Forecasting tourism revisited

This article examines the accuracy and efficiency of forecasting techniques by applying time series regression to forecasting visitor arrivals. Past studies have shown that simpler time series techniques perform as well or better than complex forecasting models. An assessment of visitor forecasts developed at regional, destination and individual market levels suggests that time series regression performs well in producing annual forecasts of visitors which can also serve as a baseline for evaluating the net returns from applying more complex techniques. Tourism managers should appreciate the usefulness of simpler formal methods in developing forecasts of visitors.     

Modelling gold futures: should the level of speculation inform our choice of variables?

Prior literature provides conflicting evidence about the impact of speculation on gold futures returns, volatility, and the relationship between market fundamentals and prices. In this paper, we exploit trade volume information to determine the most appropriate family of factors to adopt when modelling gold futures. Using the Disaggregated Commitment of Traders report, we find that extreme levels of speculation are informative in that they signify a shift in the relative modelling accuracy of macroeconomic and latent factors. A simple composite prediction framework, incorporating the changing level of speculation, empirically demonstrates the uncovered phenomenon and offers improved predictive accuracy for gold futures prices. Furthermore, our findings are shown to be robust to alternative latent and macroeconomic model specifications.     

Leaning Tower of Pizza: A Business Start‐up

The following take‐out pizzeria restaurant simulation highlights the information sources that an entrepreneur can use to prepare a cash budget and financial forecast for a new business venture. Based on the information contained in this simulation, students make a capital budgeting decision and prepare pro forma financial statements.     

武陵源旅游需求预测模型精确性的研究

旅游预测的计量经济学模型是国外研究较多的一个方向,在国内方兴未艾。由于旅游业各种统计数据较难获得,以及旅游业的复杂性,旅游需求模型应用的实例研究很少。以武陵源为案例,运用计量经济学模型中的时间序列模型（SARI—MA）和一般一特殊模型对其进行预测,预测的结果与实际值较为吻合。本研究不仅是旅游预测研究中计量经济学模型一次成功的实证研究;同时在时间序列模型中,选用主景点的门票出售数量替代游客人次,给今后的研究者提供了一种新的数据选择视角。也就旅游预测模型研究中经常会遇到的各种技术难题提出了解决方案。     

Long-run growth uncertainty

Observed macroeconomic forecasts display a positive correlation between expectations of long-run growth of endogenous variables (e.g., output) and cyclical activity. Existing business cycle models appear inconsistent with the evidence. This paper presents a model of the business cycle in which households have imperfect knowledge of long-run growth rate of endogenous variables and continually learn about these growth rates. The model features comovement and mutual influence between households׳ growth expectations and market outcomes. It can replicate the evidence on growth forecasts and suggests that optimism and pessimism about long-run growth rates is a crucial ingredient in understanding business cycle fluctuations.     

An application of a random level shifts model to the volatility of Peruvian stock and exchange rate returns

The literature has shown that the volatility of stock and forex rate market returns shows the characteristic of long memory. Another fact that is shown in the literature is that this feature may be spurious and volatility actually consists of a short memory process contaminated with random level shifts (RLS). In this paper, we follow recent econometric approaches estimating an RLS model to the logarithm of the absolute value of stock and forex returns. The model consists of the sum of a short-term memory component and a component of level shifts. The second component is specified as the cumulative sum of a process that is zero with probability ‘1-alpha’ and is a random variable with probability ‘alpha’. The results show that there are level shifts that are rare, but once they are taken into account, the characteristic or property of long memory disappears. Also, the presence of General Autoregressive Conditional Heteroscedasticity (GARCH) effects is eliminated when included or deducted level shifts. An exercise of out-of-sample forecasting shows that the RLS model has better performance than traditional models for modelling long memory such as the models ARFIMA (p,d,q).     

Risk forecast as work practice: between codified and practical knowledge

If the use of meteorological data has progressively expanded in tackling different sources of risk, less developed is by contrast a reflection on how meteorological systems apply in local contexts and to what extent that locality may affect the use and the content of forecasting recipients. By focusing on a wildfire forecasting, I show how forecasting practice cannot be reduced to the implementation of meteorological devices; it rather takes shape in the articulation between the technical device and different sources of knowledge – tacit, practical and ‘profane’. This articulation work, this study gives account of, reveals some specific challenges in the introduction of forecasting systems in risk management.     

Nowcasting German GDP: Foreign factors,financial markets,and model averaging

This paper develops a nowcasting model for the German economy. The model outperforms a number of alternatives and produces forecasts not only for GDP but also for other key variables. We show that the inclusion of a foreign factor improves the model’s performance, while financial variables do not. Additionally, a comprehensive model averaging exercise reveals that factor extraction in a single model delivers slightly better results than averaging across models. Finally, we estimate a “news” index for the German economy in order to assess the overall performance of the model beyond forecast errors in GDP. The index is constructed as a weighted average of the nowcast errors related to each variable included in the model.     

A Litterman BVAR approach for production forecasting of technology industries

Forecasting the production of technology industries is important to entrepreneurs and governments, but usually suffers from market fluctuation and explosion. This paper aims to propose a Litterman Bayesian vector autoregression (LBVAR) model for production prediction based on the interaction of industrial clusters. Related industries within industrial clusters are included into the LBVAR model to provide more accurate predictions. The LBVAR model possesses the superiority of Bayesian statistics in small sample forecasting and holds the dynamic property of the vector autoregression (VAR) model. Two technology industries in Taiwan, the photonics industry and semiconductor industry are used to examine the LBVAR model using a rolling forecasting procedure. As a result, the LBVAR model was found to be capable of providing outstanding predictions for these two technology industries in comparison to the autoregression (AR) model and VAR model.     

一种基于周期模型的物联网实体实时预测方法

对物联网中实体的海量数据进行分析处理成为了物联网应用层发展的基础,很多应用场合需要对物联网实体状态进行预测。物联网中的实体状态具有强实时性和高动态性,通过判断物联网实体的周期模式采用合理的预测模型,对特定时间段建立时间窗口,预测出实体状态的概率值。该预测方法耗时短,能在2~4 s给出预测结果,且准确度高,能与实际相符合,对物联网应用层的决策分析起到了很好的指示作用。