How Important Are Foreign Shocks in Small Open Economy? The Case of Slovakia

In this paper, we provide evidence on the nature and the relative importance of domestic and foreign shocks in Slovak economy based on block-restriction vector autoregression model in 1999-2007. We document well-functioning monetary transmission mechanism in Slovakia. Subject to various sensitivity checks, we find that contractionary monetary policy shock has a temporary negative effect on the degree of economic activity and price level. We find that using output gap instead of GDP alleviates the price puzzle. In general, prices are driven mainly by foreign factors and the European Central Bank monetary policy shock on Slovak prices is more powerful than that of the National Bank of Slovakia. Slovak central bank interest rate policy seems to follow the ECB’s interest rates. On the other hand, spectacular Slovak economic growth is primarily driven by domestic factors suggesting the positive role of recently undertaken Slovak economic reforms.small open economy, foreign shocks, monetary policy, Slovakia, euro area

How Important Are Foreign Shocks in Small Open Economy? The Case of Slovakia

In this paper, we provide evidence on the nature and the relative importance of domestic and foreign shocks in Slovak economy based on block-restriction vector autoregression model in 1999-2007. We document well-functioning monetary transmission mechanism in Slovakia. Subject to various sensitivity checks, we find that contractionary monetary policy shock has a temporary negative effect on the degree of economic activity and price level. We find that using output gap instead of GDP alleviates the price puzzle. In general, prices are driven mainly by foreign factors and the European Central Bank monetary policy shock on Slovak prices is more powerful than that of the National Bank of Slovakia. Slovak central bank interest rate policy seems to follow the ECB’s interest rates. On the other hand, spectacular Slovak economic growth is primarily driven by domestic factors suggesting the positive role of recently undertaken Slovak economic reforms.small open economy, foreign shocks, monetary policy, Slovakia, euro area.

Wavelet Neural Networks: A Practical Guide

Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of application. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thorough examined: the structure of a WN, training methods, initialization algorithms, variable significance and variable selection algorithms, model selection methods and finally methods to construct confidence and prediction intervals. In addition the complexity of each algorithm is discussed. Our proposed framework was tested in two simulated cases, in one chaotic time series described by the Mackey-Glass equation and in three real datasets described by daily temperatures in Berlin, daily wind speeds in New York and breast cancer classification. Our results have shown that the proposed algorithms produce stable and robust results indicating that our proposed framework can be applied in various applications

Absorptive capacity and the growth and investment effects of regional transfers : a regression discontinuity design with heterogeneous treatment effects

Researchers often estimate average treatment effects of programs without investigating heterogeneity across units. Yet, individuals, firms, regions, or countries vary in their ability, e.g., to utilize transfers. We analyze Objective 1 Structural Funds transfers of the European Commission to regions of EU member states below a certain income level by way of a regression discontinuity design with systematically heterogeneous treatment effects. Only about 30% and 21% of the regions - those with sufficient human capital and good-enough institutions - are able to turn transfers into faster per-capita income growth and per-capita investment. In general, the variance of the treatment effect is much bigger than its mean

Ancestral Causal Inference

Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existing approaches can still be greatly improved in terms of accuracy and scalability. We present a novel method that reduces the combinatorial explosion of the search space by using a more coarse-grained representation of causal information, drastically reducing computation time. Additionally, we propose a method to score causal predictions based on their confidence. Crucially, our implementation also allows one to easily combine observational and interventional data and to incorporate various types of available background knowledge. We prove soundness and asymptotic consistency of our method and demonstrate that it can outperform the state-of-the-art on synthetic data, achieving a speedup of several orders of magnitude. We illustrate its practical feasibility by applying it on a challenging protein data set.Comment: In Proceedings of Advances in Neural Information Processing Systems 29 (NIPS 2016