Abstract
Long memory and nonlinearity have been proven as two models that are easily to be mistaken. In other words, nonlinearity is a strong candidate of spurious long memory by introducing a certain degree of fractional integration that lies in the region of long memory. Indeed, nonlinear process belongs to short memory with zero integration order. The idea of the forecast is to obtain the future condition with minimum error. Some researches argued that no matter what the model is, the important thing is we can generate a reliable forecast. Several tests have been proposed to solve the problem of distinguishing long memory and nonlinearity appears in a series. The power of the tests is somehow questionable in the sense that there is still a probability to obtain spurious result. To overcome this, model combination will be one of the solutions dealing with uncertainty in the model selection. In this case, it is assumed that both processes are candidates of best models with certain power to generate a good forecast. This research investigates the performance three model combination approaches to forecast the Indonesia inflation i.e., simple combination using balance weight as well as inverse Mean Prediction Error (MSPE) weight and Bayesian Model Averaging (BMA). These methods are capable to generate a reliable forecast in very short lead time. Combination using BMA outperforms the simple averaging for 1 ahead forecast, while MSPE performs best for long lead forecasts.
Highlights
The references therein discuss the real and spurious long memory properties of stock market data
Nonlinearity is a strong candidate of spurious long memory by introducing a certain degree of fractional integration that lies in the region of long memory
Combination is done by combining the forecasts generated from different time series models with an expectation that the forecast will be more reliable than single model forecast
Summary
The references therein discuss the real and spurious long memory properties of stock market data. The existing tests cannot detect spurious long memory perfectly It means that there is uncertainty in the model choice leading to probability of obtaining wrong identification result. To overcome this problem, it turns to the idea of combining the forecast output from both competing models instead of selecting the best model. It turns to the idea of combining the forecast output from both competing models instead of selecting the best model This idea is quiet reasonable and straightforward as the forecasters never know the true model especially for the case of long memory and nonlinear process. The stylized facts and results of forecasting the Indonesian inflation using forecast combination are presented in section 3 and 4 concludes
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