Do experts' adjustments on model‐based SKU‐level forecasts improve forecast quality?

Model‐based SKU‐level forecasts are often adjusted by experts. In this paper we propose a statistical methodology to test whether these expert forecasts improve on model forecasts. Application of the methodology to a very large database concerning experts in 35 countries who adjust SKU‐level forecasts for pharmaceutical products in seven distinct categories leads to the general conclusion that expert forecasts are equally good at best, but are more often worse than model‐based forecasts. We explore whether this is due to experts putting too much weight on their contribution, and this indeed turns out to be the case. Copyright © 2009 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.     

Why do regime‐switching models forecast so badly?

Most non‐linear techniques give good in‐sample fits to exchange rate data but are usually outperformed by random walks or random walks with drift when used for out‐of‐sample forecasting. In the case of regime‐switching models it is possible to understand why forecasts based on the true model can have higher mean squared error than those of a random walk or random walk with drift. In this paper we provide some analytical results for the case of a simple switching model, the segmented trend model. It requires only a small misclassification, when forecasting which regime the world will be in, to lose any advantage from knowing the correct model specification. To illustrate this we discuss some results for the DM/dollar exchange rate. We conjecture that the forecasting result is more general and describes limitations to the use of switching models for forecasting. This result has two implications. First, it questions the leading role of the random walk hypothesis for the spot exchange rate. Second, it suggests that the mean square error is not an appropriate way to evaluate forecast performance for non‐linear models. Copyright © 1999 John Wiley & Sons, Ltd.     

Do Long‐Run Theory Restrictions Help in Forecasting?

Do long‐run equilibrium relations suggested by economic theory help to improve the forecasting performance of a cointegrated vector error correction model (VECM)? In this paper we try to answer this question in the context of a two‐country model developed for the Canadian and US economies. We compare the forecasting performance of the exactly identified cointegrated VECMs to the performance of the over‐identified VECMs with the long‐run theory restrictions imposed. We allow for model uncertainty and conduct this comparison for every possible combination of the cointegration ranks of the Canadian and US models. We show that the over‐identified structural cointegrated models generally outperform the exactly identified models in forecasting Canadian macroeconomic variables. We also show that the pooled forecasts generated from the over‐identified models beat most of the individual exactly identified and over‐identified models as well as the VARs in levels and in differences. Copyright © 2011 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.     

Short‐term forecasts of economic activity: Are fortnightly factors useful?

A short‐term mixed‐frequency model is proposed to estimate and forecast Italian economic activity fortnightly. We introduce a dynamic one‐factor model with three frequencies (quarterly, monthly, and fortnightly) by selecting indicators that show significant coincident and leading properties and are representative of both demand and supply. We conduct an out‐of‐sample forecasting exercise and compare the prediction errors of our model with those of alternative models that do not include fortnightly indicators. We find that high‐frequency indicators significantly improve the real‐time forecasts of Italian gross domestic product (GDP); this result suggests that models exploiting the information available at different lags and frequencies provide forecasting gains beyond those based on monthly variables alone. Moreover, the model provides a new fortnightly indicator of GDP, consistent with the official quarterly series.     

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.     

<Emphasis Type="Italic">Ginkgo biloba</Emphasis> extract EGb<Superscript>®</Superscript> 761 increases endothelial nitric oxide production <Emphasis Type="Italic">in vitro</Emphasis> and <Emphasis Type="Italic">in vivo</Emphasis>

Beneficial effects of Ginkgo biloba on peripheral arterial occlusive disease have been repeatedly shown in clinical trials, especially after use of EGb 761, a standardized special extract. Since the underlying mechanisms are widely unknown, we aimed to elucidate the molecular basis on which EGb 761 protects against endothelial dysfunction in vitro and in vivo. Application of therapeutically feasible doses of EGb 761 for 48 h caused endothelial nitric oxide (NO) production by increasing endothelial nitric oxide synthase (eNOS) promoter activity and eNOS expression in vitro. Phosphorylation of eNOS at a site typical for Akt (Ser 1177) was acutely enhanced by treatment with EGb 761, as was Akt phosphorylation at Ser 478. Furthermore, the extract caused acute relaxation of isolated aortic rings and NO-dependent reduction of blood pressure in vivo in rats. These influences on eNOS represent a putative molecular basis for the protective cardiovascular properties of EGb 761.     

Real‐time or current vintage: does the type of data matter for forecasting and model selection?

In this paper we investigate the impact of data revisions on forecasting and model selection procedures. A linear ARMA model and nonlinear SETAR model are considered in this study. Two Canadian macroeconomic time series have been analyzed: the real‐time monetary aggregate M3 (1977–2000) and residential mortgage credit (1975–1998). The forecasting method we use is multi‐step‐ahead non‐adaptive forecasting. Copyright © 2008 John Wiley & Sons, Ltd.     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

Are industry‐level indicators more helpful to forecast industrial stock volatility? Evidence from Chinese manufacturing purchasing managers index

Effectively explaining and accurately forecasting industrial stock volatility can provide crucial references to develop investment strategies, prevent market risk and maintain the smooth running of national economy. This paper aims to discuss the roles of industry‐level indicators in industrial stock volatility. Selecting Chinese manufacturing purchasing managers index (PMI) and its five component PMI as the proxies of industry‐level indicators, we analyze the contributions of PMI on industrial stock volatility and further compare the volatility forecasting performances of PMI, macroeconomic fundamentals and economic policy uncertainty (EPU), by constructing the individual and combination GARCH‐MIDAS models. The empirical results manifest that, first, most of the PMI has significant negative effects on industrial stock volatility. Second, PMI which focuses on the industrial sector itself is more helpful to forecast industrial stock volatility compared with the commonly used macroeconomic fundamentals and economic policy uncertainty. Finally, the combination GARCH‐MIDAS approaches based on DMA technique demonstrate more excellent predictive abilities than the individual GARCH‐MIDAS models. Our major conclusions are robust through various robustness checks.     

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.     

<Emphasis Type="Italic">Cis</Emphasis>–<Emphasis Type="Italic">trans</Emphasis> interactions of cell surface receptors: biological roles and structural basis

Cell surface receptors bind ligands expressed on other cells (in trans) in order to communicate with neighboring cells. However, an increasing number of cell surface receptors are found to also interact with ligands expressed on the same cell (in cis). These observations raise questions regarding the biological role of such cis interactions. Specifically, it is important to know whether cis and trans binding have distinct functional effects and, if so, how a single cell discriminates between interactions in cis versus trans. Further, what are the structural features that allow certain cell surface receptors to engage ligand both on the same as well as on an apposed cell membrane? Here, we summarize known examples of receptors that display cis–trans binding and discuss the emerging diversity of biological roles played by these unconventional two-way interactions, along with their structural basis.     

Is Newton a ‘radical empiricist’ about method?

Recently, some Newton scholars have argued that Newton is an empiricist about metaphysics—that ideally, he wants to let advances in physical theory resolve either some or all metaphysical issues. But while proponents of this interpretation are using ‘metaphysics’ in a very broad sense, to include the ‘principles that enable our knowledge of natural phenomena’, attention has thus far been focused on Newton’s approach to ontological, not epistemological or methodological, issues. In this essay, I therefore consider whether Newton wants to let physical theory bear on the very ‘principles that enable our knowledge’. By examining two kinds of argument in the Principia, I contend that Newton can be considered a methodological empiricist in a substantial respect. I also argue, however, that he cannot be a ‘radical empiricist’—that he does not and cannot convert all methodological issues into empirical issues.     

What leads from <Emphasis Type="Italic">dead-end?</Emphasis>

The 129 mouse strain develops congenital testicular germ cell tumors (TGCTs) at a low frequency. TGCTs in mice resemble the testicular tumors (teratomas) that occur in human infants. The genes that cause these tumors in 129 have not been identified. The defect at the Ter locus increases TGCT incidence such that 94% of 129-Ter/Ter males develop TGCTs. The primary effect of the Ter mutation is progressive loss of primordial germ cells (PGCs) during embryonic development. This results in sterility in adult Ter/Ter mice on all mouse strain backgrounds. However, on the 129 background, Ter causes tumor development in addition to sterility. Therefore, Ter acts as a modifier of 129-derived TGCT susceptibility genes. Ter was identified to be a mutation that inactivates the Dead-end1 (Dnd1) gene. In this perspective, I discuss the possible areas of future investigations to elucidate the mechanism of TGCT development due to Dnd1 inactivation. Received 29 September 2006; received after revision 29 January 2007; accepted 19 February 2007