Publications

In this paper we analyse determinants of the recent boom-and-bust cycle of the Lithuanian economy with the help of a medium-sized macroeconometric model that incorporates a functional financial block. Special emphasis is put on the role of credit market conditions during the overheating episode. We quantitatively estimate the impact of credit conditions and externally funded bank lending on macroeconomic developments. There is evidence that easy credit conditions and active credit expansion contributed moderately to real economic growth but significantly added to overheating pressures by pushing up real estate prices, encouraging concentration of labour and capital into procyclical sectors and increasing private sector’s debt burden. During the boom episode buoyant external environment provided strong background for export-led growth, which was later strongly affected by temporary foreign trade collapse at the outset of the economic crisis. Model results also suggest that government’s discretionary fiscal policies may have contributed to economic overheating and severity of the ensuing crisis by not adopting sufficiently prudent fiscal stance during the boom episode. The model confirms that more favourable interest rate environment and accommodating fiscal policies are important for providing a temporary relief for the crisis-stricken economy but deep structural transformation of the economy is needed for the sustainable recovery to take hold.

JEL Codes: E10, E17, E37, E51.

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

In view of the increasing use of Dynamic Stochastic General Equilibrium (DSGE) models in the macroeconomic projections and the policy process, this paper examines, both conceptually and empirically, alternative notions of potential output within DSGE models. Furthermore, it provides historical estimates of potential output/output gaps on the basis of selected DSGE models developed by the European System of Central Banks’ staff. These estimates are compared to the corresponding estimates obtained applying more traditional methods. Finally, the paper assesses the usefulness of the DSGE model-based output gaps for gauging inflationary pressures.

JEL Codes: E32, E37, E52.

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

The new generation of dynamic stochastic general equilibrium (DSGE) models seems particularly suited for conducting scenario analysis. These models formalise the behaviour of economic agents on the basis of explicit micro-foundations. As a result, they appear less prone to the Lucas critique than traditional macroeconometric models. DSGE models provide researchers with powerful tools, which allow for the designing of a broad range of scenarios and tackling a large range of issues, offering at the same time an appealing structural interpretation of the scenario specification and simulation results. The paper provides illustrations on some of the modelling issues that often arise when implementing scenarios using DSGE models in the context of projection exercises or policy analysis. These issues reflect the sensitivity of DSGE model-based analysis to scenario assumptions, which in more traditional models are apparently less critical, such as, for example, scenario event anticipation and duration, treatment of monetary and fiscal policy rules.

JEL Codes: E32, E52, E62, E37.

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

This paper investigates the wage and price setting behaviour of Lithuanian firms on the basis of an ad hoc survey “On Price and Wage Setting” undertaken by the Bank of Lithuania. The paper provides survey evidence on the frequency of wage and price changes. The frequency of wage changes turns out to be higher in firms that apply collective pay agreements, while the frequency of price changes appears to be positively affected by the market competition. Labour cost share is not found to be significant in making the impact on the frequency of price changes. This paper also investigates the role of certain technological, institutional and other factors in shaping firms’ responses to a negative demand shock, an intermediate input cost shock and a wage shock. A higher labour cost share is found to increase the likelihood of a price increase following a wage shock. Flexible wage components mitigate firms’ responses to a slowdown in demand and an intermediate input cost increase. The behaviour of firms following the investigated shocks is also affected by the level of competition. The role of collective pay agreements appears to be rather limited in shaping responses of firms to the shocks.

JEL Codes: D40, J30.

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

In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision making, application of the Q-learning algorithm for driving individual behaviour, and rich market setup.

JEL Codes: G10, G11, G14.

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

This paper presents preliminary estimates of the euro area flexible-price output gap using the estimated version of the New Area-Wide Model (NAWM) – a large-scale DSGE model of the euro area developed and maintained by ECB staff. Following a definition of the flexible-price output gap frequently used in the literature, we show that the NAWM-based measure may at times differ quite considerably from more traditional output gap measures and may display fluctuations of larger amplitude. The dynamics of flexible-price output is mainly driven by shocks to technology, whereas fluctuations in the output gap can be attributed equally to supply and demand shocks. We analyse how robust this output gap estimate is with respect to new incoming data and compare it’s inflation forecast performance with alternative measures.

JEL Codes: C11, C32, E31, E32.

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

The goal of this paper is to examine the dynamic effects of fiscal instruments in Lithuania on the economy and welfare. In the analysis, a calibrated dynamic stochastic general equilibrium model for Lithuania is employed. The calculation implies that 9-16 percent of tax cuts are self-financing in the long run. It suggests that the slope of Laffer curve in Lithuanian economy is rather flat. The analysis of effects of different tax cuts shows that the impact of 1 percentage point permanent decrease in statutory tax rate on gross domestic product is very small (within the range of –0.15 through 0.15 percent in all cases). The estimated government expenditure multiplier has a different sign in the long run when various financing sources are used to balance the government budget.

JEL Codes: E62, H24, H25.

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

In this paper we provide a brief introduction to the literature on agent-based financial modelling and, more specifically, artificial stock market modelling. In the selective literature review two broad categories of artificial stock market models are discussed: models based on hard-wired rules and models with learning and systemic adaptation. The paper discusses pros and cons of agent-based financial modelling as opposed to the standard representative-agent approach. We advocate the need for the proper account of market complexity, agent heterogeneity, bounded rationality and adaptive (though not simplistic) expectations in financial modelling. We also argue that intelligent adaptation in highly uncertain environment is key to understanding actual financial market behaviour and we resort to a specific area of artificial intelligence theory, namely reinforcement learning, as one plausible and economically appealing algorithm of adaptation and learning.

JEL Codes: G10, G11, G14, Y20.

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

In this paper, the economic impact of the 2006–2008 personal income tax (PIT) reform in Lithuania is analyzed applying model-based simulations. We find that the undertaken PIT reform is unsustainable as it leads to permanent government budget deficits and ever increasing public debt. This result holds even allowing for endogenous reduction in tax evasion. After introducing permanent compensatory fiscal measures ensuring long-term sustainability of the PIT reduction, we demonstrate that the lower PIT produces higher output and lower prices in the long run. Higher domestic spending is supported by higher employment and after-tax wages. Moreover, following a reduction in the marginal production costs, producer prices fall enhancing economy’s international competitiveness and boosting domestic exports. Pre-announcement of the tax reform implies early macroeconomic reaction, and thus in most cases smoother adjustment of the economy to the tax change.

JEL Codes: E62, H24, H25, H26.

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

This paper evaluates different models for the short-term forecasting of real GDP growth in ten selected European countries and the euro area as a whole. Purely quarterly models are compared with models designed to exploit early releases of monthly indicators for the nowcast and forecast of quarterly GDP growth. Amongst the latter, we consider small bridge equations and forecast equations in which the bridging between monthly and quarterly data is achieved through a regression on factors extracted from large monthly datasets. The forecasting exercise is performed in a simulated real-time context, which takes account of publication lags in the individual series. In general, we find that models that exploit monthly information outperform models that use purely quarterly data and, amongst the former, factor models perform best. 

JEL Codes: E37, C53. 

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