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 yearahead 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.
Iqra Iqbal, Ahsan ul Haq Satti. (2020) Inflation Forecasting under Different Macroeconomic Conditions: A Case Study of Pakistan, Journal of Quantitative Methods, Volume 4, Issue 2.
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