Short term Bayesian inflation forecasting for Tunisia

Dahem, Ahlem (2015): Short term Bayesian inflation forecasting for Tunisia. Published in: ECOFORUM JOURNAL , Vol. 5, No. 1 (8) (2016)

Abstract

In order to explain clearly inflation forecasting and the dynamic of Tunisian prices, this paper uses two econometric approaches, the Standard VAR and Bayesian VAR (BVAR), to assess three models for predicting inflation, the mark-up model, the monetary model and Phillips curve over the period 1990 Q1 – 2013 Q4. In order to compare predictions, an out-of-sample estimation was conducted. We used the structural break test of Bai & Perron (1998, 2003) and the RMSE criterion for both inflation indices: CPI and PPI. We found that the Bayesian VECM mark-up model is best suited to forecast inflation for Tunisia. Our conclusions corroborate the literature of Bayesian VAR forecasting. Our findings indicate that the models which incorporate more economic information outperform the benchmark autoregressive models (AR (1) and AR (2)). The results reveal that forecasting with the BVECM markup model leads to a reduction in forecasting error compared to the other models. The results of the study are relevant to decision-makers to predict inflation in the short- and long-terms in Tunisia and may help them adopt the appropriate strategies to contain inflation.

• Item Type: MPRA Paper
• Language: English
• Keywords: Bayesian VAR - Bayesian VECM - Inflation forecasting - Mark-up Model - Monetary Model - Phillips Curve
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; 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 > E31 - Price Level ; Inflation ; Deflation; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

References

Adjemian, S., and Pelgriny, F., (2008). Un regard Bayésien sur les Modèles Dynamiques de la Macroéconomie. Economie et prévision, n° 2008/2, pp. 127-152.

Alexova, M., (2012). Inflation drivers in new EU members. National Bank of Slovakia, Working paper NBS, n° 6/2012.

Atkeson, A., and Ohanian, Lee E., (2001). Are Phillips Curves Useful for Forecasting Inflation? Federal Reserve Bank of Minneapolis, Quarterly Review, vol. 25(1), pp. 2–11.

Bai, J., and Perron, P., (1998). Estimating and testing linear models with multiple structural changes. Econometrica, vol. 66, pp. 47–78.

Banbura, M., Giannone, D., and Reichlin, L., (2008). Large Bayesian VARs. ECB Working Paper, n° 966.

Boschi, M., and Girardi, A., (2007). Euro Area inflation: long run determinants and short-run dynamics. Applied Financial Economics, Taylor and Francis Journals, vol. 17(1), p. 9-24.

Carbenciov, I., et al., (2008). A Small Quarterly Projection Model of the US Economy. International Monetary Fund Working Paper, WP/08/278.

Dahem, A., Saidane D., and Siala Guermazi F., (2015). Drivers and forecasting inflation for Agreement Agadir countries. Journal of World Economic Research, vol. 3(6-1), pp. 33-38.

De Brouwer G., & Ellis L., (1998). Forward-Looking Behaviour and Credibility: Some Evidence and Implications For Policy. Reserve Bank of Australia, Research Discussion Paper, n° 9803.

Doan, T., Litterman, R., and Sims, C.A., (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, Vol. 3, pp.1–100.

Faisal, F., (2012). Forecasting Bangladesh’s Inflation Using Time Series ARIMA Models.World Review of Business Research, Vol.2, n° 3, pp. 100-117.

Feddy, L., (2012). Forecasting Inflation in Asian Economies.MPRA Paper 36781, 2012, University Library of Munich, Germany.

Fernandez et al., (2006). Estimating Macroeconomic Models: A Likelihood Approach. NBER Technical Working Paper, n° 321, February 2006. Fernandes-Villaverde, J., (2009). The econometrics of DSGE models. Penn Institute for Economic Research working paper, n° 09-008. Gamber, E. N., et al., (2013). Inflation Persistence: Revisited. Research Program on Forecasting, RPF Working Paper, n° 2013-002.

Gelfand, A.E., and Smith, A.F.M., (1990). Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association, Vol. 85, pp. 398–409.

Gelfand, A., and Dey, D., (1994). Bayesian model choice: Asymptotics and exact calculations. Journal of the Royal Statistical Society Series B, Vol. 56, pp. 501-514.

Geweke, J., and Whiteman, C., (2006). Bayesian Forecasting Chapters, Handbook of Economic Forecasting, Elsevier.

Geweke, J., (2005). Contemporary Bayesian econometrics and statistics. Wiley series in probability and statistics. Statistical Papers, October 2008, Vol. 49, Issue 4, pp. 801-802

Hammersly, J.M., and Handscomb, D.C., (1964). Monte Carlo Methods. Methuen and Company, London.

Jaromír, B., et al., (2014). Changes in inflation dynamics under inflation targeting? Evidence from Central European countries. Economic Modelling, Vol. 44, pp. 116–130.

Jochmann, M., Koop, G., and Strachan, R.W., (2010). Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks. International Journal of Forecasting, Vol. 26, pp. 326–347.

Kadiyala, K., and Karlsson, S., (1997). Numerical methods for estimation and inference in Bayesian VAR-models. Journal of Applied Economics, Vol. 12, pp. 99–132.

Korobilis, D., (2009b). VAR forecasting using Bayesian variable selection. Manuscript.

Koop, G., (2003). Bayesian Econometrics. John Wiley and Sons Ltd.

Koop, G., Poirier, D., and Tobias, J., (2007). Bayesian Econometric Methods. Cambridge University Press.

Koop, G., and Potter, S., (2004). Forecasting in dynamic factor models using Bayesian model averaging. The Econometrics Journal, Vol. 7, pp. 550-565.

Koop, G., and Korobilis, D., (2010). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. MPRA Paper No. 20125, University Library of Munich, Germany.

Kosta, J., et al., (2014). Macroeconomic policy responses to financial crises in emerging European economies. Economic Modelling, Vol. 36, pp. 577-591

Lajmi, M., and El Khadraoui, S., (2014). Medium-Term Forecasting Model for Tunisia. Central Bank of Tunisia, July 2014.

Litterman, R., (1980). A Bayesian procedure for forecasting with vector autoregression. Massachusetts Institute of Technology, Department of Economics Working Paper.

Litterman, R., (1986). Forecasting with Bayesian vector autoregressions–five years of experience. Journal of Business and Economic Statistics, Vol. 4, pp. 25–38.

Lubik, and Schorfheide, (2005). A Bayesian Look at New Open Economy Macroeconomics. Economics Working Paper Archive521, The Johns Hopkins University, Department of Economics.

Neal, R.M., (1998). Philosophy of Bayesian Inference. Online at : http://www.cs.toronto.edu/~radford/res-bayes-ex.html Odunc, F K., et al., (2013). Short term inflation forecasting models for Turkey and a forecast combination analysis. Economic Modelling, Vol.33, pp. 312-325

Rangan, G., (2009). Bayesian methods of forecasting inventory investment. South African Journal of Economics, Vol.77, pp. 113–126.

Smets, F., and Wouters, R., (2003). An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area. Journal of the European Economic Association, Vol. 1(5), pp. 1123–1175.

Sims, C., and Zha, T., (1998). Bayesian methods for dynamic multivariate models. International Economic Review, Vol. 39, pp.949-968.

Spencer, D. E., (1993). Developing a Bayesian Vector Autoregression Model. International Journal of Forecasting, Vol. 9, pp. 407-421.

Todd, R. M., (1984). Improving Economic Forecasting with Bayesian Vector Autoregression. Quarterly Review Federal Reserve Bank of Minneapolis, pp. 18-29.

Wieland, et al., (2012). A New Comparative Approach to Macroeconomic Modeling. CFS working paper, n° 2012/03.

Yong, M., and Shushu, L., (2014). Bayesian estimation of China's monetary policy transparency: A New Keynesian approach. Economic Modelling, Vol. 45, pp. 236–248