Comparative performances of measures of consumer and economic sentiment in forecasting consumption: a multi-country analysis

ABSTRACT

This paper seeks to compare the capabilities of assorted measures of consumer and economic sentiment in predicting the growth of household expenditure. An analysis of quarterly data on five European countries shows that for none of these can the model which incorporates the EU’s headline consumer confidence indicator be deemed to be significantly inferior to any of its seven rivals. However, the rankings of the sentiment variables are seen to be influenced by: the proportion of total spending by households that is devoted to durable goods; and the nature of the behaviour of consumption over the forecast interval.     

Patent indicators for monitoring convergence - examples from NFF and ICT

The blurring of boundaries between hitherto distinct scientific disciplines, technologies or markets is a common and powerful phenomenon. Concentrating on monitoring convergence through patent indicators, we discuss convergence with examples from the area of Nutraceuticals and Functional Foods (NFF)/Cosmeceuticals as well as information technologies, consumer electronics, and telecommunications (ICT). We analyze 7455 scientific and patent references on phytosterols with the aid of SciFinder Scholar and 3836 documents employing STN AnaVist. Furthermore, we test an IPC co-classification based approach on 859,469 ICT-related and 341,846 NFF-related patents. Our results show clear indications for convergence and a proof of principle for our monitoring concept. Academics may better evaluate environmental parameters, like cases of convergence, influencing companies' actions. Implications for practitioners are based on a more reliable assessment prior to the forming of strategic alliances or mergers and acquisitions. This could help avoid costly adventures such as the mergers and acquisitions seen in ICT.     

Threshold models for trended time series

This paper presents the theoretical development of a new threshold autoregressive model based on trended time series. The theoretical arguments underlying the model are outlined and a nonlinear economic model is used to derive the specification of the empirical econometric model. Estimation and testing issues are considered and analysed. Additionally we apply the model to the empirical investigation of U.S. GDP.This paper is the result of work carried out for the author's Ph.D. thesis. I would like to thank Hashem Pesaran for his help, encouragement and insights during the preparation of this paper. I also thank Gary Koop and Sean Holly for helpful comments. Financial assistance from the Economic and Social Research Council is gratefully acknowledged.     

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.     

A review of Delphi forecasting research in tourism

The Delphi technique is the most popular judgemental forecasting method in tourism studies, but theoretical and empirical developments in this area (especially for forecasting purposes) have been slow. This paper analyses published research on Delphi forecasting in tourism and hospitality, explores how the Delphi forecasting method has progressed over the past four decades in terms of topical areas, empirical applications, and issues of reliability and validity, and is thus expected to advance understanding of the Delphi technique, providing topical and methodological recommendations for researchers and industry practitioners for producing accurate forecasts. The literature concerning the qualitative and quantitative applications of Delphi forecasting in tourism is mainly divided into three research themes: event forecasting, forecasting tourism demand, and forecasting future trends/market conditions (the most popular application). Issues of accuracy, reliability, and validity, as well as a group of Delphi-specific characteristics, such as panel size, panel selection, consensus measures, and analysis of results, are summarized and discussed. This study also examines the accuracy of Delphi forecasts as well as exploring the role of the Delphi approach in integrating human judgement into quantitative forecasts.     

盈余构成、持续性差异与财务分析师盈余预测——基于我国A股上市公司的经验分析

遵循Sloan的经典研究思路,从分析师对盈余构成持续性差异区分的角度,对我国分析师盈余预测的有效性进行了实证研究。研究发现,我国的财务分析师高估了所有盈余构成的持续性,其发布的盈余预测虽然能够反映出应计利润的低持续性和现金流量的高持续性,但不能恰当地反映出操控性应计利润与非操控性应计利润之间的持续性差异。进一步的研究表明,财务分析师对盈余构成持续性的高估会显著影响其整体盈余预测准确度,这意味着我国财务分析师发布的盈余预测是有一定信息含量的,但在应对复杂会计信息时仍显不足。     

Impact of financial/economic crisis on demand for hotel rooms in Hong Kong

The main objectives of this study are (1) to identify the factors that influence the demand for hotel rooms in Hong Kong and (2) to generate quarterly forecasts of that demand to assess the impact of the ongoing financial/economic crisis. The demand for four types of hotel room from the residents of nine major origin countries is considered, and forecasts are generated from the first quarter of 2009 to the fourth quarter of 2015. Econometric approaches are employed to calculate the demand elasticities and their corresponding confidence intervals, which are then used to generate interval demand predictions. The empirical results reveal that the most important factors in determining the demand for hotel rooms in Hong Kong are the economic conditions (measured by income level) in the origin markets, the price of the hotel rooms and the ‘word of mouth’ effect. Demand for High Tariff A and Medium Tariff hotel rooms is estimated to have experienced negative annual growth in 2009 due to the influence of the financial/economic crisis, whereas that for High Tariff B hotel rooms is thought to have grown in 2009 after having decreased in 2008. The demand for tourist guesthouse rooms is expected to be the least affected by the crisis. Overall demand is predicted to recover gradually from 2010 onwards.     

城市电网负荷预测方法研究

介绍了不同负荷预测方法的适用范围,分析了我国城市电网负荷发展的特点,给出了城市电网负荷预测的思路和重点;在此基础上,提出了城市规划不同阶段和城市不同地区的负荷预测实现方法,进一步研究了提高城市电网负荷预测结果准确性的主要措施。     

Judgmental aggregation strategies depend on whether the self is involved

We report the results of a novel experiment that addresses two unresolved questions in the judgmental forecasting literature. First, how does combining the estimates of others differ from revising one’s own estimate based on the judgment of another? The experiment found that participants often ignored advice when revising an estimate but averaged estimates when combining. This was true despite receiving identical feedback about the accuracy of past judgments. Second, why do people consistently tend to overweight their own opinions at the expense of profitable advice? We compared two prominent explanations for this, differential access to reasons and egocentric beliefs, and found that neither adequately accounts for the overweighting of the self. Finally, echoing past research, we find that averaging opinions is often advantageous, but that choosing a single judge can perform well in certain predictable situations.     

Booking horizon forecasting with dynamic updating: A case study of hotel reservation data

A highly accurate demand forecast is fundamental to the success of every revenue management model. As is often required in both practice and theory, we aim to forecast the accumulated booking curve, as well as the number of reservations expected for each day in the booking horizon. To reduce the dimensionality of this problem, we apply singular value decomposition to the historical booking profiles. The forecast of the remaining part of the booking horizon is dynamically adjusted to the earlier observations using the penalized least squares and historical proportion methods. Our proposed updating procedure considers the correlation and dynamics of bookings both within the booking horizon and between successive product instances. The approach is tested on real hotel reservation data and shows a significant improvement in forecast accuracy.