On economic evaluation of directional forecasts

It is commonly accepted that information is helpful if it can be exploited to improve a decision making process. In economics, decisions are often based on forecasts of the upward or downward movements of the variable of interest. We point out that directional forecasts can provide a useful framework for assessing the economic forecast value when loss functions (or success measures) are properly formulated to account for the realized signs and realized magnitudes of directional movements. We discuss a general approach to (directional) forecast evaluation which is based on the loss function proposed by Granger, Pesaran and Skouras. It is simple to implement and provides an economically interpretable loss/success functional framework. We show that, in addition, this loss function is more robust to outlying forecasts than traditional loss functions. As such, the measure of the directional forecast value is a readily available complement to the commonly used squared error loss criterion.     

Combination of long term and short term forecasts, with application to tourism demand forecasting

Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time aggregations. For example, we could create a yearly time series from a monthly time series and produce forecasts for both, then combine the forecasts. These forecasts would each be tracking the dynamics of different time scales, and would therefore add diverse types of information. A comparison of several forecast combination methods, performed in the context of this setup, shows that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined.As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we consider 33 individual source countries, as well as the aggregate. The novel combination strategy also produces a generally improved forecasting accuracy.     

Diagnostics for evaluating the value and rationality of economic forecasts

A number of studies have sought to determine whether economic forecasts had predictive value. These analyses used a single statistical methodology based on the independence of the actual and predicted changes. This paper questions whether the observed results are robust if alternative statistical methodologies are used to analyze this question. Procedures suggested by Cumby and Modest as well as rationality tests were applied to two data sets. Sometimes the conclusions differ depending on the procedures that are used. The results yield a guideline for the diagnostics that should be employed in testing for the value of economic forecasts.     

Open economy forecasting with a DSGE-VAR: Head to head with the RBNZ published forecasts

We construct a DSGE-VAR model for competing head to head with the long history of published forecasts of the Reserve Bank of New Zealand. We also construct a Bayesian VAR model with a Minnesota prior for forecast comparison. The DSGE-VAR model combines a structural DSGE model with a statistical VAR model based on the in-sample fit over the majority of New Zealand’s inflation-targeting period. We evaluate the real-time out-of-sample forecasting performance of the DSGE-VAR model, and show that the forecasts from the DSGE-VAR are competitive with the Reserve Bank of New Zealand’s published, judgmentally-adjusted forecasts. The Bayesian VAR model with a Minnesota prior also provides a competitive forecasting performance, and generally, with a few exceptions, out-performs both the DSGE-VAR and the Reserve Bank’s own forecasts.     

Testing the value of directional forecasts in the presence of serial correlation

Common approaches to testing the economic value of directional forecasts are based on the classical χ²${\chi }^2$-test for independence, Fisher’s exact test or the Pesaran and Timmermann test for market timing. These tests are asymptotically valid for serially independent observations, but in the presence of serial correlation they are markedly oversized, as has been confirmed in a simulation study. We therefore summarize robust test procedures for serial correlation and propose a bootstrap approach, the relative merits of which we illustrate by means of a Monte Carlo study. Our evaluations of directional predictions of stock returns and changes in Euribor rates demonstrate the importance of accounting for serial correlation in economic time series when making such predictions.     

Statistical efficiency in the linear combination of forecasts

In seeking an efficient combination of forecasts which minimises the forecast error variance, many methods have been suggested. Through analysis, simulation and case studies, this paper seeks to develop insights into the statistical circumstances which influence the relative accuracy of six of these methods. The six methods chosen have all been advocated in various publications and consist of ‘equal weighting’ (i.e., pooled average), ‘optimal’ (i.e., error variance minimising), ‘optimal with independence assumption’ (i.e., error variance minimising assuming zero correlation between individual forecast errors) and three variations on the formulation of a Bayesian combination based upon posterior probabilities. The statistical circumstances reflected varying conditions of relative forecast errors, error correlations and outliers.     

Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7

In this paper, we use survey data to analyze the accuracy, unbiasedness and efficiency of professional macroeconomic forecasts. We analyze a large panel of individual forecasts that has not previously been analyzed in the literature. We provide evidence on the properties of forecasts for all G7-countries and for four different macroeconomic variables. Our results show a high degree of dispersion of forecast accuracy across forecasters. We also find that there are large differences in the performances of forecasters, not only across countries but also across different macroeconomic variables. In general, the forecasts tend to be biased in situations where the forecasters have to learn about large structural shocks or gradual changes in the trend of a variable. Furthermore, while a sizable fraction of forecasters seem to smooth their GDP forecasts significantly, this does not apply to forecasts made for other macroeconomic variables.     

企业生态经济效率与可持续发展

认为可持续发展是当今社会发展的主题,生态经济是现代乃至未来经济发展的方向;随着经济发展由工业时代向环境时代、由经济效率向生态效率的转变,将生态环境因素纳入企业管理决策中显得愈发迫切,在业绩评价中融入生态经济效率概念,对于企业可持续发展有着重要的现实意义。从业绩评价的角度对生态经济效率的相关概念、内涵、特点及作用进行了诠释和解读,指出生态经济效率融合了环境业绩与经济业绩,要求企业以较少的生态环境影响实现较大的经济效益,从而实现可持续发展的战略目标及创造长期的价值。     

Predicting a recovery date from the economic crisis of 2008

Predicting a recovery from a crisis is always difficult, but it is particularly so with the 2008 crisis in the United States. How could a small segment of the financial markets known as subprime credit bring down the world’s largest economy into the worst recession since WWII? The resulting conflicts in policy responses are so severe that the short-term objective (recovery) clashes with the longer-term and more structural goals (governance, regulations, technology). This and the enormous uncertainties caused by it add to the difficulties to predict the pace of recovery. While the economic turnaround depends on consumers’ decision to spend and business’ decision to invest and hire, in an uncertain situation such decisions can only be taken as a result of market players’ perceptions of opportunity that depend on their emotional state and confidence. When the latter produces spontaneous urge to action (‘animal spirits’), the recovery process accelerates. Thus, the appropriate model to predict recovery should be able to incorporate such perceptions factors. By identifying and prioritizing economic and policy factors, it is shown how such a model, the Analytic Network Process (ANP), can be used to make the prediction of the recovery time of the US economy. The forecast was made during Spring 2009 by the author working with participants in a seminar of “Economics of Financial Crisis” at Cornell University. We used an expert judgment approach within the framework of a decision theory model, based on the ANP structure that captures the interplay between financial market, housing sector, and market confidence, all of which are influenced by a range of policies. It is estimated that a real sustainable recovery will begin around late July or early August 2010. While a quicker recovery is possible given the enormous size of fiscal stimulus, monetary injection and unprecedented measures of qualitative easing, it is our conjecture that the temporary nature of all these measures will make such a quick turn-around unsustainable (a double-dip recession). When sensitivity analysis was performed, it was found that altering the priorities of the policies, and their interactions with the aggregate demand components, would not significantly change the estimated time to recovery. This stability of the prediction is due to the overriding importance of restoring confidence, making the other factors less important.     

Predicting the geo-temporal variations of crime and disorder

Traditional police boundaries—precincts, patrol districts, etc.—often fail to reflect the true distribution of criminal activity and thus do little to assist in the optimal allocation of police resources. This paper introduces methods for crime incident forecasting by focusing upon geographical areas of concern that transcend traditional policing boundaries. The computerised procedure utilises a geographical crime incidence-scanning algorithm to identify clusters with relatively high levels of crime (hot spots). These clusters provide sufficient data for training artificial neural networks (ANNs) capable of modelling trends within them. The approach to ANN specification and estimation is enhanced by application of a novel and noteworthy approach, the Gamma test (GT).     

Space-varying Coefficient Simultaneous Autoregressive Models for the Structural Analysis of Residential Water Demand

Abstract

This study develops two space-varying coefficient simultaneous autoregressive (SVC-SAR) models for areal data and applies them to the discrete/continuous choice model, which is an econometric model based on the consumer's utility maximization problem. The space-varying coefficient model is a statistical model in which the coefficients vary depending on their location. This study introduces the simultaneous autoregressive model for the underlying spatial dependence across coefficients, where the coefficients for one observation are affected by the sum of those for the other observations. This model is named the SVC-SAR model. Because of its flexibility, we use the Bayesian approach and construct its estimation method based on the Markov chain Monte Carlo simulation. The proposed models are applied to estimate the Japanese residential water demand function, which is an example of the discrete/continuous choice model.     

Forecasting performance of multivariate time series models with full and reduced rank: an empirical examination

Since Quenouille's influential work on multiple time series, much progress has been made towards the goal of parameter reduction and model fit. Relatively less attention has been paid to the systematic evaluation of out-of-sample forecast performance of multivariate time series models. In this paper, we update the hog data set studied by Quenouille (and other researchers who followed him). We re-estimate his model with extended observations (1867–1966), and generate recursive one- to four-steps-ahead forecasts for the period of 1967 through 2000. These forecasts are compared to forecasts from an unrestricted vector autoregression, a reduced rank regression model, an index model and a cointegration-based error correction model. The error correction model that takes into account both nonstationarity of the data and rank reduction performs best at all four forecasting horizons. However, differences among competing models are statistically insignificant in most cases. No model consistently encompasses the others at all four horizons.     

小波神经网络对沪深A300的分析和预测

针对金融时间序列非平稳性、非线性的特点,本文采用小波分析与人工神经网络相结合的方法,对沪深A300收盘价进行分析和预测。结果表明,小波神经网络有较强的预测能力,能达到预期效果。为了验证该方法的预测能力,进一步将时间序列数据多步分段,全方位地进行预测,并与小波-ARIMA模型、BP神经网络预测方法进行比较,体现了小波神经网络的预测优势。     

Combining predictive distributions for the statistical post-processing of ensemble forecasts

Statistical post-processing techniques are now used widely for correcting systematic biases and errors in the calibration of ensemble forecasts obtained from multiple runs of numerical weather prediction models. A standard approach is the ensemble model output statistics (EMOS) method, which results in a predictive distribution that is given by a single parametric law, with parameters that depend on the ensemble members. This article assesses the merits of combining multiple EMOS models based on different parametric families. In four case studies with wind speed and precipitation forecasts from two ensemble prediction systems, we investigate the performances of state of the art forecast combination methods and propose a computationally efficient approach for determining linear pool combination weights. We study the performance of forecast combination compared to that of the theoretically superior but cumbersome estimation of a full mixture model, and assess which degree of flexibility of the forecast combination approach yields the best practical results for post-processing applications.     

铁路多经物流的规模经济研究及实例分析

随着物流被列为十大振兴产业革命之一,目前,我国铁路多经物流运输企业正面临着一个十分良好的发展机遇,近年来,不少企业通过各种方式扩大自己的生产经营规模,以取得更好的经营效果。通过运用多元线性回归模型来分析企业的规模经济,分别从理论和实例两个方面相结合进行研究,论证铁路多经物流存在着局部和总体的规模经济,结合此多元线性回归模型分析铁路多经物流由于规模扩大而出现经济的原因。     

A comparison of two model averaging techniques with an application to growth empirics

Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. This paper compares the performance of various model averaging techniques. In particular, it contrasts Bayesian model averaging (BMA) — currently one of the standard methods used in growth empirics — with a new method called weighted-average least squares (WALS). The new method has two major advantages over BMA: its computational burden is trivial and it is based on a transparent definition of prior ignorance. The theory is applied to and sheds new light on growth empirics where a high degree of model uncertainty is typically present.     

预测和解释人们对新事物接受的扩展模型研究

通过对理论文献的综述,分析和解释了人们对新技术的接受的基本过程。然而由于人们所处的环境不同,文化的不同等因素,技术接受模型在解释人们对新技术的采纳存在一定的局限。文中通过分析研究提出新的变量引入技术接受模型中,提升了模型对态度、意愿、行为的解释力,丰富了模型的应用,并提出了模型的在移动通信服务业中应用,对移动行业如何开展增值服务有一定的指导意义。     

The global economic costs of the need to treat polluted water

We estimate the global costs and other implications of the need to treat wastewater before it can be re-used. We extend the World Trade Model by creating water treatment sectors and provide alternative sources of water for satisfying users’ quantity and quality requirements. The database distinguishes qualities and quantities of water endowments, sectoral water requirements, and wastewater discharges. We estimate that global water treatment costs could be reduced by several trillion dollars if water endowments were maintained at higher quality than currently is the case. Under scenarios where water quality degrades further, the treatment costs more than double even without taking account of likely increases in quality requirements. This modeling framework provides a starting point not only for more detailed empirical investigations of water management strategies, but also for examining prospects and associated costs for recovering other resources, such as metals, which can be reused multiple times.     

A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model

Computationally efficient methods for Bayesian analysis of seemingly unrelated regression (SUR) models are described and applied that involve the use of a direct Monte Carlo (DMC) approach to calculate Bayesian estimation and prediction results using diffuse or informative priors. This DMC approach is employed to compute Bayesian marginal posterior densities, moments, intervals and other quantities, using data simulated from known models and also using data from an empirical example involving firms’ sales. The results obtained by the DMC approach are compared to those yielded by the use of a Markov Chain Monte Carlo (MCMC) approach. It is concluded from these comparisons that the DMC approach is worthwhile and applicable to many SUR and other problems.     

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model’s outperformance of another.