Do Experts’ SKU Forecasts Improve after Feedback?

We analyze the behavior of experts who quote forecasts for monthly SKU‐level sales data, where we compare data before and after the moment that experts received different kinds of feedback on their behavior. We have data for 21 experts located in as many countries who make SKU‐level forecasts for a variety of pharmaceutical products for October 2006 to September 2007. We study the behavior of the experts by comparing their forecasts with those from an automated statistical program, and we report the forecast accuracy over these 12 months. In September 2007 these experts were given feedback on their behavior and they received training at the headquarters office, where specific attention was given to the ins and outs of the statistical program. Next, we study the behavior of the experts for the 3 months after the training session, i.e. October 2007 to December 2007. Our main conclusion is that in the second period the experts’ forecasts deviated less from the statistical forecasts and that their accuracy improved substantially. Copyright © 2013 John Wiley & Sons, Ltd.     

How Informative are the Subjective Density Forecasts of Macroeconomists?

In this paper, we propose a framework to evaluate the subjective density forecasts of macroeconomists using micro data from the euro area Survey of Professional Forecasters (SPF). A key aspect of our analysis is the use of evaluation measures which take account of the entire predictive densities, and not just the probability assigned to the outcome that occurs. Overall, we find considerable heterogeneity in the performance of the surveyed densities at the individual level. However, it is hard to exploit this heterogeneity and improve aggregate performance by trimming poorly performing forecasters in real time. Relative to a set of simple benchmarks, density performance is somewhat better for GDP growth than for inflation, although in the former case it diminishes substantially with the forecast horizon. In addition, we report evidence of an improvement in the relative performance of expert densities during the recent period of macroeconomic volatility. However, our analysis also reveals clear evidence of overconfidence or neglected risks in expert probability assessments, as reflected in frequent occurrences of events which are assigned a zero probability. Copyright © 2014 John Wiley & Sons, Ltd.     

Does Information Help Intra‐Day Volatility Forecasts?

While much research related to forecasting return volatility does so in a univariate setting, this paper includes proxies for information flows to forecast intra‐day volatility for the IBEX 35 futures market. The belief is that volume or the number of transactions conveys important information about the market that may be useful in forecasting. Our results suggest that augmenting a variety of GARCH‐type models with these proxies lead to improved forecasts across a range of intra‐day frequencies. Furthermore, our results present an interesting picture whereby the PARCH model generally performs well at the highest frequencies and shorter forecasting horizons, whereas the component model performs well at lower frequencies and longer forecast horizons. Both models attempt to capture long memory; the PARCH model allows for exponential decay in the autocorrelation function, while the component model captures trend volatility, which dominates over a longer horizon. These characteristics are likely to explain the success of each model. Copyright © 2013 John Wiley & Sons, Ltd.     

When are Direct Multi‐step and Iterative Forecasts Identical?

Although both direct multi‐step‐ahead forecasting and iterated one‐step‐ahead forecasting are two popular methods for predicting future values of a time series, it is not clear that the direct method is superior in practice, even though from a theoretical perspective it has lower mean squared error (MSE). A given model can be fitted according to either a multi‐step or a one‐step forecast error criterion, and we show here that discrepancies in performance between direct and iterative forecasting arise chiefly from the method of fitting, and is dictated by the nuances of the model's misspecification. We derive new formulas for quantifying iterative forecast MSE, and present a new approach for assessing asymptotic forecast MSE. Finally, the direct and iterative methods are compared on a retail series, which illustrates the strengths and weaknesses of each approach. Copyright © 2015 John Wiley & Sons, Ltd.     

The Predictive Power of Survey‐Based Exchange Rate Forecasts: Is there a Role for Dispersion?

Previous research has shown that the consensus of individual exchange rate forecasts performs no better than many commonly used forecasting models in predicting future exchange rates. Studies on equity and bond markets have explored the effects of dispersion in forecasts on the predictive power of forecasts; however, no earlier paper has investigated such effects in the context of the foreign exchange market. This study explores the role of consensus forecast dispersion as a factor leading to bias and anchoring in exchange rate forecasts. Our analysis of five currency pairs reveals that consensus forecasts mostly appear to be unbiased predictors of exchange rates in the long run, but most are unable to pass tests for short‐run unbiasedness. In three of the five currencies examined it appears that forecasters should take greater account of reported forecast dispersion. Copyright © 2015 John Wiley & Sons, Ltd.     

Daily FX Volatility Forecasts: Can the GARCH(1,1) Model be Beaten using High‐Frequency Data?

Volatility forecasting remains an active area of research with no current consensus as to the model that provides the most accurate forecasts, though Hansen and Lunde (2005) have argued that in the context of daily exchange rate returns nothing can beat a GARCH(1,1) model. This paper extends that line of research by utilizing intra‐day data and obtaining daily volatility forecasts from a range of models based upon the higher‐frequency data. The volatility forecasts are appraised using four different measures of ‘true’ volatility and further evaluated using regression tests of predictive power, forecast encompassing and forecast combination. Our results show that the daily GARCH(1,1) model is largely inferior to all other models, whereas the intra‐day unadjusted‐data GARCH(1,1) model generally provides superior forecasts compared to all other models. Hence, while it appears that a daily GARCH(1,1) model can be beaten in obtaining accurate daily volatility forecasts, an intra‐day GARCH(1,1) model cannot be. Copyright © 2011 John Wiley & Sons, Ltd.     

Term Structure Forecasting of Government Bond Yields with Latent and Macroeconomic Factors: Do Macroeconomic Factors Imply Better Out‐of‐Sample Forecasts?

This study empirically examines the role of macroeconomic and stock market variables in the dynamic Nelson–Siegel framework with the purpose of fitting and forecasting the term structure of interest rate on the Japanese government bond market. The Nelson–Siegel type models in state‐space framework considerably outperform the benchmark simple time series forecast models such as an AR(1) and a random walk. The yields‐macro model incorporating macroeconomic factors leads to a better in‐sample fit of the term structure than the yields‐only model. The out‐of‐sample predictability of the former for short‐horizon forecasts is superior to the latter for all maturities examined in this study, and for longer horizons the former is still compatible to the latter. Inclusion of macroeconomic factors can dramatically reduce the autocorrelation of forecast errors, which has been a common phenomenon of statistical analysis in previous term structure models. Copyright © 2013 John Wiley & Sons, Ltd.     

Affine Term Structure Model with Macroeconomic Factors: Do No‐Arbitrage Restriction and Macroeconomic Factors Imply Better Out‐of‐Sample Forecasts?

This study extends the affine dynamic Nelson–Siegel model for the inclusion of macroeconomic variables. Five macroeconomic variables are included in affine term structure model, derived under the arbitrage‐free restriction, to evaluate their role in the in‐sample fitting and out‐of‐sample forecasting of the term structure. We show that the relationship between the macroeconomic factors and yield data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Moreover, the macroeconomic factors significantly improve the forecast performance of the model. The affine Nelson–Siegel type models outperform the benchmark simple time series forecast models. The out‐of‐sample predictability of the affine Nelson–Siegel model with macroeconomic factors for the short horizon is superior to the simple affine yield model for all maturities, and for longer horizons the former is still compatible to the latter, particularly for medium and long maturities. Copyright © 2015 John Wiley & Sons, Ltd.     

Stratosph?rische Wellenvorg?nge als Ursache der Witterungssingularit?ten

Ohne Zusammenfassung     

Why eliminativism?

A central thesis of Steven French's brand of ontic structural realism has always been his eliminativism about objects. Unsurprisingly, this bold and controversial thesis has seen a lot of critical discussion. In his book The Structure of the World—Metaphysics & Representation, French accordingly defends this thesis against a range of challenges. A novel feature of this defense is the use of dependence relations to articulate his eliminativism. In this paper I take a critical look at French's defense of eliminativism and argue that the dependence relations invoked do not eliminate objects.     

Whence chemistry?

Along with exploring some of the necessary conditions for the chemistry of our world given what we know about quantum mechanics, I will also discuss a different reductionist challenge than is usually considered in debates on the relationship of chemistry to physics. Contrary to popular belief, classical physics does not have a reductive relationship to quantum mechanics and some of the reasons why reduction fails between classical and quantum physics are the same as for why reduction fails between chemistry and quantum physics. However, a neoreductionist can accept that classical physics has some amount of autonomy from quantum mechanics, but still try to maintain that classical+quantum physics taken as a whole reduces chemistry to physics. I will explore some of the obstacles lying in the neoreductionist's path with respect to quantum chemistry and thereby hope to shed more light on the conditions necessary for the chemistry of our world.     

OV or TOV?

The well-known equation for hydrostatic equilibrium in a static spherically symmetric spacetime supported by an isotropic perfect fluid is referred to as the Oppenheimer–Volkoff (OV) equation or the Tolman–Oppenheimer–Volkoff (TOV) equation in various General Relativity textbooks or research papers. We scrutinize the relevant original publications to argue that the former is the more appropriate terminology.     

Do cells think?

A microorganism has to adapt to changing environmental conditions in order to survive. Cells could follow one of two basic strategies to address such environmental fluctuations. On the one hand, cells could anticipate a fluctuating environment by spontaneously generating a phenotypically diverse population of cells, with each subpopulation exhibiting different capacities to flourish in the different conditions. Alternatively, cells could sense changes in the surrounding conditions – such as temperature, nutritional availability or the presence of other individuals – and modify their behavior to provide an appropriate response to that information. As we describe, examples of both strategies abound among different microorganisms. Moreover, successful application of either strategy requires a level of memory and information processing that has not been normally associated with single cells, suggesting that such organisms do in fact have the capacity to ‘think’. Received 3 January 2007; accepted 4 April 2007     

Is there directional smelling?

The aim of the present study was to establish the crucial precondition for directional smelling, i.e. the ability of humans to discriminate between odorous stimuli perceived either from the right or from the left side. When the pure odorants hydrogen sulphide or vanillin were used as stimulants localization was random. On the other hand stimulation with carbon dioxide or menthol yielded identification rates of more than 96%. These results established the fact that directional orientation, considering single momentary odorous sensations, can only be assumed, when the olfactory stimulants simultaneously excite the trigeminal somatosensory system.     

Ψ-epistemic quantum cosmology?

This paper provides a prospectus for a new way of thinking about the wavefunction of the universe: a Ψ-epistemic quantum cosmology. We present a proposal that, if successfully implemented, would resolve the cosmological measurement problem and simultaneously allow us to think sensibly about probability and evolution in quantum cosmology. Our analysis draws upon recent work on the problem of time in quantum gravity and causally symmetric local hidden variable theories. Our conclusion weighs the strengths and weaknesses of the approach and points towards paths for future development.     

What was classical genetics?

I present an account of classical genetics to challenge theory-biased approaches in the philosophy of science. Philosophers typically assume that scientific knowledge is ultimately structured by explanatory reasoning and that research programs in well-established sciences are organized around efforts to fill out a central theory and extend its explanatory range. In the case of classical genetics, philosophers assume that the knowledge was structured by T. H. Morgan’s theory of transmission and that research throughout the later 1920s, 30s, and 40s was organized around efforts to further validate, develop, and extend this theory. I show that classical genetics was structured by an integration of explanatory reasoning (associated with the transmission theory) and investigative strategies (such as the ‘genetic approach’). The investigative strategies, which have been overlooked in historical and philosophical accounts, were as important as the so-called laws of Mendelian genetics. By the later 1920s, geneticists of the Morgan school were no longer organizing research around the goal of explaining inheritance patterns; rather, they were using genetics to investigate a range of biological phenomena that extended well beyond the explanatory domain of transmission theories. Theory-biased approaches in history and philosophy of science fail to reveal the overall structure of scientific knowledge and obscure the way it functions.     

What is narrative possibility?

In this paper I offer a new account of narrative possibility that I call the “ecological approach.” I situate it relative to alternative “metaphysical” and “epistemological” approaches, and argue that it has advantages in comparison. It saves some of the important insights from each, but serves the purposes of narrative explanation better than either, specifically because it delimits the explanatory modal space of narrative explanation correctly, whereas the others do not.