Against a General Theory of Populism: The Case of East-Central Europe

Intereconomics - Social science, like all other sciences, is supposed to explore the substance below the surface of things. However, populism discourse in general is stuck on the surface, focusing...     

Instrumental measurement of the cognitive and emotional background of deception

Quality & Quantity - The aim of this study is to instrumentally explore the cognitive and emotional load behind deception. According to literature, the cognitive and emotional load comprises of...     

Too-Big-To-Fail: Why Megabanks Have Not Become Smaller Since the Global Financial Crisis?

ABSTRACT

More than ten years after the global financial crisis, what has happened to the ‘too-big-to-fail’ (TBTF) banks whose reckless behavior was among its preconditions, but which received public support and guarantees in the midst of that crisis? Insofar as this too-big-to-fail status helped create the crisis and then imposed costs on the rest of society, we would expect these banks to have shrunk. We investigate the evolution of 31 global-TBTF banks and find that their overall size has hardly recorded any substantial change. However, there is no sense of urgency in the flourishing post-crisis literature on TBTF banks about the need to contain their size; the prevalent view therein is that if properly regulated, the risks that arise from a financial system dominated by TBTF banks are manageable. This view rests on the same overly narrow theoretical underpinnings whose flaws were exposed in the crisis. We argue that too-big-to-fail banking is embedded in a set of self-reinforcing policies—consolidation, balance-sheet support through quantitative easing, favorable regulations, bank lobbying, and geo-economic and geo-political considerations—which explain why these banks have not shrunk and why they remain a threat to financial stability, well after the lessons of the crisis should have been learned.     

Estimating the U.S. output gap with state‐level data

This paper develops a method to estimate the U.S. output gap by exploiting the cross‐sectional variation of state‐level output and unemployment rate data. The model assumes that there are common output and unemployment rate trend and cycle components, and that each state's output and unemployment rate are subject to idiosyncratic trend and cycle perturbations. I estimate the model with Bayesian methods using quarterly data from 2005:Q1 to 2018:Q2 for the 50 states and the District of Columbia. Results show that the U.S. output gap reached about negative 4.6% around the years of the Great Recession and was about 0.9% in 2018:Q2.     

Dynamics of price level changes in the Visegrad group: comparative study

Quality & Quantity - In Poland, as in many other countries, to measure inflation, expressed both by the CPI and HICP, the Laspeyres price index with weights from the base period is used. Thus...     

The evolution of world trade from 1995 to 2014: A network approach

This paper employs network analysis to study world trade from 1995 to 2014. We focus on the main connective features of the world trade network (WTN) and their dynamics. Results suggest that countries’ efforts to attain the benefits of trade have resulted in an intertwined network that is increasingly dense, reciprocal, and clustered. However, these features do not correspond to a linear aggregation of the characteristics of its constituents (trade sectors). Trade linkages are distributed homogeneously among countries, but their intensity (i.e. their value) is highly concentrated in a small set of countries. The main connective features of the WTN were not affected by the 2007–2008 international financial crisis. However, we find that the crisis marks a turning point in the evolution of the WTN from a two-group (led by the US and Germany) to a three-group (led by the US, Germany, and China) hierarchical structure.     

Bank market power and the intensity of borrower discouragement: analysis of SMEs across developed and developing European countries

Small Business Economics - This paper analyzes the effect of bank market power on the financial constraints of small and medium-sized enterprises (SMEs) through the study of borrower...     

On the peer effects of star students

We estimate the effect that star students have on their siblings’ learning outcomes, measured by their high school grade point average (GPA) and their math grades. To this end, we couple administrative school data on grades with an unusual natural experiment in Peru that generates exogenous variation in the presence of star students at home. We find that star students increase their siblings’ GPA by 0.33 SDs and their math grades by 0.22 SDs. The effect size is inversely related to number of siblings, suggesting that the remaining siblings act as substitutes for the star student.     

Tests of Normality of Functional Data

The paper is concerned with testing normality in samples of curves and error curves estimated from functional regression models. We propose a general paradigm based on the application of multivariate normality tests to vectors of functional principal components scores. We examine finite sample performance of a number of such tests and select the best performing tests. We apply them to several extensively used functional data sets and determine which can be treated as normal, possibly after a suitable transformation. We also offer practical guidance on software implementations of all tests we study and develop large sample justification for tests based on sample skewness and kurtosis of functional principal component scores.     

A Geometrical Interpretation of Collinearity: A Natural Way to Justify Ridge Regression and Its Anomalies

Justifying ridge regression from a geometrical perspective is one of the main contributions of this paper. To the best of our knowledge, this question has not been treated previously. This paper shows that ridge regression is a particular case of raising procedures that provide greater flexibility by transforming the matrix X associated with the model. Thus, raising procedures, based on a geometrical idea of the vectorial space associated with the columns of matrix X , lead naturally to ridge regression and justify the presence of the well-known constant k on the main diagonal of matrix X ^′ X . This paper also analyses and compares different alternatives to raising with respect to collinearity mitigation. The results are illustrated with an empirical application.