On eliciting beliefs in strategic games

Several recent studies in experimental economics have tried to measure beliefs of subjects engaged in strategic games with other subjects. Using data from one such study we conduct an experiment where our experienced subjects observe early rounds of strategy choices from that study and are given monetary incentives to report forecasts of choices in later rounds. We elicit beliefs using three different scoring rules: linear, logarithmic, and quadratic. We compare forecasts across the scoring rules and compare the forecasts of our trained observers to forecasts of the actual players in the original experiment. We find significant differences across scoring rules. The improper linear scoring rule produces forecasts closer to 0 and 1 than the proper rules, and these forecasts are poorly calibrated. The two proper scoring rules induce significantly different distributions of forecasts. We find that forecasts by observers under both proper scoring rules are significantly different from the forecasts of the actual players, in terms of accuracy, calibration, and the distribution of forecasts. We also find evidence for belief convergence among the observers.     

Assimilation of aggregated inputs into Delphi forecasts: A regression analysis

To examine the manner in which the individual assessments of a panel of delphi experts are combined into a delphi forecast, the supporting reasons they gave for their forecasts of 40 computer applications were coded into categories of assessed “technical feasibility,” “cost of initiating,” and “benefits or needs provided.” Even though different sets of experts provided these statements in support of their individual forecasts, with some experts emphasizing one aspect and others another, it was found that the median forecast of the entire panel was significantly related to the average proportion of reasons in each category which favored bringing about the development. That is, the delphi forecasts of computer applications suggest that the computer application is forecasted to occur sooner to the extent it was judged to be technically feasible, beneficial to users or society, and not costly to develop. The results indicate further that delphi forecasting among a group of experts has logical validity, and that individual contributions are integrated into a group outcome.     

Forecast content and content horizons for some important macroeconomic time series

Abstract.  The information content of statistical forecasts of approximately stationary quantities tends to decline as the forecast horizon increases, and there exists a maximum horizon beyond which forecasts cannot provide discernibly more information about the variable than is present in the unconditional mean (the content horizon ). The pattern of decay of forecast content (or skill) with increasing horizon is well known for many types of meteorological forecasts; by contrast, little generally accepted information about these patterns or content horizons is available for economic variables. In this paper we estimate content horizons for a variety of macroeconomic quantities; more generally, we characterize the pattern of decay of forecast content as we project farther into the future. We find a wide variety of results for the different macroeconomic quantities, with models for some quantities providing useful content several years into the future, for other quantities providing negligible content beyond one or two months or quarters.     

Rationality tests in the presence of instabilities in finite samples

The importance of expectations in modern macroeconomic models and in particular of policy makers expectations for forward looking policy rules has generated a lot of interest in time series of professional forecasts (including central bank staff forecasts). This has spawned a large literature on the evaluation of forecasts that are not model based or where the model is unknown. Although, the available time series of historical forecasts are typically short, this literature has so far mostly disregarded the small sample properties of the proposed tests and estimators. In this paper we fill this gap in the literature, focusing on a set of recently proposed rationality tests for unstable environments. Using a Monte Carlo study we demonstrate that the asymptotic tests are substantially oversized in finite samples including any sample size that is practically available. We provide finite sample adjusted critical values, that allow those tests to be properly applied to sample sizes of typically available forecasts such as the Survey of Professional Forecasters, the Federal Open Market Committee. The critical values we provide will help to avoid false rejections using those data.     

Predictive ability of asymmetric volatility models at medium-term horizons

Using realized volatility to estimate conditional variance of financial returns, we compare forecasts of volatility from linear GARCH models with asymmetric ones. We consider horizons extending to 30 days. Forecasts are compared using three different evaluation tests. With data from an equity index and two foreign exchange returns, we show that asymmetric models provide statistically significant forecast improvements upon the GARCH model for two of the datasets and improve forecasts for all datasets by means of forecasts combinations. These results extend to about 10 days in the future, beyond which the forecasts are statistically inseparable from each other.     

"好公民"还是"好演员":企业社会责任行为异象研究——基于企业业绩预告视角

履行社会责任的公司究竟是真心实意的"好公民"还是表里不一的"好演员"?文章基于业绩预告视角,研究了企业社会责任声誉与行为背离的异象.研究发现,企业履行社会责任越佳,发布业绩预告的概率越低,初步表明我国很可能存在企业社会责任背离的"好演员".进一步的实证表明,在发布业绩预告的企业中,企业社会责任评分越高,业绩预告的准确性和精确性也越高.这表明发布业绩预告且履行社会责任好的企业是真心实意的"好公民".文章的研究表明,业绩预告行为有助于利益相关者甄别履行社会责任好的企业是"好公民"还是"好演员",也有利于投资者做出投资决策和缓解市场信息不对称.     

Growth and inflation forecasts for Germany a panel-based assessment of accuracy and efficiency

We analyse forecasts of professional forecasters for Germany regarding the time span from 1970 to 2004. This novel panel data set renders it possible to assess the accuracy and efficiency of growth and inflation forecasts more efficiently than in previous studies. We argue that the forecasts are, on average, unbiased and weakly—but not strongly—efficient. Using model confidence sets suggested by Hansen et al. (2004), we find that, besides the effect of diverging forecasting dates, no other substantial differences in forecasting quality among forecasters exist. Nevertheless, on the basis of a direction-of-change analysis we argue that it is not always advisable to listen to the majority of forecasters.

Desirability bias in foresight: Consequences for decision quality based on Delphi results

In foresight activities uncertainty is high and decision makers frequently have to rely on human judgment. Human judgment, however, is subject to numerous cognitive biases. In this paper, we study the effects of the desirability bias in foresight. We analyze data from six Delphi studies and observe that participants systematically estimate the probability of occurrence for desirable (undesirable) future projections higher (lower) than the probability for projections with neutral desirability. We also demonstrate that in the course of a multi-round Delphi process, this bias decreases but is not necessarily eliminated. Arguably, the quality of decisions based on Delphi results may be adversely affected if experts share a pronounced and common desirability for a future projection. Researchers and decision makers have to be aware of the existence and potential consequences of such a desirability bias in Delphi studies when interpreting their results and taking decisions. We propose a post-hoc procedure to identify and quantify the extent to which the desirability bias affects Delphi results. The results of this post-hoc procedure complement traditional Delphi results; they provide researchers and decision makers with information on when and to which extent results of Delphi-based foresight may be biased.     

Analysis of human needs as a basis for research policy

The authors postulate the necessity of establishing a coherent system of socio-economic goals and say that the analysis of human needs and studies of the dynamics of their satisfaction can become the means for goal-setting. They refer to the psychological theory of human needs [1, 2] enabling the decomposition of goals and quantification of requirements concerning the satisfaction of particular categories of needs. The model of needs is discussed and its usefulness for forecasting of the tasks of research and technology is shown. The program design based on the concept of the model should include such elements as diagnosis of the current state of needs satisfaction, diagnosis of the current state of identified desires, assessment of areas of needs dissatisfaction expected in the future, forecasts of the development of the system of needs, analysis, and evaluation of the present and future opportunities, and forecasts of barriers to growth. Decomposition of goals and dynamic analysis of needs satisfaction allow one to make a hierarchy of research projects from the point of view of their social importance.     

Forecasting exchange rates using panel model and model averaging

We propose to produce accurate point and interval forecasts of exchange rates by combining a number of well known fundamental based panel models. Combination of each model utilizes a set of weights computed using a linear mixture of experts's framework, where weights are determined by log scores assigned to each model's predictive performance. As well as model uncertainty, we take potential structural break in the parameters of the models into consideration. In our application, to quarterly data for ten currencies (including the Euro) for the period 1990q1–2008q4, we show that the forecasts from ensemble models produce mean and interval forecasts that outperform equal weight, and to a lesser extent random walk benchmark models. The gain from combining forecasts is particularly pronounced for longer-horizon forecasts for central forecasts, but much less so for interval forecasts. Calculations of the probability of the exchange rate rising or falling using the combined or ensemble model show a good correspondence with known events and potentially provide a useful measure for uncertainty of whether the exchange rate is likely to rise or fall.     

Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs

We investigate the ability of small- and medium-scale Bayesian VARs (BVARs) to produce accurate macroeconomic (output and inflation) and credit (loans and lending rate) out-of-sample forecasts during the latest Greek crisis. We implement recently proposed Bayesian shrinkage techniques based on Bayesian hierarchical modeling, and we evaluate the information content of forty-two (42) monthly macroeconomic and financial variables in terms of point and density forecasting. Alternative competing models employed in the study include Bayesian autoregressions (BARs) and time-varying parameter VARs with stochastic volatility, among others. The empirical results reveal that, overall, medium-scale BVARs enriched with economy-wide variables can considerably and consistently improve short-term inflation forecasts. The information content of financial variables, on the other hand, proves to be beneficial for the lending rate density forecasts across forecasting horizons. Both of the above-mentioned results are robust to alternative specification choices, while for the rest of the variables smaller-scale BVARs, or even univariate BARs, produce superior forecasts. Finally, we find that the popular, data-driven, shrinkage methods produce, on average, inferior forecasts compared to the theoretically grounded method considered here.     

Evaluating strange forecasts: The curious case of football match scorelines

This study analyses point forecasts of exact scoreline outcomes for football matches in the English Premier League. These forecasts were made for distinct competitions and originally judged differently. We compare these with implied probability forecasts using bookmaker odds and a crowd of tipsters, as well as point and probability forecasts generated from a statistical model. From evaluating these sources and types of forecast, using various methods, we argue that regression encompassing is the most appropriate way to compare point and probability forecasts, and find that both these types of forecasts for football match scorelines generally add information to one another.     

Ecological sustainability in policy assessments: A wide-angle view and a close watch

We give a perspective from two practitioners on some of the challenges of addressing sustainability concerns in environmental policy assessments. We focus on the ecological dimension of sustainability, which is closely related to the concept of “ecosystem resilience.” First, we discuss several recent benefit-cost analyses conducted by EPA that illustrate many of the practical difficulties analysts have faced when attempting to assess the ecological benefits of proposed regulations. Next, we discuss the importance of increased coordination of policy assessments among offices and agencies that traditionally operate more or less independently. We conclude by using a stylized model to illustrate how using an “adaptive management” approach to designing and evaluating policies can help to avoid some of the limitations of standard policy assessments highlighted in this special section of Ecological Economics and elsewhere.     

A two factor model to combine US inflation forecasts

The combination of individual forecasts is often a useful tool to improve forecast accuracy. The most commonly used technique for forecast combination is the mean, and it has frequently proved hard to surpass. This study considers factor analysis to combine US inflation forecasts showing that just one factor is not enough to beat the mean and that the second one is necessary. The first factor is usually a weighted mean of the variables and it can be interpreted as a consensus forecast, while the second factor generally provides the differences among the variables and, since the observations are forecasts, it may be related with the dispersion in forecasting expectations and, in a sense, with its uncertainty. Within this approach, the study also revisits Friedman's hypothesis relating the level of inflation with expectations uncertainty at the beginning of the twenty-first century.     

微博、信息披露与分析师盈余预测

信息技术和社交网络的发展改变了信息的数量、类型及其传播方式。作为金融市场上最专业的信息使用者,分析师无疑会受到这一变化的影响。文章研究了上市公司开通微博对分析师盈余预测的影响,结果发现：（1）开通微博后,分析师盈余预测的修正频率增加,说明分析师会使用微博信息及时更新盈余预测。（2）开通微博后,分析师的平均盈余预测偏差和盈余预测分歧度都显著下降,说明微博信息是分析师进行预测的重要信息源,有助于其更好地了解和分析公司的经营活动。（3）开通微博后,公司股价对分析师盈余预测修正的反应更大。一个合理的解释是,投资者对微博发布的信息反应不足,而分析师能够帮助理解这些信息。文章的研究结论对于监管部门制定基于微博的社交网络信息披露标准,以及分析师如何在资本市场信息定价效率方面最大程度地发挥作用具有参考意义。     

Understanding forecast failure of ESTAR models of real exchange rates

The forecast performance of the empirical ESTAR model of Taylor et al. (2001) is examined for 4 bilateral real exchange rate series over an out-of-sample evaluation period of nearly 12?years. Point as well as density forecasts are constructed, considering forecast horizons of 1 to 22 steps head. The study finds that no forecast gains over a simple AR(1) specification exist at any of the forecast horizons that are considered, regardless of whether point or density forecasts are utilised in the evaluation. Non-parametric methods are used in conjunction with simulation techniques to learn about the models and their forecasts. It is shown graphically that the nonlinearity in the conditional means (or point forecasts) of the ESTAR model decreases as the forecast horizon increases. The non-parametric methods show also that the multiple steps ahead forecast densities are normal looking with no signs of bi-modality, skewness or kurtosis.     

Multi-horizon inflation forecasts using disaggregated data

In this paper we use multi-horizon evaluation techniques to produce monthly inflation forecasts for up to twelve months ahead. The forecasts are based on individual seasonal time series models that consider both, deterministic and stochastic seasonality, and on disaggregated Consumer Price Index (CPI) data. After selecting the best forecasting model for each index, we compare the individual forecasts to forecasts produced using two methods that aggregate hierarchical time series, the bottom-up method and an optimal combination approach. Applying these techniques to 16 indices of the Mexican CPI, we find that the best forecasts for headline inflation are able to compete with those taken from surveys of experts.     

Forecasting performance of private sector’s unemployment forecasts in advanced economies

This paper provides a full characterization of unemployment rate forecasts using the mean values from Consensus Economics for a sample of nine advanced economies between 1989 and 2012. It also assesses the performance of unemployment rate forecasts around business cycles’ turning points. We find evidence for biasedness, inefficiency or information rigidities and lack of accuracy of unemployment rate forecasts and the distribution of projection errors appears to be slightly twisted to over-prediction (which decreases during recession episodes). Additionally, there is a sense of ‘pessimism’ among forecasters during recovery periods.     

Real-time forecasts of economic activity for Latin American economies

Macroeconomic policy decisions in real-time are based on the assessment of current and future economic conditions. Crucially, these assessments are made difficult by the presence of incomplete and noisy data. The problem is more acute for emerging market economies, where most economic data are released infrequently with a (sometimes substantial) lag. This paper evaluates nowcasts and forecasts of real GDP growth using five models for ten Latin American countries. The results indicate the flow of monthly data helps to improve forecast accuracy, and the dynamic factor model consistently produces more accurate nowcasts and forecasts relative to other model specifications, across most of the countries we consider.     

Anticipating business-cycle turning points in real time using density forecasts from a VAR

For the timely detection of business-cycle turning points we suggest to use medium-sized linear systems (subset VARs with automated zero restrictions) to forecast monthly industrial production index publications one to several steps ahead, and to derive the probability of the turning point from the bootstrapped forecast density as the probability mass below (or above) a suitable threshold value. We show how this approach can be used in real time in the presence of data publication lags and how it can capture the part of the data revision process that is systematic. Out-of-sample evaluation exercises show that the method is competitive especially in the case of the US, while turning-point forecasts are in general more difficult in Germany.