Bank of Lithuania
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No 88
2021-03-31

What Moves Treasury Yields?

  • Abstract

    We characterize the joint dynamics of a large number of macroeconomic variables and Treasury yields in a dynamic factor model. We use this framework to identify a yield curve news shock as an innovation that does not move yields contemporaneously but explains a maximum share of the forecast error variance of yields over the next year. This shock explains more than half, and along with contemporaneous shocks to the level and slope of the yield curve, essentially all of the variation of Treasury yields several years out. The news shock is associated with a sharp and persistent increase in implied stock and bond market volatility, falling stock prices, an uptick in term premiums, and a prolonged decline of real activity and inflation. The accommodative response by the Federal Reserve leads to persistently lower expected and actual short rates. Treasury yields do not react contemporaneously to the yield curve news shock as the positive response of term premiums and the negative response of expected short rates initially offset each other. Identified shocks to realized and implied financial market volatility imply essentially the same impulse responses and are highly correlated with the yield news shock, suggesting that they act as unspanned or hidden factors in the yield curve.

    Keywords: term structure of interest rates, yield curve, news shocks, uncertainty shocks, structural vector autoregressions, factor-augmented vector autoregressions.                                                                                                                                                                  
    JEL codes: C55, E43, E44, G12.

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

No 31
2020-01-21

The Challenges of Lithuania’s Economic Convergence and Labour Market

  • Abstract

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


    Available only in Lithuanian

     

No 17
2019-12-13

Convergence and growth decomposition: an analysis on Lithuania

  • Abstract

    We study the behaviour of Lithuania relative to other 25 EU countries, looking specifically at convergence in terms of GDP per capita and its growth accounting components: capital accumulation, labour and its subcomponents, i.e. participation and employment, and the Total Factor Productivity (TFP). We find that Lithuanian Real GDP per capita shows indeed a convergence path similar to the other Baltic States and they all belong to the second club (includes part of the periphery and the other new member states). The convergence paths of labour or capital accumulation do not seem significantly different compared to the ones of other EU members. The Lithuanian transition path in TFP has become plateau after the crisis but this is seemingly not a divergence factor. Two components show noticeable changes in behaviour after 2010: the growth in total factor productivity (TFP) considerably slows down, and the employment-population ratio appears to increase accounting for around one third of the annual GDP growth in Lithuania. In addition, we explore several transition scenarios for Lithuania to the EU-25 average.

    JEL Codes: O47, F15, F45.

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

No 12
2019-05-29

Sectoral production and diffusion index forecasts for output in Lithuania

  • Abstract

    In this paper, we develop and describe quarterly data on disaggregated sectors in Lithuania which covers the period 1998-2018. The data is useful for empirical studies requiring panels with a large number of time observations and a large number of cross-sectional units. We follow the NACE2 level of disaggregation in developing our data, thus allowing us to combine the data with world input-output tables which we in turn use to identify the hubs and the main importing and exporting sectors within the economy. The data is then used for forecasting the growth rate of GDP. There is a substantial increase in the degree of covariation among sectoral production growth rates, which is observed using a split sample around 2008. When we apply factor methods, we find that this strong covariation can be explained by a few factors which are closely correlated to the growth of the retail and wholesale sectors. For GDP growth, the forecasts we consider are the diffusion index forecasts produced using a few indexes that summarize sectoral data, and the forecasts produced using the production growth of selected hubs and importing and exporting sectors. We find that the diffusion indexes and the production growth of sectors that make heavy use of imported inputs in their production have interesting forecasting power for the growth rate of GDP in the 2006-2011 and 2012-2018 periods.

    JEL Codes: E27, E37, C3, C67.

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