Final Report SCP
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The document discusses using an ARMA model to forecast inflation in Pakistan. It presents monthly CPI data from 1989-2013 to develop the model. The best fitting ARMA model included autoregressive and moving average terms and was used to project CPI values for the next year. The projections estimated CPI would increase modestly each month through June 2014, with percentage changes ranging from 0.3% to 1.0%. The model is best for short-term forecasts and should be re-run quarterly to revise projections.
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2017.03.09 collaboration is key to thriving in the 21st century
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ARMA and VAR modelling of Industrial Production in America
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2017.02.08 The Darkside of Enterprise Social Media
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Final Report SCP
- 1. INFLATION FORECASTING PAKISTAN THROUGH ARMA MODEL Muhammad Usaid Awan Muhammad Abdul Muqueet Shahzad Ahsan Saeed Fawad Khan
- 2. The goal of our project is to determine inflation in Pakistan for upcoming months. This report represents our results in two sections. Section one discusses the raw data used and the methodology employed for this projection. Section two presents the results obtained through our forecasting model. Data and Methodology: The data used for this project was of monthly CPI of Pakistan from 1989:07- 2013:09. The year 2008 was used as base year for CPI calculations. The large data presented a crucial part in having higher degrees of freedom while projecting CPI using ARMA Model through RATS. We employed ARMA Model to project inflation. The basic model is represented as: 𝑦! = 𝑎! + 𝑎! 𝑦!!! + ⋯ + 𝑎! 𝑦!!! + 𝛽! 𝜖!!! + ⋯ + 𝛽! 𝜖!!! where: 𝑎! 𝑦!!! ! !!! is autoregressive (AR) for CPI 𝛽! 𝜖!!! ! !!! is moving average (MA). In order to use the model, first the CPI figures were tested for stationary using Augmented Dickey Fuller (ADF) test. The CPI figures were found to be first order stationary. The related figures are included in the appendix.
- 3. Before running the model we tested for seasonality using Autocorrelation Function and Partial Auto Correlation Function. The graph depicted seasonality due to sudden heights at twelfth lag. As model selection criteria for forecasting, we considered the standard criteria presented in the literature such as coefficient of determination (R2), the adjusted coefficient of determination (R2a), the Akaike Information Criterion (AIC), the Schwartz Bayesian Criterion, t-statistics and Ljung-Box Q-Statistics. In total, one hundred and twenty two models were run and using the selection criteria along with Box-Jenkins parsimony approach, the best model was selected. The outcome is presented in Table 1 in the Appendix. Correlations of the first difference of FDLCPI DCORRS DPCORRS 0 5 10 15 20 25 30 35 40 -1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
- 4. Forecasting Outcomes: After running several ARMA models and selecting the best model through selection criteria, the model was then used to make the dynamic forecasts for upcoming trend in CPI. Through our calculations we found that the CPI values will follow the following trend for upcoming months. Month Percentage Change CPI Projections Aug-13 185.7 Sep-13 -0.3 185.2 Oct-13 0.9 186.9 Nov-13 0.5 187.8 Dec-13 0.3 188.4 Jan-14 0.9 190.1 Feb-14 0.3 190.6 March-14 0.5 191.5 April-14 1.0 193.3 May-15 0.5 194.4 June-14 0.7 195.7 The CPI projections obtained from dynamic forecasting model works best for short run projections only. Therefore, we only obtained CPI projections for the end of this fiscal year. The model can be repeated at end of each quarter to revise projections.
- 5. Appendix Figure 1: FDI First Order Stationary: Table 1: Final Model 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 -0.01 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07