5This paper introduces Singular Spectrum Analysis (SSA) for tourism demand forecasting 6 via an application into total monthly U.S. Tourist arrivals from 1996-2012. The global 7 tourism industry is today, a key driver of foreign exchange inflows to an economy. Here, we 8 compare the forecasting results from SSA with those from ARIMA, Exponential Smoothing 9 (ETS) and Neural Networks (NN). We find statistically significant evidence proving that 10 the SSA model outperforms the optimal ARIMA, ETS and NN models at forecasting total 11 U.S. Tourist arrivals. The study also finds SSA outperforming ARIMA at forecasting U.S. 12Tourist arrivals by country of origin with statistically significant results. In the process, we 13 find strong evidence to justify the discontinuation of employing ARIMA, ETS and a feed-14 forward NN model with one hidden layer as a forecasting technique for U.S. Tourist arrivals 15 in the future, and introduce SSA as its highly lucrative replacement. rather than weakened this need to forecast tourist demand accurately. 25As discussed in the following section there is an extensive and high profile existing literature 26 on forecasting tourism demand. This literature covers a wide range of different forecasting 27 techniques, applied to a wide range of different countries or locations. The purpose of this paper 28 is to add to this literature by introducing a new model for forecasting tourist arrivals and to 29 apply it to inbound U.S. Tourist arrivals. Forecasting U.S. Tourist arrivals is both a demanding 30 and important task, mainly because these data exhibit a high degree of fluctuation over time. 31Figure 1 depicts the time series for total monthly U.S. Tourist arrivals between January 1996 and 32 November 2012. A first look at the time series suggests signs of seasonality in U.S tourist arrivals. 33The figure also shows that the tourism industry in the U.S. is experiencing rapid development in There are a number of components which define a good demand forecasting model for tourism Australia, concluding that using models expressed in first differences increased forecast accuracy. Using data for Hong Kong they find use of tourism arrivals to be more affected by income in 117 the country of origin and tourism expenditure to be more sensitive to prices. Wan, Wang, and 118Woo (2013), also using tourist arrival data for Hong Kong, assess the properties of disaggregated 119 forecasts using a seasonal ARIMA model relative to aggregate forecasts. They find the sum of 120 disaggregated forecasts to provide greater accuracy than an aggregate forecast. 121A very closely related strand in the literature seeks to combine two or more forecasting 122 models into a new hybrid model and to test whether this results in greater forecast accuracy. 123Andrawis, Atiya, and El-Shishiny (2011) finds that, in forecasts of tourism arrivals into Egypt, 124combining short and long term forecasts improves accuracy compared to the individual forecasts. 125Cang (2011)
This study contributes to the FDI literature by investigating the impact of all four locational motives of FDI in Sub-Saharan African (SSA) countries for the period 1996 -2010. To achieve this aim, panel data techniques (pooled OLS, fixed effects and GMM) were employed on a sample of SSA countries. The empirical results showed that efficiency and strategic asset seeking factors influenced FDI activities in SSA for the period investigated. Market size also influenced FDI however this was less robust to specifications. Surprisingly, FDI in SSA was not resource seeking. Furthermore, a statistical test confirmed structural and behavioural differences in FDI determinants between SSA sub-regional groups and when analysed separately, FDI in West and Central SSA was market and efficiency seeking while FDI in South and East Africa was best explained by efficiency seeking factors. Based on the empirical findings, a number of policy implications were derived. These policy implications include further implementation of policies targeted at increasing and sustaining trade liberalisation and trade diversification, control of corruption, credible upgrades and productive investments in infrastructure, and support for human capital accumulation as FDI is increasingly directed towards R&D, innovation and strategic asset activities.
This study examines the gender wage gap in the USA using two separate cross-sections from the Current Population Survey (CPS). The extensive literature on this subject includes wage decompositions that divide the gender wage gap into "explained" and "unexplained" components. One of the problems with this approach is the heterogeneity of the sample data. In order to address the difficulties of comparing like with like, this study uses a number of different matching techniques to obtain estimates of the gap. By controlling for a wide range of other influences, in effect, we estimate the direct effect of simply being female on wages. However, a number of other factors, such as parenthood, gender segregation, part-time working, and unionization, contribute to the gender wage gap. This means that it is not just the core "like for like" comparison between male and female wages that matters but also how gender wage differences interact with other influences. The literature has noted the existence of these interactions, but precise or systematic estimates of such effects remain scarce. The most innovative contribution of this study is to do that. Our findings imply that the idea of a single uniform gender pay gap is perhaps less useful than an understanding of how gender wages are shaped by multiple different forces.
An extensive literature exists on the adverse effects of corruption on inward FDI and the impact this may have on economic development but the reverse causality has not been fully explored. Legislation in the US and the EU prohibits firms from engaging in corrupt practices in foreign countries and this suggests that foreign owned firms might be less likely to pay bribes. However, such legislation may be ineffective because foreign firms have to adapt to local market conditions or risk being uncompetitive. Using firm level data for 41 emerging countries, a probit model estimates the probability that a firm pays bribes. To allow for possible endogeneity this probit analysis is repeated with an instrument to proxy for endogenous foreign ownership. Then, a propensity score matching technique tests for differences in the propensity to pay bribes by domestic and foreign firms. The paper finds that no difference is the behaviour of foreign owned and domestic firms with respect to corrupt practices. Results are robust to different levels of foreign ownership and support the view that foreign owned firms adapt to local practices and are neither more nor less likely to pay bribes than comparable domestic firms. The paper finds that other variables including bureaucracy, government contracts and perceived difficulties with civil society (legal and political) do have statistically significant effects on increasing bribery and that some others, such as per capita GDP, tend to reduce bribery. The study concludes that there is no evidence that foreign ownership, after investment has occurred, tends to reduce bribery but it does support the view that foreign owned firms adopt local behavioural norms.
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