Decision making and planning under low levels of predictability

This special section aims to demonstrate the limited predictability and high level of uncertainty in practically all important areas of our lives, and the implications of this. It summarizes the huge body of solid empirical evidence accumulated over the past several decades that proves the disastrous consequences of inaccurate forecasts in areas ranging from the economy and business to floods and medicine. The big problem is, however, that the great majority of people, decision and policy makers alike, still believe not only that accurate forecasting is possible, but also that uncertainty can be reliably assessed. Reality, however, shows otherwise, as this special section proves. This paper discusses forecasting accuracy and uncertainty, and distinguishes three distinct types of predictions: those relying on patterns for forecasting, those utilizing relationships as their basis, and those for which human judgment is the major determinant of the forecast. In addition, the major problems and challenges facing forecasters and the reasons why uncertainty cannot be assessed reliably are discussed using four large data sets. There is also a summary of the eleven papers included in this special section, as well as some concluding remarks emphasizing the need to be rational and realistic about our expectations and avoid the common delusions related to forecasting.     

Sensitivity of US equity returns to economic policy uncertainty and investor sentiments

This paper examines the sensitivity of major US sectoral returns to economic policy uncertainty and investor sentiments. Our analysis is based on weekly frequency and ranges from January 1995 to December 2015 covering a span of 20 years. Considering existing, however limited evidence of non-linear structure exhibited by investor sentiments and economic policy uncertainty and on the basis of our non-linear diagnostics, we use novel technique of non-parametric causality in quantiles approach proposed by Balcilar, Gupta, and Pierdzioch (2016). Our results highlight that economic policy uncertainty and investor sentiments act as driving factors for US sectoral returns. The nature of relationship is reported as asymmetrical for stock returns and symmetrical for variance of returns with an exception of Healthcare sector for economic policy uncertainty and bullish market sentiments. Our study carries implications for portfolio diversification and policy makers for forecasting market efficiency and economic trends.     

Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility

This paper constructs an aligned global economic policy uncertainty (GEPU) index based on a modified machine learning approach. We find that the aligned GEPU index is an informative predictor for forecasting crude oil market volatility both in- and out-of-sample. Compared to general GEPU indices without supervised learning, well-recognized economic variables, and other popular uncertainty indicators, the aligned GEPU index is rather powerful and can provide preponderant or complementary information. The trading strategy based on the aligned GEPU index can also generate sizable economic gains. The statistical source of the aligned GEPU index’s predictive power is that it can learn both the magnitude and sign of national EPU variables’ predictive ability and thus yields reasonable and informative loadings. On the other hand, the economic driving force probably stems from the ability for forecasting the shocks of oil-related fundamentals.     

Exploiting linear partial information for optimal use of forecasts: With an application to U.S. economic policy

Traditionally, the link between forecasting and decision making rests on the assumption of a known distribution for the future values of predicted variables. In practice, however, forecasts tend to offer little more than Linear Partial Information (LPI), typically of the form, ‘State 1 is more likely to prevail than state 2, and state 2 more likely to prevail than state 3, among five possible states’. This paper shows how such fuzzy LPI statements can be exploited in decision making. For an illustration, LPI analysis is used for determining (ex post) the optimal economic policy to be followed by the Carter Administration with a view to ensuring reelection in 1980. An optimal adaption of that policy occasioned by the fallible 1980 forecasts made by the Council of Economic Advisors is also derived.     

Economic theory and exchange rate forecasts

Businesses use forecasts of exchange rates to make decisions about production, employment, investment, financial management, and pricing decisions. The proper statistical criteria for making and evaluating these exchange rate forecasts are implied by the underlying decision problem. That decision problem is in turn affected by the economic environment facing the firm and its industry, the overall macroeconomic situation, and the main types of disturbances affecting exchange rates. In general, the proper loss function for the forecasting problem will be asymmetric.     

COVID-19: Forecasting confirmed cases and deaths with a simple time series model

Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time series approach to modelling and predicting the short-term behavior of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant.     

A Comparison of Forecasting Techniques for Predicting Exchange Rates

Forecasting exchange rates accurately helps policy makers and businesses to plan more appropriately. Exchange rates provide information for policy makers about a country's mde position relative to that of ocher nations. In addition, accurate informaticm about future exchange rates helps to improve the quality of many management decisions. This study illustrates the use of different forecasting methods in predicting exchange rates f a the British Pound, German Mark and Japanese Yen. A number of accuracy measures are used to judge the performance of these methods. The results show that simple time series techniques can perfam as well as some complex and costly techniques in forecasting exchange rates.     

System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach

In areas from medicine to climate change to economics, we are faced with huge challenges and a need for accurate forecasts, yet our ability to predict the future has been found wanting. The basic problem is that complex systems such as the atmosphere or the economy can not be reduced to simple mathematical laws and modeled accordingly. The equations in numerical models are therefore only approximations to reality, and are often highly sensitive to external influences and small changes in parameterisation — they can be made to fit past data, but are less good at prediction. Since decisions are usually based on our best models of the future, how can we proceed? This paper draws a comparison between two apparently different fields: biology and economics. In biology, drug development is a highly inefficient and expensive process, which in the past has relied heavily on trial and error. Institutions such as pharmaceutical companies and universities are now radically changing their approach and adopting techniques from the new field of systems biology to integrate information from disparate sources and improve the development process. A similar revolution is required in economics if models are to reflect the nature of human economic activity and provide useful tools for policy makers. We outline the main foundations for a theory of systems economics.     

Evaluating quantile forecasts in the M5 uncertainty competition

Probabilistic forecasts are necessary for robust decisions in the face of uncertainty. The M5 Uncertainty competition required participating teams to forecast nine quantiles for unit sales of various products at various aggregation levels and for different time horizons. This paper evaluates the forecasting performance of the quantile forecasts at different aggregation levels and at different quantile levels. We contrast this with some theoretical predictions, and discuss potential implications and promising future research directions for the practice of probabilistic forecasting.     

Corporates' strategic responses to economic policy uncertainty in China

Using a novel news‐based index of economic policy uncertainty, this paper studies the impact of economic policy uncertainty on corporate strategic positioning and corporate risk in China from 2009 to 2015. The study also investigates the impact of corporate strategic positioning on corporate risk. The results show that corporate strategic positioning and economic policy uncertainty have a significant positive impact on corporate risk. The results also explain that economic policy uncertainty increases the market risk of the firms irrespective of their corporate strategy. However, it increases the business risk of prospector firms and decreases the business risk of defensive firms. The study may help the firms to formulate and improve their strategic positioning while considering economic policy uncertainty. Our results are robust to alternate proxies of economic policy uncertainty and corporate risk.     

Economic uncertainty and growth performance: a macroeconomic modeling analysis for Pakistan

This paper investigates the effects of economic uncertainty on growth performance of Pakistan through developing a small macroeconomic model. The GARCH method has been used for construction of economic uncertainty variables related to macroeconomic policies. The structural outcomes clearly indicate that economic policy uncertainty affects negatively on real and nominal sectors of Pakistan. The forecasting of model and different policy uncertainty simulation shocks also indicated that an adjustment in economic policies due to change of policy objectives create uncertain environment in country, which not only deteriorates the investment climate of country, it also affects the economic growth. Our study concludes that economic uncertainty not only reduces the current investment and economic growth, it also affects the future decision of investment and economic growth. This study suggests that sustainable and steady economic policies always reduce economic uncertainty and promote the confidence of economic agents, which help in achieving the targets of investment, trade and economic growth. Our study also maintains the predictability and reliability of government policies for the accomplishment of macroeconomic goals and economic development of country.     

Are professional forecasters overconfident?

Do professional forecasters have an accurate sense of the uncertainties surrounding their own forecasts? This paper examines forecaster overconfidence by comparing ex ante, surveyed forecaster uncertainty with ex post, realised uncertainty based on the dispersion of an individual’s forecast errors. Unlike the literature that focuses on consensus forecasts, our focus is at the level of the individual forecaster. Using microdata from the three major surveys of professional forecasters (Euro Area, US and UK), we examine real GDP growth forecasts over the period 1999–2015. Our findings show that overconfidence dominates among individual forecasters, particularly for longer forecast horizons, and that individual forecasters appear to have little understanding of their own uncertainty.     

DeepAR: Probabilistic forecasting with autoregressive recurrent networks

Probabilistic forecasting, i.e., estimating a time series’ future probability distribution given its past, is a key enabler for optimizing business processes. In retail businesses, for example, probabilistic demand forecasts are crucial for having the right inventory available at the right time and in the right place. This paper proposes DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an autoregressive recurrent neural network model on a large number of related time series. We demonstrate how the application of deep learning techniques to forecasting can overcome many of the challenges that are faced by widely-used classical approaches to the problem. By means of extensive empirical evaluations on several real-world forecasting datasets, we show that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.     

Daily retail demand forecasting using machine learning with emphasis on calendric special days

Demand forecasting is an important task for retailers as it is required for various operational decisions. One key challenge is to forecast demand on special days that are subject to vastly different demand patterns than on regular days. We present the case of a bakery chain with an emphasis on special calendar days, for which we address the problem of forecasting the daily demand for different product categories at the store level. Such forecasts are an input for production and ordering decisions. We treat the forecasting problem as a supervised machine learning task and provide an evaluation of different methods, including artificial neural networks and gradient-boosted decision trees. In particular, we outline and discuss the possibility of formulating a classification instead of a regression problem. An empirical comparison with established approaches reveals the superiority of machine learning methods, while classification-based approaches outperform regression-based approaches. We also found that machine learning methods not only provide more accurate forecasts but are also more suitable for applications in a large-scale demand forecasting scenario that often occurs in the retail industry.     

Economic policy uncertainty and cost of debt financing: International evidence

Prior research shows that economic policy uncertainty affects a wide range of corporate financial decisions; however, there is little research on the relationship between economic policy uncertainty and cost of debt financing across countries. In this paper, we argue that economic policy uncertainty affects cost of debt financing through two mechanisms including information asymmetry and default risk. With a sample of 163,243 firm-years across 17 countries from 2003 to 2016, we find that economic policy uncertainty positively affects cost of debt financing and this effect is stronger during the global financial crisis from 2008 to 2009. Moreover, our research findings show that large firms’ debt financing cost is less affected by economic policy uncertainty.     

Exploring the reshoring and insourcing decision making process: toward an agenda for future research

The topics of reshoring and insourcing have recently become more widely discussed among operations management and international business scholars and managers, as some firms are revoking their offshoring and outsourcing decisions. This research focuses on and clarifies the decision making processes related to the two distinct, yet closely related phenomena of reshoring and insourcing. It presents a conceptual framework of all theoretically possible reshoring and insourcing decisions, illustrated in its applicability by a review of the United States and German business press. Then four future research avenues are developed as part of an overall decision making framework together with an overview of specific research questions for this emergent field. Further research avenues include the need to differentiate between reshoring/insourcing as strategic direction or reaction to failure, studying organizational readiness in addition to decision drivers, improve coverage of the implementation stage and explore further contingency factors such as technological advancement as well as to focus on decision makers as the unit of analysis.     

Learning,disagreement and inflation forecasting

This paper studies inflation forecasting based on the Bayesian learning algorithm which simultaneously learns about parameters and state variables. The Bayesian learning method updates posterior beliefs with accumulating information from inflation and disagreement about expected inflation from the Survey of Professional Forecasters (SPF). The empirical results show that Bayesian learning helps refine inflation forecasts at all horizons over time. Incorporating a Student’s t innovation improves the accuracy of long-term inflation forecasts. Including disagreement has an effect on refining short-term inflation density forecasts. Furthermore, there is strong evidence supporting a positive correlation between disagreement and trend inflation uncertainty. Our findings are helpful for policymakers when they forecast the future and make forward-looking decisions.     

The role of spatial scale in the timing of uncertain environmental policy

The spatial scale of an environmental problem is dictated by boundaries. Physical boundaries limit the extent of impacts while the scale of decision making creates perceived boundaries beyond which impacts are ignored by decision makers. While it is well understood that uncertainty and irreversibility will alter policy decisions aimed at alleviating environmental impacts, the effect of spatial scales, both physical and perceived, is less understood. When spatial scale is included in a real options model of environmental policy adoption results indicate that the importance and influence of spatial considerations depends on the level of uncertainty, stringency of the proposed policy and flexibility of the policy decision. Recognizing spatial scale may force policy adoption to take place within a window of current damage. When spatial scale is small or uncertainty high, this window for policy adoption can close precluding policy adoption entirely. This undermines well-known results demonstrating that changes in uncertainty will only alter the timing of policy adoption. In other instances, the policy adoption window remains open but the option value increases faster than the benefits of the policy creating a scenario where it is always preferable to delay. Here the inclusion of an option value can prevent adoption of policies that would be adopted according to traditional cost-benefit analysis. In general policy decisions will be most affected by spatial considerations when the spatial scale is small, damage is spreading fast, and the uncertainty in damage spread is high.     

On credit and output: Is the supply of credit relevant?

The consensus that changes in the supply of credit were irrelevant to making monetary policy decisions existed among macroeconomists during the second half of the twentieth century. Transmission of shocks to the real economy through changes in the supply of credit, however, played an important role in the recent U.S. financial crisis. This paper explores the extent to which policymakers should consider changes in the supply of credit when making forecasts and monetary policy decisions. More specifically, it considers whether a measure of real credit balances offers consistent and stable information, beyond that of a real interest rate and real money balances, about future output gaps during the U.S. post-war era. Results yield evidence that changes in real credit balances are the only variable, among those considered, to provide consistent and stable information about future output gaps over the entire sample period. Each information variable, however, provides relatively little value added for forecasting future output gaps, beyond a simple autoregressive model. To improve upon forecasts and monetary policy decisions, policymakers therefore should consider a broader range of information variables and occasionally reassess the relative weightings assigned to each.