for econometric texts, foray into the frequency domain and wavelet methods. Again, I believe that the latter will be important in the future, and the former is a stepping stone to the latter. The bookis a textbook and as such a compendium. It does not contain new material, at most a few examples or cases. It is based mostly on other textbooks, but selected from a rather wide range. At the end of each section those texts I used most extensively are listed, in all areas these should be consulted if someone wants to have a deeper understanding of the issues involved. The present text aims at a wide coverage, rather than an in-depth one.