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Abstract
This paper considers the role of financial information in the estimation and dynamics of the US output gap over more than a century. To this end, we extend the parsimonious approach of Borio, Disyatat, and Juselius (2016, 2014) to allow for time-varying effects of financial factors. This novel feature significantly improves real-time estimates of the output gap. It signals the peak and trough in economic activity related to both the Great Recession and the Great Depression. Two major insights follow. Credit dynamics are the primary drivers of the observed financial crisis, albeit with different conduits over the century: the stock market in 1929 and the housing market in 2008. Accounting for credit growth, the US potential growth has been stable at 2% since the beginning of 1980.JEL Codes: C11, C32, E32, O47.The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.
All results 6
No 51
2018-08-29
No 7
2018-05-14
Firm heterogeneity and macroeconomic dynamics: a datadriven investigation
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Abstract
In this paper we offer a unique firm-level view of the empirical regularities underlying the evolution of the Lithuanian economy over the period of 2000 to 2014. Employing a novel data-set, we investigate key distributional moments of both the financial and real characteristics of Lithuanian firms. We focus in particular on the issues related to productivity, firm birth and death and the associated employment creation and destruction across industries, firm sizes and trade status (exporting vs. non-exporting). We refrain from any structural modeling attempt in order to map out the key economic processes across industries and selected firm characteristics. We uncover similar empirical regularities as already highlighted in the literature: trade participation has substantial benefits on firm productivity, the 2008 recession has had a cleansing effect on the non-tradable sector, firm birth and death are highly pro-cyclical. The richness of the dataset allows us to produce additional insights such as the change in the composition of assets and liabilities over the business cycles (tilting both liabilities and assets towards the short-term) or the increasing share of exporting firms but the constant share of importing ones since 2000.
JEL Codes: D22, D24, E30, J21, J24, J30, L11, L25.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.
No 6
2018-05-04
Network-based macro fluctuations: Evidence from Lithuania
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Abstract
Do inter-sectoral linkages of intermediate products affect the spread of sectoral shocks at the aggregate level in Lithuania, a small and open economy? We answer this question by: i) constructing the domestic sector-by-sector direct requirements table using the Lithuanian interindustry transactions tables, and ii) applying Acemoglu et al. (2012)'s network-based methodology and Gabaix and Ibragimov (2011)'s modified log rank-log size regression to analyse the nature of inter-sectoral linkages. Our results indicate that the direct and indirect inter-sectoral linkages cause aggregate volatility to decay at a rate lower than √n - the rate predicted by the standard diversification argument. Furthermore, indirect linkages play an important role in the above-mentioned process, supporting the findings of Acemoglu et al. (2012). These results suggest that the inter-sectoral network of linkages represent a potential propagation mechanism for idiosyncratic shocks throughout the Lithuanian economy.
JEL Codes: C13, C46, C67, E00.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.
No 45
2017-06-23
The knotty interplay between credit and housing
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Abstract
We employ the recent Jordà et al. (2016) and Knoll et al. (2017) datasets to investigate the long-run relationship between house prices and credit volume, allowing for interest rate, real exchange rate and real gross domestic product (GDP). We refine the analysis using more recent data at the quarterly-level to define relevant co-integrating relationships across a number of European economies. Housing, GDP and credit cross-sectional averages are included in the analysis to detect potential spill-over effects. Empirical results indicate cross-country heterogeneities and an uneven feedback mechanism between credit and housing – the full loop is established only for several countries in the dataset. Important results relate to the statistical properties of the housing time series. Grouping countries for panel-like econometric exercises may lead to spurious regression results, poor inference and misleading policy implications. Short-run dynamics, compared to the long-run may often lead to contradicting policy advice if the order of integration of the house price series is not properly accounted for. Accounting for spatial patterns of house prices which cannot be attributed to global output shocks may provide useful insights into policy making.
JEL Codes: C21, E51, O18, R31.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.
No 4
2017-04-24
Unemployment or credit: Who holds the potential? Results from a small-open economy
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Abstract
This paper investigates the importance of unemployment and credit in determining the potential level of real activity for a small-open economy with a low degree of financialization. We estimate a multivariate unobserved component model (MUC) to derive the potential output and its associated output gap for the Lithuanian economy. The model is estimated via Bayesian methods and the time-paths of unobserved variables are extracted via the Kalman filter. We find that the inclusion of unemployment into the MUC model substantially improves the estimates of output gap in real-time. Once information about unemployment is accounted for, adding information about credit does not substantially alter either the estimates of output gap or its performance in real time. We uncover a strong negative correlation between the model-implied unemployment gap (without credit) and real credit growth. This explains the relatively muted impact of the financial variable on the level and dynamics of the output gap. Data revisions appear not to be the primary source of revisions on output gaps estimates.
JEL Codes: C11, C32, E24, E32.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.
No 2
2016-09-23
Aging, informality and public policies in a small open economy
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Abstract
We extend OGRE, the overlapping generation model developed by Baksa and Munkacsi (2016) by adding openness. We then employ the model to explore how the macroeconomic effects of aging, assumed to manifest itself as a decrease in the mortality rate, can be counteracted through public policies. The extended version inherits the previous modelling features of OGRE allowing us to also account for the impact openness has on the effectiveness of the considered policies.
JEL Codes: E24, E26, F41, H55, J11, J46.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.