Inflation Forecasting under Different Macroeconomic Conditions: A Case Study of Pakistan
Abstract
Inflation forecasting is of primary importance not only for the conduct of monetary policy, but also for individuals to make choices. Forecasting inflation provides the precise image of how the economy is expected to accomplish in the future. For forecasting inflation, personal consumption expenditure is used to measure inflation because of its superiority of less sensitivity of price shock and its revision in subsequent years. For inflation forecasting, naive model, ARIMA model, Philips curve model, and Philips curve threshold autoregressive model are applied under different macroeconomic conditions with real-time, revised and final data from 1974 to 2016. The result shows that the naive model is superior to other models because RMSE and MAE of naive model are smaller than other models by using real-time, revised and final data for one year-ahead out-of-sample inflation forecasting. However, for two years ahead out of the sample inflation forecast, the real-time data RMSE shows that the naive model outperforms the other models, whereas the MAE shows that Philips curve threshold autoregressive model is superior than other models. For revised and final data for two years ahead out-of-sample inflation forecasting both forecasting accuracy measures show the naive model performance is the best.
JEL Classification Codes: C53
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References
Afzal, M., Rehman, H. U., & Butt, A. R. (2002). Forecasting: A dilemma of modules (A comparison of theory based and theory free approaches). Pakistan Economic and Social Review, 40(1), 1–18. https://www.jstor.org/stable/25825233.
Alles, L., & Horton, D. (1999). An evaluation of alternative methods of forecasting Australian inflation. Australian Economic Review, 32(3), 237–248. https://doi.org/10.1111/1467-8462.00111.
Ang, A., Bekaert, G., & Wei, M. (2007). Do macro variables, asset markets, or surveys forecast inflation better?. Journal of Monetary Economics, 54(4), 1163–1212. https://doi.org/10.1016/j.jmoneco.2006.04.006.
Asghar, N., Awan, A., & Rehman, H. (2012). Human capital and economic growth in Pakistan: A cointegration and causality analysis. International Journal of Economics and Finance, 4(4), 135–147. https://doi.org/10.5539/ijef.v4n4p135.
Atkenson, A., & Ohanian, L. E. (2001). Are Phillips curves useful for forecasting inflation? Federal Reserve Bank of Minneapolis Quarterly Review, 25(1), 2-11. https://doi.org/10.21034/qr.2511.
Bokhari, S. M., & Feridun, M. (2006). Forecasting inflation through econometric models: An empirical study on Pakistani data. Doğuş Üniversitesi Dergisi, 7(1), 39–47. https://doi.org/10.31671/dogus.2019.260.
Bokil, M., & Schimmelpfennig, A. (2005). Three attempts at inflation forecasting in Pakistan (Vol.5). Washington, D.C., United States: International Monetary Fund.
Croushore, D., & Stark, T. (2003). A real-time data set for macroeconomists: Does the data vintage matter? The Review of Economics and Statistics, 85(3), 605–617. https://doi.org/10.1162/003465303322369759.
Dotsey, M., & Stark, T. (2005). The relationship between capacity utilization and inflation. Federal Reserve Bank of Philadelphia Business Review, 2, 8–17.
Dotsey, M., Fujita, S., & Stark, T. (2018). Do Phillips curves conditionally help to forecast inflation? International Journal of Central Banking, September, 14(4), 43-92.
Fisher, J. D., Te Liu, C., & Zhou, R. (2002). When can we forecast inflation? Economic Perspectives, 26(1), 32–44.
Fuhrer, J. C., & Olivei, G. (2010). The role of expectations and output in the inflation process: An empirical assessment (Public Policy Brief). Federal Reserve Bank of Boston. Retrieved from https://ideas.repec.org/a/fip/fedbpb/y2010n10-2.html.
Hafer, R. W., & Hein, S. E. (1990). Forecasting inflation using interest-rate and time-series models: Some international evidence. Journal of Business, 63(1), 1–17.
Haider, A., & Hanif, M. N. (2009). Inflation forecasting in Pakistan using artificial neural networks. Pakistan Economic and Social Review, 47(1), 123–138.
Hanif, M. N., & Malik, M. J. (2015). Evaluating performance of inflation forecasting models of Pakistan. SBP Research Bulletin, 11(1), 1–37.
Jahan, S., & Mahmud, A. S. (2013). What is the output gap. Finance & Development, 50(3), 38–39.
Kanyama, I. K., & Thobejane, B. M. (2013). Forecasting macroeconomic variables in South Africa: Parametric vs. non-parametric methods. Department of Economics and Econometrics, University of Johannesburg. http://www.essa2013.org.za/fullpaper/essa2013_2716.pdf
Khan, M. M. S., & Schimmelpfennig, M. A. (2006). Inflation in Pakistan: Money or wheat? (Working Paper No. 06/60). International Monetary Fund.
Önder, A. Ö. (2004). Forecasting inflation in emerging markets by using the Phillips curve and alternative time series models. Emerging Markets Finance and Trade, 40(2), 71–82. https://doi.org/10.1080/1540496X.2004.11052566.
Orphanides, A., & Van Norden, S. (2005). The reliability of inflation forecasts based on output gap estimates in real time. Journal of Money, Credit and Banking, 37(3), 583–601.
Rees, A. (1970). The Phillips curve as a menu for policy choice. Economica, 37(147), 227–238.
Stock, J. H., & Watson, M. W. (1999). Forecasting inflation. Journal of Monetary Economics, 44(2), 293–335. https://doi.org/10.1016/S0304-3932(99)00027-6.
Stock, J. H., & Watson, M. W. (2007). Why has US inflation become harder to forecast? Journal of Money, Credit and Banking, 39, 3–33. https://doi.org/10.1111/j.1538-4616.2007.00014.x.
Stock, J. H., & Watson, M. W. (2008). Phillips curve inflation forecasts (NBER Working Paper No. 14322). National Bureau of Economic Research. https://doi.org/10.3386/w14322.
Sultana, K., Rahim, A., Moin, N., Aman, S., & Ghauri, S. P. (2013). Forecasting inflation and economic growth of Pakistan by using two time series methods. International Journal of Business and Economics Research, 2(6), 174–178. https://doi.org/10.11648/j.ijber.20130206.17.
Swanson, N. (1996). Forecasting using first-available versus fully revised economic time-series data. Studies in Nonlinear Dynamics & Econometrics, 1(1), 1–20.
Yellen, J. L. (2015, September 24). Inflation dynamics and monetary policy [Speech]. Amherst, Massachusetts, MA: University of Massachusetts. https://doi.org/10.2202/1558-3708.1012.
Zardi, S. C. (2017). Forecasting inflation in a macroeconomic framework: An application to Tunisia (Working Paper No. 07-2017). Graduate Institute of International and Development Studies Working Paper.
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