A Thick ANN Model for Forecasting Inflation
Muhammad Hanif (),
Khurrum Mughal () and
Javed Iqbal ()
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Khurrum Mughal: State Bank of Pakistan
No 99, SBP Working Paper Series from State Bank of Pakistan, Research Department
Inflation forecasting is an essential activity at central banks to formulate forward looking monetary policy stance. Like in other fields, machine learning is finding its way to forecasting; inflation forecasting is not any exception. In machine learning, most popular tool for forecasting is artificial neural network (ANN). Researchers have used different performance measures (including RMSE) to optimize set of characteristics - architecture, training algorithm and activation function - of an ANN model. However, any chosen ‘optimal’ set may not remain reliable on realization of new data. We suggest use of ‘mode’ or most appearing set from a simulation based distribution of optimum ‘set of characteristics of ANN model’; selected from a large number of different sets. Here again, we may have a different trained network in case we re-run this ‘modal’ optimal set since initial weights in training process are assigned randomly. To overcome this issue, we suggest use of ‘thickness’ to produce stable and reliable forecasts using modal optimal set. Using January 1958 to December 2017 year on year (YoY) inflation data of Pakistan, we found that our YoY inflation forecasts (based on aforementioned multistage forecasting scheme) outperform those from a number of inflation forecasting models of Pakistan economy.
Keywords: Artificial Neural Networks; Inflation Forecasting (search for similar items in EconPapers)
JEL-codes: C45 E31 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-mac and nep-mon
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