Tag Archives: Partial Autocorrelation Function

Comparative Performance between the Box – Jenkins and Time Series Regression Models (Published)

This work compared the performance of the Box-Jenkins and time series regression models. The two methods were theoretically presented. Data was also collected for fitting the models. Three test measures were used for the comparative analysis. The results showed that the time series regression model performs better than the Box-Jenkins model.

 

Keywords: Arima models., Autocorrelation Function, Partial Autocorrelation Function, time series regression, white noise process

Forecasting of Exchange Rate between Naira and US Dollar Using Time Domain Model (Published)

Most time series analysts have used different technical and fundamental approach in modeling and to forecast exchange rate in both develop and developing countries, whereas the forecast result varies base on the approach used or applied. In these view, a time domain model (fundamental approach) makes the use of Box Jenkins approach was applied to a developing country like Nigeria to forecast the naira/dollar exchange rate for the period January 1994 to December 2011 using ARIMA model. The result reveals that there is an upward trend and the 2nd difference of the series was stationary, meaning that the series was I (2). Base on the selection criteria AIC and BIC, the best model that explains the series was found to be ARIMA (1, 2, 1). The diagnosis on such model was confirmed, the error was white noise, presence of no serial correlation and a forecast for period of 12 months terms was made which indicates that the naira will continue to depreciate with these forecasted time period.

Keywords: AIC, Auto Regressive Integrated Moving Average, Autocorrelation Function, BIC, Exchange Rate, Partial Autocorrelation Function