Publications

This paper analyses the link between migration and sizes of countries. It explains why larger countries (in terms of population) have lower shares of migrants in their populations. First, the data is analysed; next, a macroeconomic model with international trade and migration, explaining the stylised facts, is developed. The model includes country size, which gives rise to cheaper country-specific goods produced in a large country relative to the goods produced in a smaller country. Higher wages in the small country spur immigration to it.

JEL Codes: F16, F22.

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

The paper presents a short-term Lithuanian inflation forecasting model for predicting monthly inflation of 5 main HICP subgroups. We model inflation employing a set of univariate equations, which are mainly based on firms’ mark-up pricing. We make use of disaggregate HICP data, consisting of 92 price series, which naturally evokes discussion of potential pros and cons of forecasting disaggregate series vs. forecasting an aggregate. Besides exploring potential gains of using disaggregate data, we are also interested in the international commodity prices transmission mechanism, which we implement employing a distributed lag model. To examine the performance of model’s forecasts, we employ a recursive pseudo real-time out-of–sample forecasting exercise, generating inflation forecasts up to 15 months ahead. We find that our suggested set of univariate equations produce more accurate forecasts than the competing factor model, VARX model and various benchmark models for all 5 HICP subgroups.

JEL Codes: C52, C53, E37.

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

In the recent debate about the channels for growth there are two different aspects to take into account: the importance of the exports in services and the sophistication of the exports themselves. We analyze in this paper the situation of exports in Lithuania, highlighting the modern/high-tech sectors with a special focus on services.
We found that the percentage of high-tech merchandise exports on the total exports increased in general the last decade, from less than 4% of total merchandise exports to more than 10%.The exports in modern services are instead the only one which experienced a positive growth rate in the worst period of the crisis (2009). Looking at the foreign demand for services, especially for modern services, this changed a lot in the last decade. The main partner for modern services now is Germany, while for total service exports the first destination is Russia, as it is for total goods. For total goods other countries not in the EU mattered more in 2004 than nowadays. Non-EU countries are becoming crucial for exports in modern services.
In addition, we provide a simple econometric setup in which we study the impact on the different type of real exports of the Real Effective Exchange Rate (REER, deflated in several ways) and the trade-weighted (weighted for trade in services and in goods) foreign demand based on real GDP data or Gross Value Added. The exports of modern services seem to be not explained by the competitiveness or demand factor. In our setups the main determinant is the exports in the previous period. The outcome for modern services is quite robust across the specifications and with different REERs. The exceptions concern the setup with GDP foreign demand estimated by OLS, in which also the foreign demand seems to play a role for exports in modern services. For exports in total services and traditional services instead, foreign demand matter in more cases and using different REERs especially with a more general measure of foreign demand based on real GDP.
The REER for goods and services exports matters more if deflated by CPI both in case of GDP and Gross Value Added-based foreign demand, and mostly in the short-run.

JEL Codes: F14, F43, C22.

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

In this paper, we analyze how borrower characteristics influence the choice between the short-term fixed-rate mortgage (STFRM) and the long-term fixed-rate mortgage (LTFRM) types in an emerging market. We use the national Survey of Households with Housing Loans conducted by the Bank of Lithuania between 2009 and 2012. This paper is the first to empirically test the findings of Campbell and Cocco in an emerging market. Following the model of Campbell and Cocco (2003), our analysis focuses on the interaction of demand and supply. We focus on the contract outcome; we do not specify who initiated such outcome – the household or the mortgage provider. We supplement the findings of previous literature by testing the model among financially constrained households in a different economic and institutional setting; that is, in Lithuania.
We define financial constraints of a household in multiple ways: high mortgage payment-toincome ratio, low residual income, high loan-to-value ratio, absence of savings, existence of other obligations, a single breadwinner in the household, and the existence of dependants in the household. Estimates based on these measures indicate that constrained households are more likely to choose a safer, but more expensive, long-term interest rate mortgage. Our results are in line with Campbell and Cocco’s (2003) suggestion that, when borrowing constraints are binding, financially constrained households should choose a long-term interest rate mortgage. Our results contradict the empirical evidence of Coulibaly and Li (2009), of Damen and Buyst (2013), of Ehrmann and Ziegelmeyer (2013), and of Hullgren and Söderberg (2013) that financially constrained households prefer short-term interest rate mortgages. We argue that the difference arises because of institutional features.
Our study adds to existing literature by showing that, in the world where borrowing constraints are binding, financially constrained households have the safer mortgage type that reduces consumption shock and liquidity risks. 

This paper presents a simple condition for optimal asymmetric labour (capital) taxation/subsidization in a two-sector model with logarithmic utilities and Cobb-Douglas production functions, linked to demographic factors: fertility rate and longevity. The paper shows that depending on parameter values, it may be optimal to tax or subsidize labour in the sectors. If it is optimal to tax the investment-goods sector, a Pareto-improving tax reform is possible. Larger output elasticities of capital in the sectors reduce the possibilities of a Pareto-improving reform, while population ageing in terms of higher longevity enhances the possibilities of welfare improvement for all generations. Fertility rates do not affect optimal taxation.

JEL Codes: E62, H21, J10.

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

The paper provides estimates for the New Keynesian Phillips curve (NKPC) in Lithuania. The paper considers the baseline and hybrid NKPC, the latter accounting for inflation inertia, under the closed and open economy frameworks. The estimates highlight the importance of expected and lagged inflation in the inflation formation process. The role of real marginal cost is found to be limited in shaping the dynamics of inflation. The study yields estimates for the underlying characteristics of pricing behaviour in Lithuania. The estimates show that the price duration stands at around 2.2–2.8 quarters, while the fraction of firms that adjust prices in a backward looking way amounts to around one third.

JEL Codes: D40, E30.

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

This paper focuses on short-term Lithuanian GDP forecasting using a large monthly dataset. The forecasting accuracy of various factor model specifications is assessed using the out-of-sample forecasting exercise. It is argued that factor extraction by using a simple principal components method might lead to a loss of important information related GDP forecasting, therefore, other methods should be also considered. Performance of several factor models, which relate the factor extraction step to GDP forecasting, was tested. The effect of using weighted principal components model, with weights depending on variables’ absolute correlation with GDP, was explored in greater detail. Although factor models performed better than naive benchmark forecast for GDP nowcasting and 1 quarter ahead forecasting, we were unable to set up the ranking among different factor model specifications. We also find that a small scale factor model with 5 variables (which could be regarded as the most important monthly variables for GDP nowcasting) is able to nowcast GDP better than models with a full data set of 52 variables, which might indicate that for the case of the Lithuanian economy, a smaller scale factor models may be more suitable.

JEL Codes: C22, E37.

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

This paper explores the behavior of profits in the four largest euro area countries (Germany, France, Italy and Spain) and the euro area as a whole, while at the same time considering three main sectors (manufacturing, construction and services) in each economy over the period 1988–2010. The paper presents stylized facts about profit developments and, applying a vector autoregressive modeling framework, discusses the sensitivity of profits to four distinctive structural shocks (a demand shock, an employment shock, a wage and price mark-up shocks). In addition, it provides the shock decomposition of historical developments in profits across countries and sectors.

JEL Codes: C32, E23, E25.

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

The paper examines price setting in Lithuania based on ad hoc survey of the Bank of Lithuania “On Price and Wage Setting”. The study extends the survey data analysis presented in Virbickas (2009). The paper points to the incidence of both the time-dependent and the state-dependent price reviewing policies used by the investigated firms, though the price reviewing practices appear to be somewhat tilted to the state-dependent pricing. Analysis provides evidence on the reasons for upward and downward stickiness of prices. Delayed price adjustment is found to be mostly related to the price adjustment stage rather than the price reviewing stage. The most momentous explanations for not adjusting prices upwards or downwards rest on the cost-based pricing and the explicit contracts. The study finds an asymmetric influence of some of the price factors. In particular, the cost factors are found to be decisive in invoking the price increase rather than the price decrease.

JEL Codes: D40, E30.

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

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.