Note on discrete a-unimodality

An interpretation is given of ABOUAMMOH'S (1987) discrete analogue of the (one-dimensional) definition of a-unimodality by Olshen and Savage. The concepts are of some interest in the context of selfdecomposability.     

Value-added forecasting

Long ago, the emphasis shifted away from forecasting as a competitive weapon when it became apparent that forecast error could never be eliminated. Forecasts became a necessary evil that no one wanted to claim responsibility for. It's time to clear up some of the misconceptions about forecasts and to seize the opportunity inherent in the forecasting process. It is not forecast accuracy but rather improved understanding and use of forecasting as a tool for reducing both costs and lead times that will add real value to an enterprise and can improve the results from any and all other initiatives.     

Some observations on forecasting and policy

This paper offers some thoughts on the use of macroeconomic and financial forecasts in monetary and fiscal policy. It stresses the role of nowcasting in constructing good forecasts: most of the value added in macreoeconomic forecasts comes from getting a good approximation to the jumping-off point. Some specific applications are discussed: long-range debt/GDP projections and forecasting recessions using asset prices. I also discuss the construction and use of density forecasts.     

Theory-coherent forecasting

We consider a method for producing multivariate density forecasts that satisfy moment restrictions implied by economic theory, such as Euler conditions. The method starts from a base forecast that might not satisfy the theoretical restrictions and forces it to satisfy the moment conditions using exponential tilting. Although exponential tilting has been considered before in a Bayesian context (Robertson et al. 2005), our main contributions are: (1) to adapt the method to a classical inferential context with out-of-sample evaluation objectives and parameter estimation uncertainty; and (2) to formally discuss the conditions under which the method delivers improvements in forecast accuracy. An empirical illustration which incorporates Euler conditions into forecasts produced by Bayesian vector autoregressions shows that the improvements in accuracy can be sizable and significant.     

Portfolio optimization based on GARCH-EVT-Copula forecasting models

This study uses GARCH-EVT-copula and ARMA-GARCH-EVT-copula models to perform out-of-sample forecasts and simulate one-day-ahead returns for ten stock indexes. We construct optimal portfolios based on the global minimum variance (GMV), minimum conditional value-at-risk (Min-CVaR) and certainty equivalence tangency (CET) criteria, and model the dependence structure between stock market returns by employing elliptical (Student-$t$ and Gaussian) and Archimedean (Clayton, Frank and Gumbel) copulas. We analyze the performances of 288 risk modeling portfolio strategies using out-of-sample back-testing. Our main finding is that the CET portfolio, based on ARMA-GARCH-EVT-copula forecasts, outperforms the benchmark portfolio based on historical returns. The regression analyses show that GARCH-EVT forecasting models, which use Gaussian or Student-$t$ copulas, are best at reducing the portfolio risk.     

Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting

The Global Energy Forecasting Competition 2017 (GEFCom2017) attracted more than 300 students and professionals from over 30 countries for solving hierarchical probabilistic load forecasting problems. Of the series of global energy forecasting competitions that have been held, GEFCom2017 is the most challenging one to date: the first one to have a qualifying match, the first one to use hierarchical data with more than two levels, the first one to allow the usage of external data sources, the first one to ask for real-time ex-ante forecasts, and the longest one. This paper introduces the qualifying and final matches of GEFCom2017, summarizes the top-ranked methods, publishes the data used in the competition, and presents several reflections on the competition series and a vision for future energy forecasting competitions.     

Adaptive learning forecasting,with applications in forecasting agricultural prices

We introduce a new forecasting methodology, referred to as adaptive learning forecasting, that allows for both forecast averaging and forecast error learning. We analyze its theoretical properties and demonstrate that it provides a priori MSE improvements under certain conditions. The learning rate based on past forecast errors is shown to be non-linear. This methodology is of wide applicability and can provide MSE improvements even for the simplest benchmark models. We illustrate the method’s application using data on agricultural prices for several agricultural products, as well as on real GDP growth for several of the corresponding countries. The time series of agricultural prices are short and show an irregular cyclicality that can be linked to economic performance and productivity, and we consider a variety of forecasting models, both univariate and bivariate, that are linked to output and productivity. Our results support both the efficacy of the new method and the forecastability of agricultural prices.     

Focus forecasting reconsidered

Focus Forecasting is a popular heuristic methodology for production and inventory control although there has never been a rigorous test of accuracy using real time series. We compare Focus Forecasting to damped-trend, seasonal exponential smoothing using five time series of cookware demand in a production planning application. We also make comparisons using 91 time series from the M-Competition study of forecast accuracy. Exponential smoothing was more accurate in both cases.     

Stochastic demographic forecasting

"This paper describes a particular approach to stochastic population forecasting, which is implemented for the U.S.A. through 2065. Statistical time series methods are combined with demographic models to produce plausible long run forecasts of vital rates, with probability distributions. The resulting mortality forecasts imply gains in future life expectancy that are roughly twice as large as those forecast by the Office of the Social Security Actuary....Resulting stochastic forecasts of the elderly population, elderly dependency ratios, and payroll tax rates for health, education and pensions are presented."     

A Note on Student Academic Performance

ABSTRACT This study compares the educational performance of both highly rural and highly urban area students to students from areas of moderate population density. The results suggest (1) that students from both highly rural and highly urban areas perform similarly, but less well, in terms of educational achievement than students from moderate areas, and (2) that empirical studies of student educational performance should include measures of both cognitive skills and educational market competition as explanatory variables. The policy implications of this research include our recommendation that policymakers consider students from highly urban areas to be subjects of concern similar to students from highly rural areas in attempts to affect expected student achievement. Implications also include our recommendation that policymakers recognize that competitive educational market systems that allow educational consumers greater choice in the acquisition of educational services are those systems within which student academic achievement is higher.     

Approximate Bayesian forecasting

Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of challenging statistical problems, most notably those characterized by an intractable likelihood function. In this paper, we focus on the use of ABC not as a tool for parametric inference, but as a means of generating probabilistic forecasts; or for conducting what we refer to as ‘approximate Bayesian forecasting’. The four key issues explored are: (i) the link between the theoretical behavior of the ABC posterior and that of the ABC-based predictive; (ii) the use of proper scoring rules to measure the (potential) loss of forecast accuracy when using an approximate rather than an exact predictive; (iii) the performance of approximate Bayesian forecasting in state space models; and (iv) the use of forecasting criteria to inform the selection of ABC summaries in empirical settings. The primary finding of the paper is that ABC can provide a computationally efficient means of generating probabilistic forecasts that are nearly identical to those produced by the exact predictive, and in a fraction of the time required to produce predictions via an exact method.