Introduction to data analysis

With the latest development in computer science, multivariate data analysis methods became increasingly popular among economists. Pattern recognition in complex economic data and empirical model construction can be more straightforward with proper application of modern softwares. However, despite the appealing simplicity of some popular software packages, the interpretation of data analysis results requires strong theoretical knowledge. This book aims at combining the development of both theoretical and applicationrelated data analysis knowledge. The text is designed for advanced level studies and assumes acquaintance with elementary statistical terms. After a brief introduction to selected mathematical concepts, the highlighting of selected model features is followed by a practice-oriented introduction to the interpretation of SPSS1 outputs for the described data analysis methods. Learning of data analysis is usually time-consuming and requires efforts, but with tenacity the learning process can bring about a significant improvement of individual data analysis skills.

Bevezetés a sztochasztikus analízisbe közgazdászoknak (Introducing stochastic analysis)

A dolgozat célja, hogy rövid bevezetést adjon a folytonos idejű sztochasztikus analízisbe. A hazai pénzügyi oktatási gyakorlat nagyrészt a diszkrét idejű és gyakran diszkrét állapotterű modellekre épül. Ennek oka a folytonos időparaméterű sztochasztikus folyamatok elméletétől való érthető idegenkedés. A folytonos időparaméterű sztochasztikus analízis a modern matematika egyik csúcsteljesítménye, amely teljeskörű matematikai megértése egyrészt feltételezi, hogy az olvasó tisztában van a modern analízis szinte minden részletével; másrészt a matematikai részletek pontos megértése nem sok segítséget jelent a pénzügyi gondolatok elsajátításakor. / === / In the article we present a short, intuitive introduction to stochastic analysis. Our presentation is aimed for economist and we try to discuss only the most elementary properties of the stochastic analysis. Instead of precise proofs we present some simplified intuitive arguments. The central concept of the discussion is the quadratic variation and the Itō's lemma.

Spillovers from Foreign Direct Investment in Central and Eastern Europe. An index for measuring a country’s potential to benefit from technology spillovers

In the paper, we construct a composite indicator to estimate the potential of four Central and Eastern European countries (the Czech Republic, Hungary, Poland and Slovakia) to benefit from productivity spillovers from foreign direct investment (FDI) in the manufacturing sector. Such transfers of technology are one of the main benefits of FDI for the host country, and should also be one of the main determinants of FDI incentives offered to investing multinationals by governments, but they are difficult to assess ex ante. For our composite index, we use six components to proxy the main channels and determinants of these spillovers. We have tried several weighting and aggregation methods, and we consider our results robust. According to the analysis of our results, between 2003 and 2007 all four countries were able to increase their potential to benefit from such spillovers, although there are large differences between them. The Czech Republic clearly has the most potential to benefit from productivity spillovers, while Poland has the least. The relative positions of Hungary and Slovakia depend to some extent on the exact weighting and aggregation method of the individual components of the index, but the differences are not large. These conclusions have important implication both the investment strategies of multinationals and government FDI policies.