A note on forecasting international tourism demand in Spain

This paper considers the extent to which price and income proxy variables help in forecasting tourist demand in Spain. Contrary to some recent studies, we found that the inputs' contribution in terms of fitting and forecasting is nil when compared with alternative univariate models. Whether these findings are the results of the restrictions embedded in building the proxy inputs or in a poor specification of the dynamics of these models remains to be seen. We also contend that when dealing with medium, long-term forecasting comparisons, the use of the traditional aggregate accuracy measures like RMSE and MAPE help very little in discriminating among competing models. In these situations, predicted annual growth rates may be a better alternative.     

倾斜摄影测量中影响模型精度的因素分析

目前,倾斜摄影测量作为一种新技术,经过不断的探索、应用和发展,已开始在测绘行业中大显身手,并取得了不小的成绩。通过三维模型进行的矢量数据采集,其矢量精度完全依赖于三维模型精度。论文以实践经验为基础,定性地分析了在航空摄影、像控测量、模型生产三阶段中,影响模型精度的因素,希望对三维模型的生产起到一定的指导作用。     

The Delphi method, impeachment and terrorism: Accuracies of short-range forecasts for volatile world events

Two experiments examined the effects of iterated polling and feedback on prediction accuracy for volatile world events using the Delphi paradigm. In both experiments, participants predicted rapidly changing geopolitical events. The first experiment occurred during the litigation surrounding the Clinton/Lewinsky affair in 1998. Results indicated that feedback improved individual and group accuracy for predictions of whether an event would occur. These types of predictions derived from group consensus were approximately 90% accurate, which was significantly higher than the average individual participant's accuracy. Neither polling nor feedback had much effect on time course predictions. The second experiment occurred during the American military action against the Taliban regime in Afghanistan in 2001. In Experiment 2, participants were polled three times to determine if increasing the number of iterated polls would improve Delphi accuracy. The results showed that accuracy improved from the first to the second poll but not from the second to the third. The groups predicted whether these scenarios would occur with approximately 75% accuracy, which was significantly higher than the accuracy of any individual participant. Once again, the Delphi procedure did not enhance time course predictions in Experiment 2.     

The coverage assignments of financial analysts

Previous studies document that forecast accuracy impacts analyst career outcomes. This paper investigates the influence of forecast accuracy on coverage assignments. I show that brokerage houses reward accurate analysts by assigning them to high-profile firms and penalise analysts exhibiting poor accuracy by assigning them to smaller firms. The coverage of high-profile firms increases the potential for future compensation linked to investment banking and trading commissions. In addition, covering such firms increases analysts' recognition from buy-side investors, which, in turn, increases the likelihood of obtaining broker votes and votes for the Institutional Investor star ranking. Overall, my results indicate that high forecast accuracy leads to increased future compensation.     

浅谈选煤厂建设投资估算影响因素及改进措施

本文对选煤厂建设工程投资估算中影响准确性的因素进行了分析,并结合BP神经网络理论基于Matlab软件对选煤厂带式输送机及栈桥单位工程进行了数值模拟,通过对比数值模拟结果与概算指标估算结果,详细阐述了可以提高选煤厂建设工程投资估算准确性的几点建议。     

学术期刊引文不规范问题及对策分析

当今，学术界的引文不规范行为十分严重。针对于此，本文主要概括了引文不规范行为的分类、原因和解决对策，强调引文在科技论文中的重要作用，并从作者自身、期刊社和相关法律制度三个方面解决该问题。     

Modelling property values in Nigeria using artificial neural network

Unreliable and inaccurate property valuation has been associated with techniques currently used in property valuation. A possible explanation for these findings may be due to the utilisation of traditional valuation methods. In the current study, an artificial neural network (ANN) is applied in property valuation using the Lagos metropolis property market as a representative case. Property sales transactions data (11 property attributes and property value) were collected from registered real estate firms operating in Lagos, Nigeria. The result shows that the ANN model possesses a good predictive ability, implying that it is suitable and reliable for property valuation. The relative importance analysis conducted on the property attributes revealed that the number of servants’ quarters is the most important attribute affecting property values. The findings suggest that the ANN model could be used as a tool by real estate stakeholders, especially valuers and researchers for property valuation.     

Cointegration as an explanation for the Meese–Rogoff puzzle

Some economists suggest that the failure of exchange-rate models to outperform the random walk in exchange rate forecasting out of sample can be attributed to failure to take into account cointegration when it is present. We attempt to find out if cointegration matters for forecasting accuracy by examining the relation between the stationarity and size of the forecasting error. Results based on three macroeconomic models of exchange rates do not provide strong support for the proposition that cointegration matters for forecasting accuracy. The simulation results show that while stationary errors tend to be smaller than non-stationary errors, this is not a universal rule. Irrespective of the presence or absence of cointegration, none of the three models can outperform the random walk in out-of-sample forecasting, which means that cointegration cannot solve the Meese–Rogoff puzzle.     

Comparing linear and nonlinear forecasts for stock returns

We compare the out-of-sample performance of monthly returns forecasts for two indices, namely the Dow Jones (DJ) and the Financial Times (FT) indices. A linear and a nonlinear artificial neural network (ANN) model are used to generate the out-of-sample competing forecasts for monthly returns. Stationary transformations of dividends and trading volume are considered as fundamental explanatory variables in the linear model and the input variables in the ANN model. The comparison of out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano test [J. Bus. Econ. Stat. 13 (1995) 253.], and forecast encompassing, using the Clements and Hendry approach [J. Forecast. 5 (1998) 559.]. The results suggest that the out-of-sample ANN forecasts are significantly more accurate than linear forecasts of both indices. Furthermore, the ANN forecasts can explain the forecast errors of the linear model for both indices, while the linear model cannot explain the forecast errors of the ANN in either of the two indices. Overall, the results indicate that the inclusion of nonlinear terms in the relation between stock returns and fundamentals is important in out-of-sample forecasting. This conclusion is consistent with the view that the relation between stock returns and fundamentals is nonlinear.     

THE IMPACT OF POINT‐OF‐SALE DATA INACCURACY AND INVENTORY RECORD DATA ERRORS

The presence or absence of error in point‐of‐sale (POS) data and inventory system records directly affects retailer performance. This study identifies various error sources in retail supply chains and studies the influence of inventory and POS (demand) errors in a simulated retail outlet according to fill rate and average inventory. Other things being equal, we find that inventory record error reduces fill rate more than demand error. This study adds further evidence to other studies that suggest the costs caused by errors in POS systems may be overstated.