Skip to main content
SpringerLink
Log in

Do money and financial variables help forecasting output in emerging European Economies?

  • Published:
Empirical Economics Aims and scope Submit manuscript

Abstract

Whether including monetary aggregates and different financial variables into small scale BVAR models improves the accuracy of output forecasts is tested for three emerging European economies. Various specifications for the priors of the BVAR models are used. The results are found to vary with respect to prior specification, variables, as well as prediction horizon. The evidence is stronger when the forecasting accuracy is compared based on log predictive likelihood but weaker when the RMSEs are used. These results may constitute evidence against dismissing the monetary aggregates or financial variables as completely irrelevant.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Banbura M, Giannone D, Reichlin L (2010) Large Bayesian vector auto regressions. J Appl Econom 25(1):71–92

    Article  Google Scholar 

  • Berger H, Österholm P (2009) Does money still matter for US output? Econ Lett 102:143–146

    Article  Google Scholar 

  • Canova F, Menz T (2011) Does money matter in shaping domestic business cycles. An international investigation. J Money Credit Banking 43(4):577–607

    Article  Google Scholar 

  • Canzoneri M, Cumby R, Diba B, Lopez-Salido D (2008) Monetary aggregates and liquidity in a Neo-Wicksellian framework. J Money Credit Banking 40(8):1667–1698

    Article  Google Scholar 

  • Castelnouvo E (2012) Estimating the evolution of money’s role in the US monetary business cycle. J Money Credit Banking 44: 23–52

    Google Scholar 

  • Christiano LJ, Ljungqvist L (1988) Money does Granger-cause output in the bivariate money-output relation. J Monet Econ 22:217–235

    Article  Google Scholar 

  • D’Agostino A, Surico P (2009) Does global liquidity help to forecast US inflation? J Money Credit Banking 41(2–3):479–489

    Article  Google Scholar 

  • De Mol C, Giannone D, Reichlin L (2008) Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components? J Econom 146:318–328

    Article  Google Scholar 

  • Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13:253–265

    Google Scholar 

  • Doan Th, Litterman R, Sims Ch (1984) Forecasting and conditional projection using realistic prior distributions. Econom Rev 3(1):1–100

    Article  Google Scholar 

  • Espinoza R, Fornari F, Lombardi MJ (2012) The role of financial variables in predicting economic activity. J Forecast 31:15–46

    Article  Google Scholar 

  • Favara G, Giordani P (2009) Reconsidering the role of money for output, prices and interest rates. J Monet Econ 56:419–430

    Article  Google Scholar 

  • George E, Sun D, Ni S (2008) Bayesian stochastic search for VAR model restrictions. J Econom 142:553–580

    Article  Google Scholar 

  • Geweke J, Amisano G (2010) Comparing and evaluating Bayesian predictive distributions of asset returns. Int J Forecast 26(2):216–230

    Google Scholar 

  • Koop G, Korobilis D (2010) Bayesian multivariate time series methods for empirical macroeconomics. Found Trends Macroecon 3(4):267–358

    Article  Google Scholar 

  • Korobilis D (2013) VAR forecasting using Bayesian variable selection. J Appl Econom 28: 204–230

    Google Scholar 

  • Litterman RB (1981) A Bayesian procedure for forecasting with vector autoregressions. Working Paper, Federal Reserve Bank of Minneapolis

  • Litterman R (1986) Forecasting with Bayesian vector autoregressions: five years of experience. J Bus Econ Stat 4(1):25–38

    Google Scholar 

  • Panopoulou E (2007) Predictive financial models of the euro area: a new evaluation test. Int J Forecast 23(4):695–705

    Article  Google Scholar 

  • Rapach DE, Weber CE (1985) Financial variables and the simulated out-of-sample forecastability of US output growth since 1985: an encompassing approach. Econ Inq 42(4):717–738

    Article  Google Scholar 

  • Ravn MO, Psaradakis Z, Sola M (2005) Markov switching causality and the money–output relationship. J Appl Econom 20:665–683

    Article  Google Scholar 

  • Sims CA (1972) Money, income, and causality. Am Econ Rev 62:540–552

    Google Scholar 

  • Sims CA (1980) Macroeconomics and reality. Econometrica 48:1–48

    Article  Google Scholar 

  • Sims CA, Stock JH, Watson MW (1990) Inference in linear time series models with some unit roots. Econometrica 58:113–144

    Article  Google Scholar 

  • Stock JH, Watson MW (1989) Interpreting the evidence on money-income causality. J Econom 40:161–181

    Article  Google Scholar 

  • Stock JH, Watson MW (2003) Forecasting output and inflation: the role of asset prices. J Econ Lit 41:788–829

    Article  Google Scholar 

  • Tomsik V, Viktorova D (2006) The relationship between money and output in the Czech Republic. Evidence from VAR analysis. East Eur Econ 44(2):23–39

    Article  Google Scholar 

  • Woodford M (2003) Interest and prices: foundation of a theory of monetary policy. Princeton University Press, Princeton, NJ

    Google Scholar 

Download references

Acknowledgments

This study was supported by CNCSIS—UEFISCSU, Project Number PNII—RU 25/5.08.2010.

Author information

Authors and Affiliations

  1. Institute for Economic Forecasting, Romanian Academy, Bucharest, Romania

    Petre Caraiani

Authors
  1. Petre Caraiani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Petre Caraiani.

Appendices

Appendix A: RMSEs for Koop and Korobilis (2010) approach

See Tables 17, 18, 19, 20, 21, and 22.

Table 17 RMSE for Czech Republic: 1 step ahead predictions
Full size table
Table 18 RMSE for Czech Republic: 4 step ahead predictions
Full size table
Table 19 RMSE for Hungary: 1 step ahead predictions
Full size table
Table 20 RMSE for Hungary: 4 step ahead predictions
Full size table
Table 21 RMSE for Poland: 1 step ahead predictions
Full size table
Table 22 RMSE for Poland: 4 step ahead predictions
Full size table

Appendix B: RMSEs for Banbura et al. (2010) approach

See Tables 23, 24, 25, and 26.

Table 23 RMSE for HP filtered data and 1 step ahead predictions
Full size table
Table 24 RMSE for HP filtered data and 4 step ahead predictions
Full size table
Table 25 RMSE for unfiltered data and 1 step ahead predictions
Full size table
Table 26 RMSE for unfiltered data and 4 step ahead predictions
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Caraiani, P. Do money and financial variables help forecasting output in emerging European Economies?. Empir Econ 46, 743–763 (2014). https://doi.org/10.1007/s00181-013-0686-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00181-013-0686-5

Keywords

JEL Classification

Search

Navigation