Forecasting Inflation using Functional Time Series Analysis

Zafar, Raja Fawad and Qayyum, Abdul and Ghouri, Saghir Pervaiz (2015): Forecasting Inflation using Functional Time Series Analysis.

Abstract

In present study we model the data using Functional Time series Analysis (FTSA). The method is basically univariate, so to check its efficiency we compared it with seasonal ARIMA models. We have used data sets of monthly frequency from 2002-2011 to forecast Consumer Price Index (CPI) of Pakistan. We withhold some data of last year (i.e. of 2011) and based on remaining year (2002-2010) we fitted model and forecasted the values of monthly CPI. Our study compares the performance of FTSA model and ARIMA model using the test data of 2011. Comparison based on forecast evaluation criteria’s and forecasted value of 2011, indicates that FTSA model using CPI general data outperforms SARIMA models

• Item Type: MPRA Paper
• Language: English
• Keywords: Forecasting, Inflation, SARIMA, FTSA
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods

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