Abstract
This paper builds a dynamic stochastic general equilibrium (DSGE) model for Latvia that would be suitable for policy analysis and forecasting purposes at Bank of Latvia. For that purpose, I adapt the DSGE model with financial frictions of Christiano et al. (J Econ Dyn Control 35:1999–2041, 2011. doi:10.1016/j.jedc.2011.09.005) to Latvia’s data, estimate it, and study whether adding the financial frictions block to an otherwise identical (‘baseline’) model is an improvement with respect to several dimensions. The main findings are: (1) The addition of the financial frictions block provides more appealing interpretation for the drivers of economic activity and allows to reinterpret their role; (2) financial frictions played an important part in Latvia’s 2008-recession; (3) the financial frictions model beats both the baseline model and the random walk model in forecasting both CPI inflation and GDP.
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Notes
A generalized Taylor rule, including foreign interest rate and nominal exchange rate, was also studied, but the results are skipped due to the space constraint. In short, the currency union fits the data better.
These nominal contracts give rise to wealth effects of unexpected changes in the price level, as emphasized by Fisher (1933). For example, when a shock occurs which drives the price level down, households receive a wealth transfer. This transfer is taken from entrepreneurs whose net worth is therefore reduced. With tightening of their balance sheets, the ability of entrepreneurs to invest is reduced, and this generates an economic slowdown.
Namely, the equilibrium debt contract maximizes the expected entrepreneurial welfare, subject to the zero profit condition on banks and the specified return on household bank liabilities.
The import share in export might appear to be too high when consulting to the literature of international trade in value added. For example, the results of Stehrer (2013) suggest, from the value-added perspective, that share about 30 %. However, re-exports (with little or no value added) is an important phenomenon in Latvia’s foreign trade and is the reason why the calibrated parameter is higher than the aforementioned 30 %.
Source: http://epp.eurostat.ec.europa.eu/cache/ITY_PUBLIC/2-29042013-CP/EN/2-29042013-CP-EN.PDF , accessed in September 6, 2013.
The fraction of time spent working calibrated to 0.27 is somewhat arbitrary but checked against the marginal data density with respect to its neighboring values.
The net worth to assets ratio for Latvia, if the definition of CTW is taken, yields about 0.15. However, the marginal data density favors a much larger number, 0.6, which is used in the final calibration. The latter number might be rationalized if the net worth was measured not only by the share price index but if it included also the real estate value.
My unreported results show that this is true regardless of the sample span used in the estimation and whether or not the foreign block is estimated separately from the domestic block. Also, the use of foreign CPI inflation instead of the foreign GDP deflator’s inflation (which is used by CTW) improves the identification of the foreign monetary policy only marginally. Therefore, the results involving the foreign monetary policy should be interpreted with caution. The replacement of the foreign SVAR with a full-fledged foreign DSGE block thus might be an improvement but is not considered in this paper.
Christiano et al. (2014) find (the anticipated part of) this shock important when loans are observable. My unpublished results show that fitting loans may require excessive amount of risk.
CTW note that their use of ‘endogenous prior’ reduces the effect of over-estimated shock standard deviations. I’m not using such a prior.
As a reminder, MEI shock enters in the capital accumulation equation and affects how (efficiently) investment is transformed into capital. This is the shock whose importance is emphasized in Justiniano et al. (2011), where one of their interpretations of this shock being a proxy for the effectiveness with which the financial sector channels the flow of the household savings into a new productive capital.
I have checked this claim by recalibrating the model.
My unpublished results show that when data measurement errors are estimated, the imports for exports markup shock is almost eliminated. The true source of this shock is yet to be determined.
The labor preference shock reflects the supply side in the labor market, and thus, it can be affected by ‘long-run’ factors such as demographics and labor force participation rate. Indeed, the latter increased during the boom years in 2006–2007 and decreased during the recession in 2009–2010. Therefore, the participation rate has dampened the cyclical fluctuations of the labor preference shock. See Christiano et al. (2010) for a model with endogenous participation rate.
The particular SVAR has some economically implausible estimated parameters, since Latvian GDP, CPI inflation, and nominal interest rate data do not possess a stable and economically plausible relationship over the particular sample span.
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Acknowledgments
I thank Viktors Ajevskis, Rudolfs Bems, Konstantins Benkovskis, Martins Bitans, Dmitry Kulikov, Karl Walentin, and two anonymous referees for feedback. I also thank Andrejs Kurbatskis and several other colleagues at Bank of Latvia for helping with the data. All remaining errors are my own. I have benefited from the program code provided by Lawrence Christiano, Mathias Trabandt, and Karl Walentin for their model.
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Buss, G. Financial frictions in Latvia. Empir Econ 51, 547–575 (2016). https://doi.org/10.1007/s00181-015-1014-z
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DOI: https://doi.org/10.1007/s00181-015-1014-z