Does a financial accelerator improve forecasts during financial crises?: Evidence from Japan with Prediction Pool Methods

Hasumi, Ryo and Iiboshi, Hirokuni and Matsumae, Tatsuyoshi and Nakamura, Daisuke (2018): Does a financial accelerator improve forecasts during financial crises?: Evidence from Japan with Prediction Pool Methods.

Abstract

Using a Markov-switching prediction pool method (Waggoner and Zha, 2012) in terms of density forecasts, we assess the time-varying forecasting performance of a DSGE model incorporating a financial accelerator a la Bernanke et al. (1999) with the frictionless model by focusing on periods of financial crisis including the so-called "Bubble period" and the "Lost decade" in Japan. According to our empirical results, the accelerator improves the forecasting of investment over the whole sample period, while forecasts of consumption and inflation depend on the fluctuation of an extra financial premium between the policy interest rate and corporate loan rates. In particular, several drastic monetary policy changes might disrupt the forecasting performance of the model with the accelerator. A robust check with a dynamic pool method (Del Negro et al., 2016) also supports these results.

• Item Type: MPRA Paper
• Language: English
• Keywords: Density forecast, Optimal prediction pool, Markov-switching prediction pool, Dynamic prediction pool, Bayesian estimation, Markov Chain Monte Carlo, Financial Friction.
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

References

[1] Adolfson, M., Linde, J., & Villani, M., (2007) "Forecasting Performance of an open economy DSGE model," Econometric Reviews, 26(2-4), 289-328.

[2] Altig, D., Christiano, L. J., Eichenbaum, & M., Linde, J. (2011) "Firm-specific capital, nominal rigidities and the business cycle ," Review of Economic Dynamics, 14(2), 225-247.

[3] Albert, J.H. & Chib, S. (1993) Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts. Journal of Business and Economic Statistics, 11 (1), 1-15.

[4] Amisano, G., & Giacomini, R. (2007) "Comparing density forecasts via weighted likelihood ratio test," Journal of Business and Economic Statistics, 25, 177-190.

[5] Andrieu, C., Doucet, A., & Holenstein, R. (2010) "Particle Markov Chain Monte Carlo Methods," Journal of the Royal Statistical Society Series B, 72, 269-342.

[6] Barnanke, B.S., Gertler, M., & Gilchrist, S., (1999) "The Financial Accelerator in Quantitative Business Cycle Framework," In: Taylor, J.B., Woodford, M. (Eds), Handbook of Macroeconomics, Vol 1C, 1341-1393.

[7] Bates, J. & Granger, C. W. (1969) "The Combination of Forecasts," Operations Reserach, 20, 451-468.

[8] Christensen, I., & Ali Dib (2008) The financial accelerator in an estimated New Keyesian model, Review of Economic Dynamics, 11, 155-178.

[9] Christiano, L.J., Motto, R., & Rostagno, M.V., (2014) "Risk Shocks," American Economic Review, 104(1), 27-65.

[10] Christiano, L.J., Tranbandt, M., & Walentin, K. (2010) "DSGE models for Monetary Policy Analysis," Handbook of Monetary Economics.

[11] Christiano, L.J., Tranbandt, M., & Walentin, K. (2011) "Introducing Financial Frictions and Unemployment into a Small Open Economy Model," Journal of Economic and Dynamic Control, 35(12) 1999-2041.

[12] Del Negro, M., Hasegawa, R.B. & Schorfheide, F. (2016) Dynamic Prediction Pools: an Investigation of Financial Frictions and Forecasting Performance. Journal of Econometrics, Vol.192(2), p.391-405.

[13] Del Negro, M. & Schorfheide, F. (2004) "Priors from General Equilibrium Models for VARs, " International Economic Reviews, 45(2), 643-673.

[14] Del Negro, M. & Schorfheide, F. (2013) "DSGE model-based forecasting," In G.Elliott and A.Timmermann (Eds.) Handbook of Economic Forecasting, Vol.2, 57-140, Elsevier.

[15] Edge, Rochelle M. & Refet S. Gurkaynak (2010) How Useful are Estimated DSGE Model Forecasts? Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(2 (Fall)), pages 209-259.

[16] Edge, R. M., Kiley, M. T., & Laforte, J.-P. (2010) \A Comparison of Forecast Performance between Federal Reserve Staff Forecast, Simple Reduced-Form Models, and a DSGE Model, " Journal of Applied Econometrics, 25(4), 720-754.

[17] Elliott, G., & Timmermann, A., (2016) Economic Forecasting, Princeton University Press.

[18] Geweke J. (1992) Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments.In Bayesian Statistics 4, ed. J. M. Bernardo, J. Berger, A. P. Dawid, and A. F. M. Smith (Oxford: Oxford University Press) pp. 169-193.

[19] Geweke J. (2010) Complete and Incomplete Econometric Models. The Econometric and Tinbergen Institutes Lectures, Princeton University Press.

[20] Geweke, J. & Amisano, G. (2011) Optimal Prediction Pools. Journal of Econometrics 164, 130-141.

[21] Geweke, J. & Amisano, G. (2012) Prediction with Misspecified Models. American Economic Review: Papers & Proceedings 2012, 102 (3), 482-486.

[22] Gilchrist, S., & Zakrajsek, E., (2012) "Credit Spreads and Business Cycle Fluctuations," American Economic Review, 102(4), 1692-1720.

[23] Hayashi, F., & Prescott, E. C., (2002) "The 1990s in Japan: the Lost decade," Review of Economic Dynamics, 5(1), 206-235.

[24] Herbst, E. & Schorfheide, F. (2012) "Evaluating DSGE model forecast of Comovement," Journal of Econometrics, 171(2), 152-166.

[25] Hoeting, J., Madigan, D., Raftey, A. & Volinsky, C. (1999) Bayesian Model Averaging. Statistical Science 14, 382-401.

[26] Johanness, M. & Polson, N. (2009) Particle Filtering, Handbook of Financial Time Series, edited by T.G. Anderson et al. pp.1015-1029. Springer-Verlag Berlin Heidelberg.

[27] Kaihatsu, S., & Kurozumi, T., 2010. Sources of Business Fluctuations: Finanical or Technology Shocks? Bank of Japan Working Paper Series, No 10-E-12.

[28] Kaihatsu, S., & Kurozumi, T., 2014a. Sources of Business Fluctuations: Finanical or Technology Shocks? Review of Economic Dynamics, 17, 224-242.

[29] Kaihatsu, S., & Kurozumi, T., 2014b. What caused Japan's Great Stagnation in the 1990s? Evidence from an estimated DSGE model. Journal of Japanese and International Economies, 34, 217-235.

[30] Kolasa, M., & Rubaszek, M. 2015. Forecasting using DSGE models with financial frictions, International Journal of Forecasting, 31, 1-19.

[31] Kolasa, M., Rubaszek, M., & Skirzypczynski, P. 2012, Putting the New Keynesian DSGE model to real-time forecasting test, Journal of Money, Credit and Banking, 44(7), 1301-1324.

[32] Rafty, A.E., Madigan, D. & Hoeting, J.A. (1997) Bayesian Model Averaging for Linear Regression Models. Journal of the American Statistical Association 92, 179-191.

[33] Smet, F. & Wouters, R. (2007) "Shocks and Frictions in US Business Cycles: a Bayesian DSGE approach," American Economic Review, 97(3), 586-606.

[34] Sugo, T. & Ueda, K. (2008) "Estimating a Dynamic Stochastic General Equilibrium Model for Japan," Journal of Japanese and International Economies, 22, 476-502.

[35] Timmermann, A., (2006) "Forecast combination ," in: Elliott, G., Granger , C.W.J., Timmermann, A., (Eds.) Handbook of Economic Forecasting, chapter 4, 135-196.

[36] Waggoner, D.F. & Zha, T. (2012) Confronting Model Misspecification in Macroeconomics. Journal of Econometrics 171, 167-184.