Income inequality and private bank credit in developed economies JRC Working Papers in Economics and Finance, 2017/6

The influence of financial deepening on income inequality in developed economies is studied with particular interest in the European Union member states that have large penetration of bank credit. Building on the model of financially open economies (Kunieda et al, 2014) and extending its implications for the top-income shares, it is shown that a simultaneous increase in private bank credit relative to the gross domestic product (GDP) and the gap between real interest rate and GDP growth rate increases inequality, as measured by both the Gini index and the top-income shares. To establish the effect on the top-income shares, a simultaneous estimation procedure is proposed that exploits the implications of the fact that a higher income range is well-characterized by the Pareto distribution.JRC.B.1-Finance and Econom

Monopolistic Competition, International Trade and Firm Heterogeneity - a Life Cycle Perspective

This paper presents a dynamic international trade model based on monopolistic competition, where observed intra-industry differences at a given point in time reflect different stages of the firm’s life cycle. New product varieties of still higher quality enter the market every period rendering old varieties obsolescent in a process of creative destruction. For given technology (variety) production costs decrease after an infant period due to learning. It is shown that several patterns of exports may arise depending primarily on the size of fixed trade costs. At a given point in time firms therefore differ due to different age, although all firms are symmetric in a life cycle perspective. The paper thus offers an alternative view on firm heterogeneity compared with other recent papers, where productivity differences appear as an outcome of a stochastic process.Product innovations; learning; creative destruction; firm heterogeneity; export performance

Finance and economic growth: financing structure and non-linear impact JRC Working Papers in Economics and Finance, 2017/7

There is growing evidence that the impact of financial development on economic growth might be non-linear and hump-shaped, exhibiting a turning point. However, such findings are typically established using total finances (mostly: credit), and the apparent non-linear impact of totals can stem from a substantial structural change in the composition of finances, that has been taking place during the recent decades. Though there are some studies going beyond total finances, they usually look at the impact of certain financing components separately or using ratios, which may bias the estimation and lead to incorrect conclusions. Finally, the findings are typically based on a global pool of countries, and may be driven by a developing versus developed country differential. Focusing on groups of high-income countries (from the OECD, EU, and EMU), this study shows that the finding of a non-linear, hump-shaped impact of financing on economic growth is robust to controlling for financing composition in terms of the sources (bank credit, debt securities, stock market) and the recipients of finances (households, non-financial and financial corporations), or both. In particular, we obtain the following results. (1) The non-linear impact of total bank credit is more pronounced than that of either household credit alone, or the sum of bank credit, debt securities, and stock market financing. (2) Credit to non-financial corporations tends to have a positive, while credit to households a negative impact on growth, even after allowing for non-linearities. (3) Debt-securities and stock market-based financing have a different impact on growth. (4) The estimated turning point of the non-linear relationship is close to that found by Cournède and Denk (2015) for the OECD countries, and lower than that established by Arcand et al. (2015) for a broad set of countries.JRC.B.1-Finance and Econom

China's WTO accession and income inequality in European regions: External pressure and internal adjustments

Exports from China have surged substantially since its accession to the World Trade Organization in 2001. We investigate how this expansion affected income inequality within European regions by separating the trade pressure experienced in external and domestic markets, as well as exploring the importance of several economic mechanisms. Despite some intermediate adjustments, softening the influence of Chinese pressure and even facilitating European exports, we establish a significant increase of inequality that is concentrated mostly in the lower part of regional income distributions. We determine a significant channeling of the trade pressure to income inequality through the shrinking manufacturing sector, the increasing unemployment rate, and the technological upgrade of manufacturing exports, together with an increasing demand for better-qualified labor

Convergence of income distributions: Total and inequality-affecting changes in the EU

By adapting the statistical framework suggested by Székely and Rizzo (2004) and considering the convergence of income distributions instead of aggregate (e.g., average) income, we exploit the scale-independence property of proper inequality metrics to evaluate not only the total but also the inequality-affecting (shape-influenced) convergence of income distributions. We illustrate the application using Monte Carlo experiments and characterizing the convergence of net equivalized income distributions among European Union member states

China's WTO accession and income inequality in European regions: External pressure and internal adjustments

Exports from China have surged substantially since its accession to the World Trade Organization in 2001. We investigate how this expansion affected income inequality within European regions by separating the trade pressure experienced in external and domestic markets, as well as exploring the importance of several economic mechanisms. Despite some intermediate adjustments, softening the influence of Chinese pressure and even facilitating European exports, we establish a significant increase of inequality that is concentrated mostly in the lower part of regional income distributions. We determine a significant channelling of the trade pressure to income inequality through the shrinking manufacturing sector, the increasing unemployment rate, and the technological upgrade of manufacturing exports, together with an increasing demand for better-qualified labor.JRC.B.1-Finance and Econom

Agreguotų AR(1) procesų autoregresijos parametro tankio vertinimo metodų palyginimas

The article investigates the properties of two alternative disaggregation methods. First one, proposed in Chong (2006), is based on the assumption of polynomial autoregressive parameter density. Second one, proposed in Leipus et al. (2006), uses the approximation of the density by the means of Gegenbauer polynomials. Examining results of Monte-Carlo simulations it is shown that none of the methods was found to outperform another. Chong’s method is narrowed by the class of polynomial densities, and the secondmethod is not effective in the presence of common innovations.Bothmethodswork correctly under assumptions proposed in the corresponding articles

Forced Running Endurance Is Influenced by Gene(s) on Mouse Chromosome 10

Acknowledgments The authors wish to acknowledge technical assistance from Mrs. Indrė Libnickienė and intellectual input from Dr. David A. Blizard. This research was funded by the European Social Fund under the Global Grant measure. Grant VP1-3.1-ŠMM-07-K-02-057 was awarded to AL.Peer reviewedPublisher PD

Baltijos šalių akcijų kainų sektorinių indeksų prognozavimas

Extending the research started in [31], the paper uses econometric methods for the short-term forecasting of quarterly values of sector indexes of stock prices from the OMX Baltic stock exchange. The ARMA models and modelling methodology that was used to build the statistical models in the previous paper are now augmented with the algorithms of time series aggregation and identification of special features of the series. Here, the search for informative factors relies on the study of related literature. The specification of models is further tailored using the traditional significance (p-value) analysis of regressors and a cross-validation analysis. The latter is implemented in this paper using the Jack-knife approach. The data period analysed covers the years 2000–2013. The results of the analysis indicate that the inclusion not only of recent autoregressive terms but also of some aggregated characteristics (as certain special features of indexes) improves the precision of forecasting substantially. The calculations were performed using the statistical analysis software SAS.Straipsnis skirtas OMX Baltijos vertybinių popierių (VP) rinkos akcijų kainų sektorinių indeksų ketvirtinių reikšmių trumpalaikiam prognozavimui ekonometriniais metodais ir yra ankstesnio straipsnio [31] tęsinys. Ankstesniame straipsnyje matematinių modelių sudarymui taikyta ARMA metodika šiame darbe yra papildyta laiko eilučių agregavimo ir ypatingų požymių išskyrimo algoritmais. Informatyvių veiksnių pirminė atranka vykdoma remiantis specialios literatūros apžvalga. Po to modelių specifikacija tikslinama, naudojant tradicinius regresorių statistinio reikšmingumo dydžius (p-value) ir kryžminės patikros metodą. Pastarasis būdas šiame darbe realizuotas naudojant Jack-knife algoritmą. Tyrimui naudoti 2000–2013 m. laikotarpio duomenys. Gauti rezultatai rodo, kad sudarant autoregresinius modelius yra tikslinga į lygčių dešiniąsias puses įtraukti ne tik praeities stebinių duomenis, bet ir jų agreguotas statistikas (indeksų raidos ypatingus požymius) – tai žymiai pagerina prognozavimo tikslumą. Skaičiavimai atlikti su statistinės analizės sistema SAS

Mixed Frequency Data Sampling Regression Models: The R Package midasr

When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002). In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression