UK tourism arrivals and departures: seasonality, persistence and time trends

Issues such as seasonality, persistence and trends are examined in the series referring to the number of UK arrivals and departures using techniques based on fractional integration. This methodology is much more flexible than others based on integer degrees of differentiation and permits us to describe in a more general way the effects of shocks in the series. Our results indicate that the series display significant time trends; they show high persistence with orders of integration in the fractional range, thus showing long-lasting effects of shocks; seasonality is an important issue, and in removing the seasonality through seasonal differentiation, the time trends disappear though persistence remains as a relevant feature of the data. Policy implications of the results obtained are displayed at the end of the article

An economic comparison of X[bar], cumulative sum and geometric moving average control charts for controlling process mean

In production processes, there are two types of variations that affect production quality - - variations produced by chance causes and variations produced by assignable causes. One of the main instruments in quality control used to control quality by distinguishing between variations produced by chance causes and a real process change is the control chart. Each type of control chart has advantages and disadvantages in a specified situation. For example, some control charts fail to detect small shifts, while the others are ineffective to detect large shifts in process mean. In this study, three types of control charts, namely, X[bar], cumulative sum, and geometric moving average control charts were compared on an economic basis. A simulation model was developed to simulate the control chart functions in a typical production process. The simulation was executed in BASIC on an IBM PC/XT. Before comparison, each control chart was matched so that all the control charts have the same characteristics when the process operates in-control for a certain period of time. The effects of the type of control chart, sample size, sampling interval, and the magnitude of shift in process mean on profit per hour were observed and analyzed using Analysis of Variance (ANOVA). The results show that, in general, the cumulative sum control chart has advantage over the other two types of control charts when shift of small magnitude of about 0.5σ is present. X[bar]-control chart is ineffective to detect small shifts; however, its effectiveness increases sharply as the magnitude of shift increases to values of 1.5σ or beyond. Geometric moving average control chart gives best results at intermediate shift levels of about l.0σ. Of the three sample sizes (3, 4 and 5) used in this study, sample size of five yields the highest profit per hour. However, too large a sample size may result in a decrease of profit per hour if the testing causes the destruction of items and the cost of sampling per item is very high. Small sampling interval of one hour yields the highest profit per hour among three sampling intervals (1, 2 and 4 hours) used in this study. Too small sampling interval could yield lower profit per hour if the increased cost of more frequent sampling, more investigations caused by false alarms, and more frequent shut down of the production process exceeds the savings from early detection of the shift, particularly, when the cost of sampling, the cost of searching for an assignable cause, and the income per hour of production are very high

The Appropriate Model and Dependence Measures of Thailand’s Exchange Rate and Malaysia’s Exchange Rate: Linear, Nonlinear and Copulas Approach

The objectives of this study are to find the fitting model and dependence measures of both Thailand’s exchange rate and Malaysia’s exchange rate during, between, and after the World’s recent financial crises based on linear, nonlinear and empirical copula approaches. The results of the study confirm that the nonlinear model (NNTs) is an appropriate model for Thailand’s exchange rate return in percentage during the periods of 2008-2011but not for Malaysia’s exchange rate return. Based on empirical copula approach, the dependence measures are very small between Thailand’s exchange and Malaysia’s exchange. This seems to suggest that when global economy is affected by World’s financial crisis, the nonlinear approach should be used to predict Thailand’s exchange rate return in percentage. In addition, it suggests that both the nonlinear and linear approaches should be used to predict the Malaysia’s exchange rate return in percentage. Moreover, the relationship between the exchange rate of Thailand and that of Malaysia is not strong. This is also true for the currencies of both countries.Linear, Nonlinear, Copulas, Exchange Rate, Thailand, Malaysia The Appropriate Forecasting Model

International Tourist Arrivals In Thailand: Forecasting With Arfima-Figarch Approach

Forecasting is an essential analytical tool for tourism policy and planning. This paper focuses on forecasting methods based on ARFIMA(p,d,q)-FIGARCH(p,d,q). Secondary data was used to produce forecasts of the number of international tourist arrivals to Thailand for the period of 2009-2010. Research results during this period confirm that the best forecasting method based on the ARFIMA(p,d,q)-FIGARCH(p,d,q) model is ARFIMA(1,-0.45,1)-FIGARCH(1,-0.07,1). Furthermore, this model predicts that the number of international tourist arrivals in Thailand for the period of 2009-2010 will not go up or be constant. If these results can be generalized for future years, then it suggests that both the Thai government sector and also the private tourism industry sector of Thailand need to develop the tourism market of Thailand immediately and also develop tourism products in Thailand urgently.Thailand, ARFIMA-FIGARCH method, International Tourists

International Tourists’ Expenditures In Thailand: A Modelling Of The Arfima-Figarch Approach

Forecasting is an essential analytical tool for tourism policy and planning. This paper focuses on forecasting methods based on ARFIMA(p,d,q)-FIGARCH(p,d,q). Secondary data was used to produce forecasts of international tourists’ expenditures in Thailand for the period 2009-2010. The results of this research for this period confirms that the best forecasting method based on ARFIMA(p,d,q)-FIGARCH(p,d,q) method is the ARFIMA(1,-0.672,1)-FIGARCH(1,-0.180,1) method. Furthermore, this method predicts the expenditures of tourists in Thailand for the period of 2009-2010 will be constant or decline. If these results can be generalized for future years, then it suggests that both the Thailand government sector and also the private tourism industry sector of Thailand need to develop the tourism market of Thailand immediately and also develop tourism products in Thailand.Thailand, ARFIMA-FIGARCH method, International Tourists’ Expenditure