An integration model for manpower forecasting

Manpower forecasting has made significant contributions to human resource management. Due to difficulties in collecting the required data for making appropriate analysis, most studies in the literature concentrate on forecasts of individual firms. This paper presents a regression model which utilizes the data of large firms to draw inferences to the demands of other firms. More specifically, a regression model showing the negative relationship between the rank of a firm and its associated demand is fitted to the data of a number of large manufacturing firms. The area under the regression line delineated by the y-axis is then an estimate of the total demand of the whole industry. Confidence intervals for the estimate can also be constructed. As an illustration, the demand for the industrial management manpower in Taiwan is forecasted by applying the proposed model.     

Efficient use of information in the formation of subjective industry forecasts

The paper considers the use of information by a panel of expert industry forecasters, focusing on their information-processing biases. The panel forecasts construction output by sector up to three years ahead. It is found that the biases observed in laboratory experiments, particularly ‘anchoring’, are observable. The expectations are formed by adjusting the previous forecast to take new information into account. By analysing forecast errors it is concluded that the panel overweight recently released information and do not understand the dynamics of the industry. However, their forecasts, both short and long term, are better than an alternative econometric model, and combining the two sources of forecasts leads to a deterioration in forecast accuracy. The expert forecasts can be ‘de-biased’, and this leads to the conclusion that it is better to optimally process information sources than to combine (optimally) alternative forecasts.     

Diagnostic tracking and model specification in combined forecasts of U.K. inflation

Combinations of several forecasts are now quite commonly used as inputs into business planning models. For example, capital budgeting generally involves a synthesis of several sources of economic forecasts. In such cases, where uncertainty and risk are also being explicitly considered, the statistical specification of the combined forecasts becomes particularly important. An investigation of the monthly forecasts of annual inflation from nine leading U.K. economic models was undertaken to examine the circumstances under which well-specified and efficient combined forecasts could be produced. This has helped to refine the general guidelines for the practical use of combinations in planning models.     

Forecasting with money demand functions: the UK case

Money demand functions have long been known to be frequently subject to structural change. Since their use for optimal monetary policy design is basically a forecasting exercise, it is crucial to analyse the effect of time instability on the quality of their forecasts. We discuss in this paper whether instability of demand for money functions precludes their use for policy experiments, analysing a 1963–84 sample for the UK which has been widely used in the literature. © 1998 John Wiley & Sons, Ltd.     

Forecasting commercial electricity sales

An important component of the New England Electric System Companies' (the 'System') total electricity sales is attributable to commercial customers. Commercial growth has recently been strong; moreover the System's peak demand is highly sensitive to commercial load. In a typical month this class represents 33 per cent of total System sales. Accurate short-run forecasts of total kWh sales are important for rate making, budgeting, fuel cause proceedings, and corporate planning. In this study we use a variety of econometric and time-series techniques to produce short-run forecasts of commercial sales for two geographical areas served by two separate retail companies;.     

The Role of High‐Frequency Intra‐daily Data,Daily Range and Implied Volatility in Multi‐period Value‐at‐Risk Forecasting

This paper assesses the informational content of alternative realized volatility estimators, daily range and implied volatility in multi‐period out‐of‐sample Value‐at‐Risk (VaR) predictions. We use the recently proposed Realized GARCH model combined with the skewed Student's t distribution for the innovations process and a Monte Carlo simulation approach in order to produce the multi‐period VaR estimates. Our empirical findings, based on the S&P 500 stock index, indicate that almost all realized and implied volatility measures can produce statistically and regulatory precise VaR forecasts across forecasting horizons, with the implied volatility being especially accurate in monthly VaR forecasts. The daily range produces inferior forecasting results in terms of regulatory accuracy and Basel II compliance. However, robust realized volatility measures, which are immune against microstructure noise bias or price jumps, generate superior VaR estimates in terms of capital efficiency, as they minimize the opportunity cost of capital and the Basel II regulatory capital. Copyright © 2013 John Wiley & Sons, Ltd.     

An analysis on the predictability of CAPM beta for momentum returns

This paper demonstrates that the forecasted capital asset pricing model (CAPM) beta of momentum portfolios explains a large portion of the return, ranging from 40% to 60% for stock‐level momentum, and from 30% to 50% for industry‐level momentum. Beta forecasts are from a realized beta estimator using daily returns over the prior year. Periods such as 1969–1989 have been found in earlier studies to contain abnormal profits from momentum trading; however, we show that these were spuriously generated by measurement error in systematic risk. These results cast further doubt on the ability of standard momentum trading strategies to generate abnormal profits.     

Combination of Forecasts across Estimation Windows: An Application to Air Travel Demand

This paper applies combining forecasts of air travel demand generated from the same model but over different estimation windows. The combination approach used resorts to Pesaran and Pick (Journal of Business Economics and Statistics 2011; 29 : 307–318), but the empirical application is extended in several ways. The forecasts are based on a seasonal Box–Jenkins model (SARIMA), which is adequate to forecast monthly air travel demand with distinct seasonal patterns at the largest German airport: Frankfurt am Main. Furthermore, forecasts with forecast horizons from 1 to 12 months ahead, which are based on different average estimation windows, expanding windows and single rolling windows, are compared with baseline forecasts based on an expanding window of the observations after a structural break. The forecast exercise shows that the average window forecasts mostly outperform the alternative single window forecasts. Copyright © 2016 John Wiley & Sons, Ltd.     

The role of age‐structured education data for economic growth forecasts

This paper utilizes for the first time age‐structured human capital data for economic growth forecasting. We concentrate on pooled cross‐country data of 65 countries over six 5‐year periods (1970–2000) and consider specifications chosen by model selection criteria, Bayesian model averaging methodologies based on in‐sample and out‐of‐sample goodness of fit and on adaptive regression by mixing. The results indicate that forecast averaging and exploiting the demographic dimension of education data improve economic growth forecasts systematically. In particular, the results are very promising for improving economic growth predictions in developing countries. Copyright © 2009 John Wiley & Sons, Ltd.     

Forecasting output growth rates and median output growth rates: a hierarchical Bayesian approach

This paper describes procedures for forecasting countries' output growth rates and medians of a set of output growth rates using Hierarchical Bayesian (HB) models. The purpose of this paper is to show how the γ‐shrinkage forecast of Zellner and Hong ( 1989 ) emerges from a hierarchical Bayesian model and to describe how the Gibbs sampler can be used to fit this model to yield possibly improved output growth rate and median output growth rate forecasts. The procedures described in this paper offer two primary methodological contributions to previous work on this topic: (1) the weights associated with widely‐used shrinkage forecasts are determined endogenously, and (2) the posterior predictive density of the future median output growth rate is obtained numerically from which optimal point and interval forecasts are calculated. Using IMF data, we find that the HB median output growth rate forecasts outperform forecasts obtained from variety of benchmark models. Copyright © 2001 John Wiley & Sons, Ltd.     

Modifying quantitative forecasts of livestock production using expert judgments: An application to the australian lamb industry

The judgmental modification of quantitative forecasts has become increasingly adopted in the production of agricultural commodity outlook information. Such modifications allow current period information to be incorporated into the forecast value, and ensure that the forecast is realistic in the context of current industry trends. This paper investigates the potential value of this approach in production forecasting in the Australian lamb industry. Several individual and composite econometric models were used to forecast a lamb-slaughtering series with a selected forecast being given to a panel of lamb industry specialists for consideration and modification. The results demonstrate that this approach offers considerable accuracy advantages in the short-term forecasting of livestock market variables, such as slaughtering, whose values can be strongly influenced by current industry conditions.     

Causal forces: Structuring knowledge for time-series extrapolation

This paper examines a strategy for structuring one type of domain knowledge for use in extrapolation. It does so by representing information about causality and using this domain knowledge to select and combine forecasts. We use five categories to express causal impacts upon trends: growth, decay, supporting, opposing, and regressing. An identification of causal forces aided in the determination of weights for combining extrapolation forecasts. These weights improved average ex ante forecast accuracy when tested on 104 annual economic and demographic time series. Gains in accuracy were greatest when (1) the causal forces were clearly specified and (2) stronger causal effects were expected, as in longer-range forecasts. One rule suggested by this analysis was: ‘Do not extrapolate trends if they are contrary to causal forces.’ We tested this rule by comparing forecasts from a method that implicitly assumes supporting trends (Holt's exponential smoothing) with forecasts from the random walk. Use of the rule improved accuracy for 20 series where the trends were contrary; the MdAPE (Median Absolute Percentage Error) was 18% less for the random walk on 20 one-year ahead forecasts and 40% less for 20 six-year-ahead forecasts. We then applied the rule to four other data sets. Here, the MdAPE for the random walk forecasts was 17% less than Holt's error for 943 short-range forecasts and 43% less for 723 long-range forecasts. Our study suggests that the causal assumptions implicit in traditional extrapolation methods are inappropriate for many applications.     

A monetary real‐time conditional forecast of euro area inflation

Based on a vector error correction model we produce conditional euro area inflation forecasts. We use real‐time data on M3 and HICP, and include real GPD, the 3‐month EURIBOR and the 10‐year government bond yield as control variables. Real money growth and the term spread enter the system as stationary linear combinations. Missing and outlying values are substituted by model‐based estimates using all available data information. In general, the conditional inflation forecasts are consistent with the European Central Bank's assessment of liquidity conditions for future inflation prospects. The evaluation of inflation forecasts under different monetary scenarios reveals the importance of keeping track of money growth rate in particular at the end of 2005. Copyright © 2009 John Wiley & Sons, Ltd.     

International Evidence on GFC‐Robust Forecasts for Risk Management under the Basel Accord

A risk management strategy designed to be robust to the global financial crisis (GFC), in the sense of selecting a value‐at‐risk (VaR) forecast that combines the forecasts of different VaR models, was proposed by McAleer and coworkers in 2010. The robust forecast is based on the median of the point VaR forecasts of a set of conditional volatility models. Such a risk management strategy is robust to the GFC in the sense that, while maintaining the same risk management strategy before, during and after a financial crisis, it will lead to comparatively low daily capital charges and violation penalties for the entire period. This paper presents evidence to support the claim that the median point forecast of VaR is generally GFC robust. We investigate the performance of a variety of single and combined VaR forecasts in terms of daily capital requirements and violation penalties under the Basel II Accord, as well as other criteria. In the empirical analysis we choose several major indexes, namely French CAC, German DAX, US Dow Jones, UK FTSE100, Hong Kong Hang Seng, Spanish Ibex 35, Japanese Nikkei, Swiss SMI and US S&P 500. The GARCH, EGARCH, GJR and RiskMetrics models as well as several other strategies, are used in the comparison. Backtesting is performed on each of these indexes using the Basel II Accord regulations for 2008–10 to examine the performance of the median strategy in terms of the number of violations and daily capital charges, among other criteria. The median is shown to be a profitable and safe strategy for risk management, both in calm and turbulent periods, as it provides a reasonable number of violations and daily capital charges. The median also performs well when both total losses and the asymmetric linear tick loss function are considered Copyright © 2012 John Wiley & Sons, Ltd.     

A test of the joint efficiency of macroeconomic forecasts using multivariate random forests

We contribute to recent research on the joint evaluation of the properties of macroeconomic forecasts in a multivariate setting. The specific property of forecasts that we are interested in is their joint efficiency. We study the joint efficiency of forecasts by means of multivariate random forests, which we use to model the links between forecast errors and predictor variables in a forecaster's information set. We then use permutation tests to study whether the Mahalanobis distance between the predicted forecast errors for the growth and inflation forecasts of four leading German economic research institutes and actual forecast errors is significantly smaller than under the null hypothesis of forecast efficiency. We reject joint efficiency in several cases, but also document heterogeneity across research institutes with regard to the joint efficiency of their forecasts.     

Combination of forecasts in electricity demand prediction

Accurate demand prediction is of great importance in the electricity supply industry. Electricity cannot be stored, and generating plant must be scheduled well in advance to meet future demand. Up to now, where online information about external conditions is unavailable, time series methods on the historical demand series have been used for short-term demand prediction. These have drawbacks, both in their sensitivity to changing weather conditions and in their poor modelling of the daily/weekly business cycles. To overcome these problems a framework has been constructed whereby forecasts from different prediction methods and different forecasting origins can be selected and combined, solely on the basis of recent forecasting performance, with no a priori assumptions of demand behaviour. This added flexibility in univariate forecasting provides a significant improvement in prediction accuracy.     

The importance of time‐varying volatility and country interactions in forecasting economic activity

This paper examines the relative importance of allowing for time‐varying volatility and country interactions in a forecast model of economic activity. Allowing for these issues is done by augmenting autoregressive models of growth with cross‐country weighted averages of growth and the generalized autoregressive conditional heteroskedasticity framework. The forecasts are evaluated using statistical criteria through point and density forecasts, and an economic criterion based on forecasting recessions. The results show that, compared to an autoregressive model, both components improve forecast ability in terms of point and density forecasts, especially one‐period‐ahead forecasts, but that the forecast ability is not stable over time. The random walk model, however, still dominates in terms of forecasting recessions.     

The Role of Financial Variables in predicting economic activity

Previous research found that the US business cycle leads the European one by a few quarters, and can therefore be useful in predicting euro area gross domestic product (GDP). In this paper we investigate whether additional predictive power can be gained by adding selected financial variables belonging to either the USA or the euro area. We use vector autoregressions (VARs) that include the US and euro area GDPs as well as growth in the Rest of the World and selected combinations of financial variables. Out‐of‐sample root mean square forecast errors (RMSEs) evidence that adding financial variables produces a slightly smaller error in forecasting US economic activity. This weak macro‐financial linkage is even weaker in the euro area, where financial indicators do not improve short‐ and medium‐term GDP forecasts even when their timely availability relative to GDP is exploited. It can be conjectured that neither US nor European financial variables help predict euro area GDP as the US GDP has already embodied this information. However, we show that the finding that financial variables have no predictive power for future activity in the euro area relates to the unconditional nature of the RMSE metric. When forecasting ability is assessed as if in real time (i.e. conditionally on the information available at the time when forecasts are made), we find that models using financial variables would have been preferred in several episodes and in particular between 1999 and 2002. Copyright 2011 John Wiley & Sons, Ltd.     

The Euro‐Sting Revisited: The Usefulness of Financial Indicators to Obtain Euro Area GDP Forecasts

This paper uses an extension of the Euro‐Sting single‐index dynamic factor model to construct short‐term forecasts of quarterly GDP growth for the euro area by accounting for financial variables as leading indicators. From a simulated real‐time exercise, the model is used to investigate the forecasting accuracy across the different phases of the business cycle. Our extension is also used to evaluate the relative forecasting ability of the two most reliable business cycle surveys for the euro area: the PMI and the ESI. We show that the latter produces more accurate GDP forecasts than the former. Finally, the proposed model is also characterized by its great ability to capture the European business cycle, as well as the probabilities of expansion and/or contraction periods. Copyright © 2014 John Wiley & Sons, Ltd.     

The Effects of Disaggregation on Forecasting Nonstationary Time Series

This paper focuses on the effects of disaggregation on forecast accuracy for nonstationary time series using dynamic factor models. We compare the forecasts obtained directly from the aggregated series based on its univariate model with the aggregation of the forecasts obtained for each component of the aggregate. Within this framework (first obtain the forecasts for the component series and then aggregate the forecasts), we try two different approaches: (i) generate forecasts from the multivariate dynamic factor model and (ii) generate the forecasts from univariate models for each component of the aggregate. In this regard, we provide analytical conditions for the equality of forecasts. The results are applied to quarterly gross domestic product (GDP) data of several European countries of the euro area and to their aggregated GDP. This will be compared to the prediction obtained directly from modeling and forecasting the aggregate GDP of these European countries. In particular, we would like to check whether long‐run relationships between the levels of the components are useful for improving the forecasting accuracy of the aggregate growth rate. We will make forecasts at the country level and then pool them to obtain the forecast of the aggregate. The empirical analysis suggests that forecasts built by aggregating the country‐specific models are more accurate than forecasts constructed using the aggregated data. Copyright © 2014 John Wiley & Sons, Ltd.