Oil price forecasting under asymmetric loss

Based on the approach developed by Elliott et al. (2005), we found that the loss function of a sample of oil price forecasters is asymmetric in the forecast error. Our findings indicate that the loss oil price forecasters incurred when their forecasts exceeded the price of oil tended to be larger than the loss they incurred when their forecast fell short of the price of oil. Accounting for the asymmetry of the loss function does not necessarily make forecasts look rational.     

Asymmetric Loss and the Rationality of Inflation Forecasts: Evidence from South Korea

We examine asymmetry in the loss functions of South Korean consumers' and the Bank of Korea's (BOK) inflation forecasts, and test the rationality of these forecasts under the assumption of a possible asymmetric loss function. Under an asymmetric loss function, we find evidence of asymmetry and support for rationality. We also examine whether the BOK's forecasts incorporate respective forecasts and consensus forecasts efficiently. They broadly use available information efficiently, and their results are robust to inflation‐targeting measures and the recent global financial crisis. However, our results suggest that the information efficiency of the BOK's forecasts for consumers was affected during the period 2007–2008.     

German business cycle forecasts, asymmetric loss and financial variables

We examine the efficiency of German forecasts for output growth and inflation allowing for an asymmetric loss function of the forecasters. We find the loss of output growth forecasts to be approximately symmetric while there is an asymmetry in the loss of the inflation forecasts. The information of financial variables seems to be adequately incorporated into the output forecasts but to a lesser extent into the inflation forecasts.     

Do foreign exchange forecasters apply asymmetric loss functions? Evidence from three major exchange rates

This article investigates which type of loss function is consistent with the hypothesis that major exchange rate forecasts, i.e. the euro, the British pound, and the Japanese yen vis-à-vis the US dollar, are rational. We apply a comprehensive data set, which also allows us to examine different forecast horizons and heterogeneity of forecasters.     

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.     

Asymmetric loss and herding behaviour of exchange rate forecasters: evidence from South Africa

This study examines the asymmetry of the loss function for private forecasters in exchange rate forecasts of the South African rand. It tests rationality under the possibility of an asymmetric loss function. The results indicate less evidence of asymmetry for a horizon of 1 month but considerable evidence of asymmetry for a horizon of 3 months. However, the shapes of the distributions formed by estimated asymmetry parameters of sub-samples for each forecaster are symmetric, regardless of the forecast horizons, which implies that these forecasters do not herd or antiherd. In fact, the results of our empirical herding test show that forecasters neither herd nor antiherd, which is in sharp contrast to recent findings on antiherding for foreign exchange rates in emerging market economies. Our findings provide consistent evidence for a recent suggestion that antiherding might result in the rejection of rationality, even under asymmetric loss functions. Our findings also suggest that central bank transparency might be associated with herding behaviours.     

The rationality of EIA forecasts under symmetric and asymmetric loss

The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and electricity prices as well as natural gas consumption, electricity sales, GDP and energy intensity.     

European commission’s fiscal forecasts in CEE countries: a thorough assessment

ABSTRACT

This paper assesses European Commission’s fiscal forecasts for a sample of 10 Central and Eastern European countries between 2005 and 2015. The analysis focus on forecasts of the budget balance, revenues, expenditures and debt and pays special attention to dynamics around business cycle turning points. Results suggest that the distribution of projection errors appears to be biased towards optimism of fiscal aggregates and accuracy increases as the forecast horizon shortens. We also find evidence of “forecast smoothing”. In addition, we find that, on average, the extent of optimism seems to increase during recessions (and to a lesser extent during recoveries). Moreover, errors in forecasting fiscal variables can be explained by forecasts errors of real GDP growth and inflation.     

Is the Fed’s news perception different from the private sector’s?

The recent literature on monetary policy has dedicated considerable attention to modelling agents’ processing of information about the future in real time. This paper contributes to this growing strand by investigating the implied differences in the so-called news shocks estimated from the standard New Keynesian dynamic stochastic general equilibrium (DSGE) model using the real-time data sets from the Survey of Professional Forecasters (SPF) and the Federal Reserve’s Greenbook (GB) forecasts. Alternative specifications with either the SPF or GB forecasts aim to delineate the differences in the private sector’s and the Fed’s expectations of future macroeconomic outcomes and identify the differences in their perception of news shocks. Our results indicate that while the demand news shocks have very similar distributions in the two datasets, the monetary and cost-push news shocks from the models estimated on the GB data tend to be larger than those from the SPF. These findings suggest that the Federal Reserve’s forecasting methods allow for more variation in future outcomes than the SPF’s. These findings mesh well with the extant literature on the superiority of the Fed’s forecasts relative to the private sector’s and provide a structural explanation for the source of this superiority.     

State dependent asymmetric loss and the consensus forecast of real U.S. GDP growth

It has been well documented that the consensus forecast from surveys of professional forecasters shows a bias that varies over time. In this paper, we examine whether this bias may be due to forecasters having an asymmetric loss function. In contrast to previous research, we account for the time variation in the bias by making the loss function depend on the state of the economy. The asymmetry parameter in the loss function is specified to depend on set state variables which may cause forecaster to intentionally bias their forecasts. We consider both the Lin–Ex and asymmetric power loss functions. For the commonly used Lin–Ex and Lin–Lin loss functions, we show the model can be easily estimated by least squares. We apply our methodology to the consensus forecast of real U.S. GDP growth from the Survey of Professional Forecasters. We find that forecast uncertainty has an asymmetric effect on the asymmetry parameter in the loss function dependent upon whether the economy is in expansion or contraction. When the economy is in expansion, forecaster uncertainty is related to an overprediction in the median forecast of real GDP growth. In contrast, when the economy is in contraction, forecaster uncertainty is related to an underprediction in the median forecast of real GDP growth. Our results are robust to the particular loss function that is employed in the analysis.     

For how long do IMF forecasts of world economic growth stay up-to-date?

This study analyses the performance of the International Monetary Fund (IMF) World Economic Outlook output forecasts for the world and for both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current year and the next year, we examine the durability of IMF forecasts, looking at how much time has to pass so that IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time data set for GDP and for indicators, we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts as soon as the publication of the IMF’s Outlook is only a few months old. In particular, there is an obvious gain using leading indicators from January to March for the forecast of the current year.     

Predictions of growth in U.S. corporate profits: Asymmetric vs. symmetric loss

This study evaluates the Federal Reserve and private forecasts of growth in corporate profits for 1984-2004. These forecasts are both rational and directionally accurate but suggest different loss structures. The Federal Reserve forecasts tend to significantly under-predict and imply asymmetric loss. The private forecasts, however, are free of such bias, suggesting symmetric loss. Given that the Federal Reserve forecasts are made to help with policymaking, our findings point to the Fed's cautiousness not to incorrectly predict the downward moves in growth in corporate profits. The private forecasts are made by experts who (with a strong profit-motivated interest) attempt to generate financial gain and thus predict the upward moves as accurately as the downward moves.     

Explanations of the inconsistencies in survey respondents’ forecasts

A comparison of the point forecasts and the probability distributions of inflation and output growth made by individual respondents to the US Survey of Professional Forecasters indicates that the two sets of forecasts are sometimes inconsistent. We evaluate a number of possible explanations, and find that not all forecasters update their histogram forecasts as new information arrives. This is supported by the finding that the point forecasts are more accurate than the histograms in terms of first-moment prediction.     

Modelling demand for lotto using a novel method of correcting for endogeneity

Modelling lottery sales as a function of the mean, standard deviation and skewness of the probability distribution of returns potentially gives insights into how the design of a game could be modified to maximise net revenue. But use of OLS is problematic because the level of sales itself affects values of the moments (and insufficient instruments are available for IV regression). We draw on the concept of a rational expectations equilibrium, developing a new regression model which corrects for endogeneity where the causal impact of the dependent variable on the right-hand side variables is deterministic. We apply the model to data on lotto sales from Spain. Using the Spanish data, we show that results provide more reliable guidance to lottery agencies because accounting for endogeneity leads to significantly different results from OLS and these results have superior performance in out-of-sample forecasting of sales. More generally, results prove consistent with the Friedman-Savage explanation of why people buy lottery tickets and with evidence from racetrack data that ‘bettors love skewness’.     

Forecasting sales and product evolution: The case of the hybrid/electric car

We present a model that forecasts sales and product evolution, based on data on market and industry, which can be collected before the product is introduced. Product evolution can be incremental but can also take place by releasing new generations. In our model adoption of a new product is motivated by attribute improvements (enabled by technology evolution), and firms' attribute improvements strategies are motivated by market growth and directed by market preferences. The interdependency between attributes' improvements and cumulative adoption level makes the problem inherently dynamic. The dependency of attribute levels on adoption levels is assessed using industry and technology analysis. Market preferences and purchase intention response to attribute levels changes are assessed based on a conjoint study. The option of collecting and interpreting data about both demand and supply aspects, before the new product is introduced, enables us to estimate sales and technology progress endogenously rather than to require them as inputs. We demonstrate the method on the hybrid car market.     

Estimation and Testing of Forecast Rationality under Flexible Loss

In situations where a sequence of forecasts is observed, a common strategy is to examine „rationality” conditional on a given loss function. We examine this from a different perspective—supposing that we have a family of loss functions indexed by unknown shape parameters, then given the forecasts can we back out the loss function parameters consistent with the forecasts being rational even when we do not observe the underlying forecasting model? We establish identification of the parameters of a general class of loss functions that nest popular loss functions as special cases and provide estimation methods and asymptotic distributional results for these parameters. This allows us to construct new tests of forecast rationality that allow for asymmetric loss. The methods are applied in an empirical analysis of IMF and OECD forecasts of budget deficits for the G7 countries. We find that allowing for asymmetric loss can significantly change the outcome of empirical tests of forecast rationality.     

The accuracy of interest rate forecasts in the Asia-Pacific region: opportunities for portfolio management

We analysed interest rate forecasts from Australia, China, Hong Kong, India, Indonesia, Malaysia, New Zealand, Singapore, South Korea, Taiwan and Thailand. We assessed 532 forecast time series with a total of 85,264 individual interest rate forecasts. To do so, we carried out a comparison to naïve forecasts and investigated the forecast time series for topically orientated trend adjustments. In addition, we deployed the sign accuracy test and the unbiasedness test. The results are very sobering in part: 95.9% of all forecast time series are characterized by the phenomenon of topically orientated trend adjustments, and 99.4% of all forecast time series proved to be biased. Only a small proportion of the forecast time series (3.6%) reflected the future interest rate trend significantly more precisely than a naïve forecast. However, at the same time some of the results of the study are surprisingly positive. The sign accuracy test revealed that 48.3% of all forecast time series predict the interest rate trend significantly better than a random walk forecast.     

Does director affiliation lead to analyst bias?

This study examines the ability of security analysts to provide objective earnings forecasts for firms with which the analyst’s brokerage firm has a director affiliation. The affiliation that we examine is where the brokerage firm has, on its board of directors, a director or an upper management individual from the firm which an analyst at the brokerage firm provides coverage. We find that affiliated analysts tend to provide earnings forecasts that are insignificantly different from unaffiliated analysts in terms of accuracy. However, we also find that forecasts provided by affiliated analysts tend to be significantly more pessimistic than those provided by their unaffiliated counterparts. This pessimistic bias in their earnings forecast will more easily allow the covered firm to beat earnings expectations when earnings are realized. We find that this bias surfaced after the Global Settlement decision, an enforcement agreement between large investment banks and the Securities and Exchange Commission (SEC) regarding issues surrounding conflicts of interest.     

Do forecasters of major exchange rates herd?

This paper uses forecast data from 1995 through 2014 to examine, whether the market consensus of exchange rate forecasts has an effect on the forecasts of individual experts. Such an effect could take the form of herding or anti-herding. We use a very comprehensive data set to study experts' forecasts of three of the most important exchange rates. The results indicate that anti-herding vis-à-vis the consensus of forecasts occurs more often than herding. We also show how the increase in the forecasting horizon and financial crises affect the intensity of anti-herding behavior. Moreover, we report that the (anti-)herding behavior does not affect the forcasting performnce.     

A product diffusion model incorporating repeat purchases

A logistic-based model for forecasting the rate of product diffusion given aggregate time series data was constructed. The model differs from earlier models based on fitting the logistic to aggregate data in that it includes a submodel to separate replacement demand from first-time sales. We fit the theoretical model to data and show that forecasts will be significantly more accurate using this model instead of the logistic curve.