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As a result of the increase in both the international tourists’ expenditures and tourist arrivals to Thailand, there is a growing interest in determining the trend of international tourists’ expenditures based on time-series modelling. In our article secondary data were used to produce forecasts of the international tourists’ expenditures in Thailand between 2009 and 2010. The forecasting method is based on the ARFIMAX (0, 0.197, 0, 0.033) model. Furthermore, this method predicted that international tourists’ expenditures in Thailand between 2009 and 2010 will have to contract and slow down. This paper seeks to determine whether the international tourists’ expenditures are affected by other circumstances. The results of this study revealed that the international tourist arrivals to Thailand will also have to slow down. However, from the results, there is solid evidence to support such a claim.

The competitiveness of the sheep sector in East Europe has been decreasing from year to year. The value added in the sector is not generated in the countries as a high proportion of the lambs are exported. For example, in Hungary, 95% of the lambs, unnecessary for replacement, are sold at an average weight of 21 kg and are slaughtered abroad. A stochastic model was constructed to investigate the connections between the cycle phases of the mutton production. Three modules were distinguished, the lamb production, fattening and slaughtering-processing sub-modules. The aim of our study was to identify the gross value added generated in the three sub-modules and to analyse the main factors influencing its volume using the conditions in Hungary as an example. The major hypothesis of our research was that the profitability of the production chain is mainly determined by the breed. The results showed that, considering market prices, the gross value added in the processing module was mostly influenced by the number of lambs sold per ewe per year at the bottom level of the mutton product chain. The next most important factors were the weight gain in the lamb producing and fattening sub-modules and dressing percentage in slaughtering-processing sub-module. Contour plots were constructed which help to describe the relationship among analyzed factors. Using the contour plots, the gross value added for different combinations of these factors might be forecast.

This study examines the impact of economic globalization on the human trafficking inflows into the Greater Mekong Sub-region (GMS) countries. The paper empirically tests for a cross-section of six countries, including Cambodia, the Yunnan Province of the People’s Republic of China (PRC), Lao People’s Democratic Republic (Lao PRD), Myanmar, Thailand, and Vietnam. Employing the Pooled OLS estimator, as the theory predicts, the economic globalization increases trafficking inflow into the GMS. However, only foreign direct investment (FDI) affects the degree of trafficking of persons, while the effect of trade is insignificant. Moreover, Exchange rate, Migration, Population and Democracy induce higher rates of trafficked persons, whereas Gross Domestic Product (GDP) and other factors, such as education, vocational training and micro-finance through village development funds decrease this problem in the region. Gross National Income per capita (GNI per capita) and rule of law do not have any significant effect on human trafficking.

The competitiveness of the Hungarian sheep sector has been in steady decline for some time now. Crucial has been the problem that the value added in the sector is not generated in Hungary, as most of the produced lambs in Hungary leave the country with an average weight of 21 kilograms, with slaughtering happening abroad.A model has been constructed for our investigations, which introduces the connections between the product cycle phases for mutton in Hungary. This model allows us to calculate the volume of gross value added generated within specific product cycle phases. We used Monte Carlo simulation for our examination, for which the Crystall ball software package was utilized, namely the OptQuest module, for optimization. First, we conducted an optimization of an experiment number of 500,000 for “Gross value added” in the case of the slaughterhouse. During the optimization, Easter, Christmas and August lamb ratio and ewe number, as well as progeny, were set as decision variables and examined as values of gross value added, the decision variables of which contribute to obtaining the best results. The gained decision variables were set in the model and a Monte Carlo simulation was run with an experiment number of 500,000, where only the values of the conditions were changed along the pre-set dispersion; the values of the decision variables were fixed. The most significant aim of our investigation was to identify the volume of gross value added generated during processing in various phases of the product chain and the change of which inputs affected this volume the most. The findings proved that, in the case of capital uniformity, the output of processing was most influenced by sheep progeny on the bottom level of the mutton product chain. This factor is followed by that of weight gain in the source material producing and fattening sub-modules, as well as the gross wage in starter lamb feed and meadow hay in the source material producing sub-modules. Contour plots helped to describe the connections between these factors. By using contour plots, the volume of gross value added might be forecast for various combinations of factors.

In our research we examine the behaviour of both Thailand’s and India’s international tourism market by using long-memory analysis. The international tourism market of Thailand combined with seven groups such as East Asia, Europe, The Americas, South Asia, Oceania, Middle East and Africa. Similarly, the international tourism market of India combined with nine countries: USA, UK, Canada, Germany, France, Japan, Malaysia, Australia and Sri Lanka. Moreover, three statistical tests for long-memory process such as R/S test, Modified R/S test and GPH-test are employed to study these markets. The empirical findings in general provide more support for long memory process in international tourism market of Thailand and evidence for short-term dependence in international tourism market of India. Therefore, the policy makers of each country should understand the behaviour of long memory process in international tourism market before launching any stimulating campaign to this industry.

The continuous weakening of Hungarian sheep sector and its low effectiveness in terms of value added have posed crucial problems in recent years.The focal problem has been partially caused by economic and market problems.Among these issues, mostly the poor mutton supply chain gives rise to difficulties; therefore the present study seeks to reveal the factors/input variables which predominantly influence the generation of value added. We have constructed a model for the mutton product cycle to represent the relations of phases but mutton trade is not included.The most significant aim of our investigation was to identify the volume of value added generated during processing in various phases of the product cycle and the change of which inputs affected this volume. The received findings suggested that in case of capital uniformity the output of processing was mostly influenced by sheep progeny on the bottom level of the mutton product cycle.

There have been growing interest in studying behavior of long memory process in tourism market. In this research examine the behavior of India’s international tourism market based on long-memory analysis. The international tourism market of India combined with nine countries: USA, UK, Canada, Germany, France, Japan, Malaysia, Australia and Sri Lanka. Moreover, three statistical tests for longmemory process such as R/S test, Modified R/S test and GPH-test are employed to test in these market. The empirical findings in general provide more support for no long memory process or no long-term dependence in international tourism market of India.

There have been growing interest in studying behavior of long memory process in tourism market. In this research examine the behavior of India’s international tourism market based on long-memory analysis. The international tourism market of India combined with nine countries: USA, UK, Canada, Germany, France, Japan, Malaysia, Australia and Sri Lanka. Moreover, three statistical tests for longmemory process such as R/S test, Modified R/S test and GPH-test are employed to test in these market. The empirical findings in general provide more support for no long memory process or no long-term dependence in international tourism market of India.

Structural equation model (LISREL 8) was applied to test the causal relationships between tourist travel motivations and tourist destination.A survey containing Likert scale questions was conducted to collect data from 100 tourists who had travelled to Greece’s tourist destination. With the help of factor analysis, four dimensions were identified for scales used in the study: travel cost satisfaction, tourism product, tourism product attributes, and tourism product management. Results indicated that the travel cost satisfaction of tourists has a positive influence on tourism product, tourism product attributes and tourism product management. Moreover, our results suggested that the tourist demographics has a positive influence on tourism product and tourism product attributes and has an insignificant relationship with tourism product management. Based on our findings the tourist demographics has not influence on tourism product management. However, these findings suggest that both the private tourism and the governmental tourism sector should develop a better management of tourist destinations so as to develop a stronger attraction of tourism, better amenities, a better accessibility, an appropriate image, to make tourism competitive and to keep tourism product prices at a reasonable level. The implications of the tourism demand model can be used for the public environmental policy-making process based mainly on reasons of interest, ideology or understanding.

Agricultural production is among the riskiest production activities. Similarly to other branches of agriculture in animal breeding the finished product is the result of complex procedures. The biological technological procedure, the creation of the product is affected by an outstanding number of environmental factors which also cause uncertainties. In the North Great Plain Region of Hungary, sows, gilts and slaughter pigs are produced on a corporate farm. The reliable operation data of this company provide a stable basis for and estimating future costs and revenue and their distributions. Monte Carlo methods are one of the generally accepted tools for modeling risks. The significant independent variables, their ranges and probability distributions, and the correlation between them were inputs to the model. The values of the variables were produced using a random number generator. The computer simulation was performed using @Ris (PalisadeCorporation) software. The study concentrates on the factors affecting the number of off spring (piglets). Model inputs were the mating, mortality and farrowing rates; the costs and the income values based on these rates have been analysed as the output data of the model.

Forecasting is an essential analytical tool in tourism policy and planning. This paper focuses on forecasting methods based on X-12-ARIMA seasonal adjustment and this method was developed by the Census Bureau in the United States. It has been continually improved since the 1960s, and it is used by many statistics agencies and central banks. The secondary data were used to produce forecasts of international tourist arrivals to India for 2007-2010 and also these data were used to produce forecasts of international tourist arrivals to Thailand for 2006-2010. From these period the results confirm that the best forecasting method based on the X-12-ARIMA seasonal adjustment is X-12-ARIMA(0,1,2)(0,1,1), X-12-ARIMA(0,1,1)(0,1,1) and X-12-ARIMA(2,1,0)(0,1,1) for India and the best forecasting method based on this method is X-12-ARIMA(0,1,1)(0,1,1) and X-12-ARIMA(2,1,0)(0,1,1) for Thailand. Furthermore this method predict that international tourism arrivals to India for 2007–2010 will growth at a positive rate as same as in this during period the number of international tourists arrival to India will be 5,079,651 million, 5,652,180 million, 6,224,480 million and 6,796,890 million, respectively. Also this method predict that international tourism arrivals to Thailand for 2006-2010 will growth at a positive rate as same as in this during period the number of international tourists arrival to Thailand will be 12,211,033 million, 12,699,532 million, 13,187,591 million, 13,674,669 million and 14,161,998 million, respectively. If these results can be generalized for future year, then it suggests that both the India government sector and the Thailand government sector also the private tourism industry sector of these country should prepare to receive increasing numbers of international tourist arrivals both to India and Thailand in this period.