25,762 research outputs found

    Forecasting airport passenger traffic: the case of Hong Kong International Airport

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    Hong Kong International Airport is one of the main gateways to Mainland China and the major aviation hub in Asia. An accurate airport traffic demand forecast allows for short and long-term planning and decision making regarding airport facilities and flight networks. This paper employs the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology to build and estimate the univariate seasonal ARIMA model and the ARIMX model with explanatory variables for forecasting airport passenger traffic for Hong Kong, and projecting its future growth trend from 2011to 2015. Both fitted models are found to have the lower Mean Absolute Percentage Error (MAPE) figures, and then the models are used to obtain ex-post forecasts with accurate forecasting results. More importantly, both ARIMA models predict a growth in future airport passenger traffic at Hong Kong

    Forecasting Player Behavioral Data and Simulating in-Game Events

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    Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

    Can Google searches help nowcast and forecast unemployment rates in the Visegrad Group countries?

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    Online activity of the Internet users has been repeatedly shown to provide a rich information set for various research fields. We focus on the job-related searches on Google and their possible usefulness in the region of the Visegrad Group -- the Czech Republic, Hungary, Poland and Slovakia. Even for rather small economies, the online searches of their inhabitants can be successfully utilized for macroeconomic predictions. Specifically, we study the unemployment rates and their interconnection to the job-related searches. We show that the Google searches strongly enhance both nowcasting and forecasting models of the unemployment rates.Comment: 22 pages, 2 figures, 3 table

    Sales Growth of New Pharmaceuticals Across the Globe: The Role of Regulatory Regimes

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    Prior marketing literature has overlooked the role of regulatory regimes in explaining international sales growth of new products. This paper addresses this gap in the context of new pharmaceuticals (15 new molecules in 34 countries) and sheds light on the effect regulatory regimes have on new drug sales across the globe. Based on a time-varying coefficient model, we find that differences in regulation substantially contribute to cross-country variation in sales. One of the regulatory constraints investigated, i.e. manufacturer price controls, has a positive effect on drug sales. The other forms of regulation such as restrictions of physician prescription budgets and the prohibition of direct-to-consumer advertising tend to hurt sales. The effect of manufacturer price controls is similar for newly launched and mature drugs. In contrast, regulations on physician prescription budget and direct-to-consumer advertising have a differential effect for newly launched and mature drugs. While the former hurts mature drugs more, the latter has a larger effect on newly launched drugs. In addition to these regulatory effects, we find that national culture, economic wealth, introduction timing, lagged sales and competition, also affect drug sales. Our findings may be used as input by managers for international launch and sales decisions. They may also be used by public policy administrators to compare drug sales in their country to other countries and to assess the role of regulatory regimes therein.economics;regulation;culture;drug;international new product growth;penalized splines;pharmaceutical;timevarying effects
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