Publications

This paper proposes and tests a new source of time variation in real investment opportunities, namely long-run shocks to the productivity of the investment sector, to explain the joint behavior of macroeconomic quantities and asset prices. A two-sector general equilibrium model with long-run investment shocks and wage rigidities produces both positive co-movement among key macroeconomic variables and a sizable return volatility differential between the investment and consumption sector. Moreover, positive long-run investment shocks are associated with low marginal utility and thus command a positive risk premium. We test our model using data on sectoral TFP and find evidence in support of our theoretical predictions.

JEL Codes: E32, G12.

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 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.

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.