Bank of Lithuania
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Discussion Paper Series

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Discussion papers disseminate economic research relevant to the tasks and functions of the Bank of Lithuania and of the European System of Central Banks. One of the main objectives of the series is to deepen the understanding of policy-relevant questions and stimulate more in-depth expert discussions by offering a more rigorous analysis of an issue under review. The research featured in the Discussion Paper Series provides a theoretically and empirically founded basis for policy-making. Discussion papers help to develop and strengthen collaboration between the Bank of Lithuania and other central banks, Lithuanian and foreign institutions acting in the fields of economic policy, analysis and/or research.

Papers are only available in English.

No 26

Business cycles in the EU: A comprehensive comparison across methods

  • Abstract

    Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this paper, we investigate different methods of assessing business cycles for the European Union in general and the euro area in particular. First, we conduct a Monte Carlo experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analyzed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, we used the structural model’s parameters calibrated to the euro area’s real GDP and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models consisting of popular Hodrick-Prescott, Christiano-Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. We find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and business cycles; and (iii) the best-performing Monte Carlo approaches provide a reasonable combination as the suite of models. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the business cycle for real GDP. Second, we estimate the business cycles for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. Our analysis also confirms that the business cycles of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronised with the aggregate euro area.

    JEL Codes: C31, E27, E32.

    The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

No 25

ECB Communication: What Is It Telling Us?

  • Abstract

    This paper examines changing ECB communication and how it has impacted euro area financial markets over the past two decades. We applied a combination of topic modelling and sentiment analysis for over 2000 public ECB Executive Board member speeches, as well as over 200 ECB press conferences. Topic analysis revealed that the ECB’s main focus has shifted from strategy and objectives, at the inception of the euro area, to various policy actions during the global financial crisis and, more recently, to instruments and economic developments. Sentiment analysis showed an expected trend of a more negative communication tone during periods of turmoil and a gradual shift to a more dovish monetary policy tone over time. Regression analysis revealed that sentiment indices had the expected impact on financial market indicators, while press conferences showed substantially stronger effects than speeches.

    JEL Codes: C80, E43, E44, E58, G12.

    The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

No 24

Natural real rates of interest across euro area countries: Are R-stars getting closer together?

  • Abstract

    Using two different methodologies, we estimate time-varying natural real rates of interest for a majority of euro area (EA) countries, including Lithuania. We find that natural real rates have been declining, particularly since 2008, albeit to different extent across EA countries. Lower rates could (at least partly) be explained by lower productivity and population growth. In line with previous literature, we find evidence of a substantial dispersion of the natural interest rate across EA economies. This became especially evident during the financial crisis of 2008-2009 and the sovereign debt crisis of 2010-2012, while estimates of natural rates tend to converge during "calm" periods. Estimates of natural rates for Lithuania were significantly above the estimates of core EA countries over 2002-2008, but this has changed after the crisis. From 2011 the estimates of natural rates for Lithuania tend to be close to the average for EA countries.

    JEL Codes: C32, E32, E43, E52.

    The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

No 23

A First Glance at the Minimum Wage Incidence in Lithuania using Social Security Data

  • Abstract

    This document explores the incidence of the minimum wage in Lithuania. The descriptive analysis exploits high-frequency data on monthly labor income coming from Social Security records between July 2013 and July 2020 to characterize (i) the evolution of the monthly minimum wage, (ii) the percentage of workers who earn the minimum wage, (iii) the bite of the minimum wage in the wage distribution, and (iv) the heterogeneity of the findings with respect to gender and age. The evidence shows that the minimum wage was raised 7 times with an average (real) increase of 7.3% and, on average, less than 10% of the workers earn at most the minimum wage but low-pay incidence is around 20%. In terms of the impact of the wage distribution, the minimum wage relative to the average wage in the economy fluctuates between 45 and 50 percent. Females and young workers exhibit a larger low-pay incidence and minimum wage bite.

    JEL Codes: J38, J48

    The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.