Inflation Forecasting Under Different Macroeconomic Conditions: A Case Study of Pakistan

  • Iqra Iqbal
  • Ahsan Satti Pakistan Institute of Development Economics (PIDE), Islamabad, Pakistan
Keywords: Inflation forecasting, Macroeconomic conditions, Naive model, ARIMA model, Philips Curve Model


Inflation forecasting has been important task for monetary authorities, policy makers and government. Prediction about inflation confer us a precise image of how the economy is expected to accomplish in the future. It is essential job for researchers to examine which methods are suitable for inflation forecasting. We have used Naive model, ARIMA model, Philips curve model and Philips Curve (TAR) under different macroeconomic conditions with reference to real-time, revised and final data from 1974 to 2014 and predicted out-of-sample inflation forecast for 2015, afterward we roll-forward our regression from 1975 to 2015 to forecast inflation for 2016.We have analyzed naive model is superior to other modelsbecause RMSE and MAE of naive model are less than other models by using real-time, revised and final data for one year-ahead out-of-sample inflation forecasting. On the other hand for two years ahead out of sample inflation forecast, according to real-time data RMSE shows that naive model is most superior to other models whereas MAE shows that Philips curve Threshold auto regressive model is most superior to other models. According to revised and final data for two years ahead out of sample inflation forecasting both forecasting accuracy measures shows Naive model is most superior to other models.


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Afzal, M., Rehman, H. U., & Butt, A. R. (2002). FORECASTING: A Dilemma of Modules. Pakistan Economic and Social Review, 40(1), 1-18.

Alles, L., & Horton, D. (1999). An Evaluation of Alternative Methods of Forecasting Australian Inflation. Australian Economic Review, 32(3), 237-248.

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.

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.

Atkeson, A. E. O. (2001). Are Phillips Curves Useful for Forecasting Inflation? [*]. Federal Reserve bank of Minneapolis quarterly review, 25(1), 2-2.

Bokharı, S. M., & Feridun, M. (2006). Forecasting inflation through econometric models: an empirical study on Pakistani data.

Bokil, M., & Schimmelpfennig, A. (2005). Three attempts at inflation forecasting in Pakistan (Vol.5). International Monetary Fund.

Croushore, D., & Stark, T. (2003). A real-time data set for macroeconomists: Does the data vintage matter?. Review of Economics and Statistics, 85(3), 605-617.

Dotsey, M., & Stark, T. (2005). The relationship between capacity utilization and inflation. Federal Reserve Bank of Philadelphia Business Review, 8-17.

Dotsey, M., Fujita, S., & Stark, T. (2017). Do Phillips Curves Conditionally Help to Forecast Inflation?.

Fisher, J. D., Te Liu, C., & Zhou, R. (2002). When can we forecast inflation?. Economic Perspectives, 26(1), 32-45.

Fuhrer, J. C., & Olivei, G. (2010). The role of expectations and output in the inflation process: an empirical assessment. FRB of Boston Public Policy Brief, (10-2).

Hafer, R. W., & Hein, S. E. (1990). Forecasting inflation using interest-rate and time-series models: Some international evidence. Journal of Business, 1-17.

Haider, A., & Hanif, M. N. (2009). Inflation forecasting in Pakistan using artificial neural networks. Pakistan economic and social review, 123-138

Hanif, M. N., & Malik, M. J. (2015). Evaluating Performance of Inflation Forecasting Models of Pakistan.

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. In Economic Society of South Africa 2013 Conference.

Khan, M. M. S., & Schimmelpfennig, M. A. (2006). Inflation in Pakistan: Money or wheat? (No. 6-60). International Monetary Fund.

McKenzie, R., & Gamba, M. (2008). Data and metadata requirements for building a real-time database to perform revisions analysis.

McKenzie, R., & Gamba, M. (2008). Interpreting the results of Revision Analyses: Recommended Summary Statistics. Contribution to OECD/Eurostat Task Force on “Performing Revisions Analysis for Sub-Annual Economic Statistics.

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.

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, 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.

Stock, J. H., & Watson, M. W. (2007). Why has US inflation become harder to forecast? Journal of Money, Credit and banking, 39, 3-33.

Stock, J. H., & Watson, M. W. (2008). Phillips curve inflation forecasts (No. w14322). National Bureau of Economic Research.

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.

Swanson, N. (1996). Forecasting using first-available versus fully revised economic time-series data. Studies in Nonlinear Dynamics & Econometrics, 1(1).

Yellen, J. L. (2015). Inflation dynamics and monetary policy. The Philip Gamble Memorial Lecture, University of Massachusetts, Amherst, MA, September, 24.

Zardi, S. C. (2017). Forecasting inflation in a macroeconomic framework: An application to Tunisia (No. 07-2017). Graduate Institute of International and Development Studies Working Paper.

How to Cite
Iqbal, I., & Satti, A. (2023). Inflation Forecasting Under Different Macroeconomic Conditions: A Case Study of Pakistan. Journal of Quantitative Methods, 7(1). Retrieved from