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The study uses a Markov-switching model (MSM) to investigate the relationship between foreign-exchange earnings from tourism and macroeconomic factors in Taiwan. The MSM decomposes foreign-exchange cycles into a high growth stage (HGS) and a low growth stage (LGS). The steady-state probability remains foreign-exchange earnings from tourism in the HGS and LGS are 0.7486 and 0.8631, respectively. The findings indicate that foreign-exchange earnings from tourism in Taiwan have a high tendency to stay in the LGS state and a low tendency to shift from the LGS state to the HGS state. Furthermore, the results show that macroeconomic factors in Taiwan can continuously affect foreign-exchange earnings from tourism in the HGS. [ABSTRACT FROM PUBLISHER]

This paper contributes to the tourism and hospitality literature by examining Taiwanese policy momentum in the development of Taiwan's cultural and creative industries (CCIs). This study uses a Markov-switching model (MSM) proposed by Hamilton [1989. A new approach to the economic analysis of nonstationary time series and the business cycle.Econometrica,57, 357–384] to analyse the influence of government policies, and the sample comprised 18 Taiwanese CCI stocks for the 2000–2014 period. The MSM decomposes policy-momentum cycles into two distinct states: high volatility and low volatility (HV and LV). The mean growth rate of the HV is 0.87% and the average growth rate of the LV is 10.37%. The state probability that will remain in the LV is 80%, and the state probability that will remain in the HV is 94%, meaning that the probability will shift from an HV (LV) to an LV (HV) is 6% (20%). The findings indicate that cultural creative industries in Taiwan have a high tendency to stay in the HV state and a low tendency to shift from an HV state to an LV state. Empirical results show that Taiwanese government policies were the main activator of development in Taiwan's CCIs and this development can continuously maintain a positive mean growth rate, even in the event of sudden economic downturns. [ABSTRACT FROM PUBLISHER]

Autoregressive model is a popular method for analysing the time dependent data, where selection of order parameter is imperative. Two commonly used selection criteria are the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), which are known to suffer the potential problems regarding overfit and underfit, respectively. To our knowledge, there does not exist a criterion in the literature that can satisfactorily perform under various situations. Therefore, in this paper, we focus on forecasting the future values of an observed time series and propose an adaptive idea to combine the advantages of AIC and BIC but to mitigate their weaknesses based on the concept of generalized degrees of freedom. Instead of applying a fixed criterion to select the order parameter, we propose an approximately unbiased estimator of mean squared prediction errors based on a data perturbation technique for fairly comparing between AIC and BIC. Then use the selected criterion to determine the final order parameter. Some numerical experiments are performed to show the superiority of the proposed method and a real data set of the retail price index of China from 1952 to 2008 is also applied for illustration. [ABSTRACT FROM AUTHOR]

The autoregressive (AR) model is a popular method for fitting and prediction in analyzing time-dependent data, where selecting an accurate model among considered orders is a crucial issue. Two commonly used selection criteria are the Akaike information criterion and the Bayesian information criterion. However, the two criteria are known to suffer potential problems regarding overfit and underfit, respectively. Therefore, using them would perform well in some situations, but poorly in others. In this paper, we propose a new criterion in terms of the prediction perspective based on the concept of generalized degrees of freedom for AR model selection. We derive an approximately unbiased estimator of mean-squared prediction errors based on a data perturbation technique for selecting the order parameter, where the estimation uncertainty involved in a modeling procedure is considered. Some numerical experiments are performed to illustrate the superiority of the proposed method over some commonly used order selection criteria. Finally, the methodology is applied to a real data example to predict the weekly rate of return on the stock price of Taiwan Semiconductor Manufacturing Company and the results indicate that the proposed method is satisfactory. [ABSTRACT FROM AUTHOR]

This article discusses the expansion of credit cards in Taiwan. The intention of the study is (1) to compare the performance of various diffusion models, such as logistic models, Fourier models and autoregressive models; (2) to identify the important explanatory factors regarding the expansion of the credit card market in Taiwan. The data set comprised annual data regarding the number of credit cards in circulation between 1991 and 2005. Empirical results show that the diffusion rate increased with per capita Gross Domestic Product (GDP), but decreases with number of banks and net savings. The rate of market penetration for the number of credit cards in circulation will reach 204.3% of the population. [ABSTRACT FROM AUTHOR]

The parametric bootstrap method is applied to derive the prediction intervals for stochastic volatility models. The study adopts the parameters estimation developed by So et al . (1997) and proves the validity of the proposed bootstrap procedure for this process. The basic stochastic volatility model specifies the mean equation with standard normal error. It is found, via simulation study, that the same algorithm can be employed to the model with heavy-tailed innovations, which demonstrates the potential of the bootstrap techniques. This methodology is also applied to a real data example to predict the daily observations on the S&P 500 index and the results confirm that our interval predictions are satisfactory. [ABSTRACT FROM AUTHOR]