Empirical Investigations of Current Monetary and Fiscal Policy Issues
This dissertation comprises four empirical, third-party funded working papers. The first two contributions are in the field of monetary policy and stability in international financial markets. These papers make use of recent methodological advances in statistical causality analysis between multiple economic variables of interest. One particular merit of statistical identification is that it offers an estimate of causal linkages without a priori restrictions by employing data features like (un)conditional heteroscedasticity or non-normality. This is in contrast to earlier approaches, where, e.g., specific causal linkages between variables of interest are ruled out pre analytically. In the first two papers I investigate causal interactions, i.e. financial market workings as well as changes to them in policy episodes. Subsequently, the empirical pattern of interactions is used to discriminate between competing finance theories. Thus, not only the effectiveness of central bank policy is revealed, but also the answer to the question how the policy action under study works in detail given disentangled empirical causalities. The latter two papers incorporated in this thesis are dedicated to the field of fiscal policy and public finance. The questions under study here are relevant for the European Stability and Growth Pact and, e.g., have consequences for the German federal as well as for state public budget constraints. We show that using real time data of both the observable gross domestic product (GDP) and tax incomes, e.g., for the structural potential growth model of the European Commission one may employ classical statistical inference procedures to evaluate estimates and forecasts of unobservable economic long-term developments. That is, we contribute to the discussion by proposing a model evaluation based on the criterion of public budget uncertainty as measured through forecasting errors and resulting density forecasts.
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