Are Central Banks in CEE Countries Concerned about the Burden of Public Debt?

The aim of this study is to analyze the monetary policy rules in the Czech Republic, Hungary and Poland, with public debt as an additional explanatory variable. We estimate linear rules by the GMM estimation and non-linear rules, using the Markov-switching model. Our findings suggest that in the Czech Republic and Poland the monetary authorities respond to growing public debt by lowering interest rates, while in Hungary the opposite may be observed. Moreover, we distinguish between passive and active monetary policy regimes and find that the degree of interest rate smoothing is lower and the response of the central banks to inflation and/or output gap is stronger in an active regime. In the passive regime, the output gap seems to be statistically insignificant

Determinants of Cyclicality of Fiscal Surpluses in The OECD Countries

In this paper we examine factors that make some governments revert to procyclical fiscal policies despite the standard normative prescription being to conduct fiscal policy countercyclically. In order to avoid the pitfalls of the two-step methods previous studies have typically used we used a one-step method with interaction variables. We found robust statistical evidence that procyclical fiscal policies are typically run by countries with weak institutions. There was also some empirical support for a hypothesis that countries that have accumulated a high debt-to-GDP ratio tend to run procyclical fiscal policies, possibly as a result of the financial constraints. We found no evidence that any other variable among the ones suggested in the literature explains the way in which governments react to the business cycle.procyclical fiscal policy, financial constraints, fiscal institutions

Convolutional Neural Networks for Counting Fish in Fisheries Surveillance Video

We present a computer vision tool that analyses video from a CCTV system installed on fishing trawlers to monitor discarded fish catch. The system aims to support expert observers who review the footage and verify numbers, species and sizes of discarded fish. The operational environment presents a significant challenge for these tasks. Fish are processed below deck under fluorescent lights, they are randomly oriented and there are multiple occlusions. The scene is unstructured and complicated by the presence of fishermen processing the catch. We describe an approach to segmenting the scene and counting fish that exploits the $N^4$-Fields algorithm. We performed extensive tests of the algorithm on a data set comprising 443 frames from 6 belts. Results indicate the relative count error (for individual fish) ranges from 2\% to 16\%. We believe this is the first system that is able to handle footage from operational trawlers