STOCHASTIC PREDICTION OF MONTHLY INFLATION RATES THROUGH KALMAN FILTERING
Inflation measure is an important indicator of the state of an economy and the desire to determine it ahead of “time” cannot be overemphasised. This paper presents a step-by-step algorithm to predict the would-be monthly inflation rate of the Nigerian economy, using Kalman Filtering Predictor (KFP). The ordinary structural model for a time series (structTS) is highlighted to “fairly” compete against our proposed KFP. The structTS is a powerful “competitor”, it is in recommended R package “stats” and used for fitting basic structural models to “univariate” time series. It is quite reliable and fast, and is used as a benchmark in some comparisons of filtering techniques, it is indeed the “predictor” to “beat”, yet our proposed KFP has more to “offer”. The pertinent statistics and pictorial representation of the results obtained, through both techniques, is highlighted for any “incorruptible” judge’s perusal. All of these are contained in the couple of illustrative examples that exhibit the steps involved in the proposed algorithm, using a hypothetical monthly inflation rate and the monthly inflation rates data (January, 2011 to June, 2014) of the Nigerian economy.
Atkeson, A., Ohanian, L. 2001. Are Phillips Curves Useful for Forecasting Inflation? Federal Reserve Bank of Minneapolis Quarterly Review 25: 2-11.
Awogbemi, C A., Ajao, O. 2011. Modelling Volatility in Financial Time Series: Evidence from Nigeria Inflation Rates. Ozean Journal of Applied Sciences 43
Bocquet, M. 2011. Ensemble Kalman filtering without the intrinsic need for inflation. Nonlinear Processes in Geophysics 18: 735–750.
Bocquet, M., Sakov, P. 2012. Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems. Nonlinear Processes in Geophysics 19: 383–399
Central Bank of Nigeria (CBN). 2010..Statistical Bulletin, 50 years Special Anniversary Edition, Abuja.
Choudhry, T. 1996. The Fisher Effect and the Gold Standard: evidence from the USA. Applied Economic letters 3: 553-555
Engle, R F. 1982. Autoregressive Conditional Heteroscedasticity with Estimates of the variance of united Kingdom Inflation. Econometrica 50(4): 987-1008.
Faragher, R. 2012. Understanding the basics of the Kalman Filter via a Simple and Intuitive Derivation. IEEE Signal Processing Magazine 130pp.
Grewal, M. S., Andrews, A P. 2010. Applications of Kalman Filters in Aerospace 1960 to the Present. Historical Perspective. IEEE Control Systems Magazine, University of Michigan Library.
Groen, J, Paap, R., Ravazzolo, F. 2010. Real-time Inflation Forecasting in a Changing World. Federal Reserve Bank of New York, Statistical Report Number 388.
Harvey, A C. 1989.. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge.
Harvey, A. C., Pierse, R G. 2012. Estimating missing observation in Economic Time Series. Journal of the American Statistical Association: 128. http://amstat.tandfonline.com/loi/uasa20
Hunt, B. R., Kostelich, E. J., Szunyogh, I. 2007. Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D 230, 112–126.
Kalman, R E. 1960. A new approach to linear filtering and prediction problems. Transactions of the ASME, Journal of Basic Engineering. 82: 34–45.
Maku, A. O., Adelowokan, O A. 2013. Dynamics of Inflation in Nigeria: An Autoregressive Approach; European Journal of Humanities and Social Sciences 22(1): 1175-1184.
Shah, M. A. A., Arshed, N. Jamal, F. 2014. Statistical Analysis of the factors affecting Inflation in Pakistan. International Journal of Research (IJR) 1(4). ISSN 2348-6848.
Stock, J., Watson, M. 1999. Forecasting Inflation. Journal of Monetary Economics 44: 293-335.
Stock, J., Watson, M. 2008. Phillips Curve Inflation Forecasts, NBER Working Paper No. 14322.