Bayesian Forecasting Model For Inflation Data
Loading...
Date
2015-09
Authors
AMRY, ZUL
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Many of the data in the economic problem can be addressed by the model
time series, such discussions inflation data using ARMA model. This thesis focuses on the discussion of the ARMA model with a Bayesian approach on the inflation data. The major problem in the analysis of ARMA model is the estimation of the parameters in the model. In Bayes statistics, the parameters estimated will be viewed as a quantity of random variable which the variation is described by probability distribution and known as the prior distribution.
The objective of this thesis is to determine the Bayes estimator, point forecast, and forecast interval with Bayesian approach for inflation data using ARMA model under normal-gamma prior and Jeffrey’s prior assumption with quadratic loss function. The method of research which used is time series forecasting model with Bayesian approach. To conclude whether the result of the model is adequate, an investigation was conducted to test the autocorrelation of the residuals by using the Q- Ljung Box statistic, while looking at the accuracy of forecasting model used the RMSE, MAE, MAPE, and U-Statistics. The results of research are the Bayes estimator, point foecast, and forecast interval in mathematical expression. Furthermore, the result of research is applied to inflation data and compared to traditional method. The results shows that the Bayesian method is better than the traditional method. The simulation for some of the data size shows also that the Bayesian method is better than the traditional method.
Description
Keywords
Bayesian Forecasting Model , For Inflation Data