Bank of Lithuania
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No 31

Households' inflation expectations in Lithuania: A First look and overview

  • Abstract

    We document a number of novel stylised facts about Lithuanian households' inflation expectations. Inflation expectations of Lithuanian households are significantly above the recent observation of actual inflation. On average, year-on-year inflation was around 4 percent from 2004 to 2023. However, one-year-ahead inflation expectations of households over the same period were on average 16.9 percent. Although we observe a clear upward bias in inflation expectations, there is significant co-movement between actual inflation and inflation expectations of households. Additionally, we find that over the economic boom, inflation expectations are higher than inflation perceptions, a finding that reverses over the economic downturn. We build a VAR model to analyse whether and how inflation, households' inflation expectations/perceptions and unemployment are linked. We show that structural shocks to inflation expectations play a minor role in overall inflation and unemployment dynamics.

    Keywords: Households' inflation perceptions, inflation expectations.
    JEL Classification: C83, D12, E21, E31.

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 80

Assessing the impact of macroprudential measures: The case of the LTV limit in Lithuania

  • Abstract

    In this paper, we adopt a dual micro-and-macro simulation strategy to assess the impact of introducing (or changing) the LTV limit. Due to the nature of borrower-based macroprudential measures, to assess this impact we need to use borrower-level micro data. Tightening (or loosening) the LTV limit increases the share of borrowers constrained by the policy measure in question; thus, the overall impact depends on initial market conditions. We find that the introduction of an LTV limit of 85 % in 2011 had a modest short-term impact on economic activity because the new regulatory limit was non-binding for most borrowers at the time. We estimate that if the LTV limit would not have been introduced, the household loan portfolio would have grown on average 1.5 percentage points faster per year (over 2012-2014). This would have led to a 0.5 percentage point higher housing price growth and a 0.2 percentage point higher real GDP growth. When the macroprudential LTV limit is binding for a significant portion of borrowers, lowering the LTV limit at current market conditions has a much more pronounced effect. We show that if the LTV limit had been implemented at the end of 2004, it would have substantially helped in tempering the credit and housing boom, albeit at the cost of lowering economic growth.

    Keywords: Financial stability, Macroprudential policy, Borrower-based macroprudential policy instruments, LTV limit.

    JEL Codes: C32, C53, E58, G28.

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

No 29

CBDC – in a whirlpool of discussion

  • Abstract

    The topic of central bank digital currency (henceforth - CBDC) has recently gained significant share of attention among policy makers and academics. A wide range of CBDC setups are discussed from the universally accessible central bank accounts or digital tokens to less extreme suggestions of only partly broadening central bank balance sheet access by providing CBDC to wholesale consumers or getting private sector to mediate in the process by providing synthetic CBDC.

    This paper recalls the possible CBDC implementation types that are discussed in the current context; reviews some of the discussions among those researching the topic; gives a brief overview of the next-step initiatives taking place among central banks with a potential to lay ground for the practical CBDC implementation; and discusses the main policy implications from financial stability and monetary policy perspectives.

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

No 28

Lithuanian house price index: modelling and forecasting

  • Abstract

    Timely monitoring of the housing market developments in Lithuania is one of the key elements in the analysis framework of the macroprudential authority aiming to contribute to financial stability in Lithuania. In this paper, we addressed three important questions related to Lithuanian house prices, namely, whether house prices are under- or over valuated, which explanatory variables have the biggest impact on the growth of house prices and what the future development of the Lithuanian house price index could be. Three separate modelling and forecasting exercises were performed in order to tackle these questions. The first exercise employs the vector error correction modelling (VECM) approach to assess under- or overvaluation of the house prices. We then use an autoregressive distributed lag model (ARDL) to evaluate which explanatory variables have the biggest impact on house price growth. As the last exercise, we develop a suite of models that are used to forecast future development of the house price index. The analysis presented in this paper may be viewed as a further step towards more formalised modelling and forecasting of the Lithuanian house price index.

    JEL Codes: C22, C32, C53, E37, R30.

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