Statistical inference for alpha-series process with truncated normal distribution
In this paper we consider an alpha-series process when the distribution of the first occurrence time of an event is assumed to be truncated normal distribution. Firstly, we study several nonparametric inferences including the Least-square estimate (LSE) and the Maximum-likelihood estimate (ML). Some estimators of the parameters in alpha-series process are derived by using the methods referred above. Furthermore, we consider the consistency and asymptotic normality properties of the estimators. Finally, we obtain the simulation results which are calculated over Monte Carlo replications to compare the performance of these variety estimators.
ABSTRACTWe utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.