Learning from Errors While Forecasting Inflation: A Case for Intercept Correction

Year 2019, , 24 - 38, 05.04.2019

Muhammad Nadim Hanif , Jahanzeb Malik

https://doi.org/10.33818/ier.304468

Abstract

Structural changes are quite common in macroeconomic time
series. Internal/external shock(s) may cause significant structural change in any
economy. Simplest form of such change is observed as shift in constant of an
underlying relationship between a pair of macroeconomic variables. Forecasting
from such a model assuming no structural break is tantamount to ignoring the important
aspects of underlying economy and mostly results in forecast failure. Intercept
correction (adjustment for the realized forecast error) is a method for
accommodating such ignored structural break(s). We use a simple model to forecast
inflation (based upon single lag of money supply growth) for 25 countries and
compare its performance with a) the same model with optimal intercept
correction, b) the same model with half intercept correction, and c) a random
walk model (with drift). Optimal intercept correction approach outperforms in
forecasting next period inflation compared to one from a) the same model without
intercept correction, b) the same model with half intercept correction, and c) random
walk model.  We also observe that higher
correction is needed for countries with more volatile inflation.  

Keywords

inflation, forecasting, intercept correction

References

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