P-Star models have become increasingly popular in recent years in developed countries. However data constraints have limited their applicability to the LDCs. In this paper, such a model is attempted for India using both annual and quarterly data for the period 1955-1995. It is found that velocity in India is trend stationary and using cointegration techniques it is then possible to develop a model to gauge inflationary pressures in the economy. The model is well calibrated to data, and in out-of-sample forecasts, it significantly outperforms a seasonal ARMA benchmark model. The velocity gap version of the model is particularly successful.
The time series forecasting strategy, Auto-Regressive Integrated Moving Average (ARIMA) model, is applied on the time series data consisting of Adobe stock prices, in order to forecast the future prices for a period of one year. ARIMA model is used due to its simple and flexible implementation for short term predictions of future stock prices. In order to achieve stationarity, the time series data requires second-order differencing. The comparison and parameterization of the ARIMA model has been done using auto-correlation plot, partial auto-correlation plot and auto.arima() function provided in R (which automatically finds the best fitting model based on the AIC and BIC values). The ARIMA (0, 2, 1) (0, 0, 2) [12] is chosen as the best fitting model, with a very less MAPE (Mean Absolute Percentage Error) of 3.854958%.
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