One-sided Simultaneous Prediction Intervals for AR(1) and MA(1) Processes with Exponential Innovations

This paper is concerned with the determination of simultaneous confidence regions for some types of time series models. We derive recursive formulas which allow the determination of the probability for an AR(1) stationary process based on exponential inputs to lie under any sequence of constants during N steps. Also, probabilities of the same form are derived for an MA(1) process, based on an exponentially distributed white noise. Numerical results are obtained and comparison of prediction regions for different values of ϕ or θ is made. The results show how the use of the correlation structure of the models can reduce the confidence regions area. © 1997 by John Wiley & Sons, Ltd.     

Unobserved components in ARCH models: An application to seasonal adjustment

The paper deals with unobserved components in ARIMA models with GARCH errors, in the context of an actual application, namely seasonal adjustment of the monthly Spanish money supply series. The series shows clear evidence of (moderate) non-linearity, which does not disappear with simple outlier correction. The GARCH structure explains reasonably well the non-linearity, and this explanation is robust with respect to the GARCH specification. We look at the time variation of the standard error of the adjusted series estimator and show how it can be measured. Next, we look at the implications this variation has on short-term monetary control. The non-linearity seems to have a small effect in practice. It is further seen that the conditional variance of the GARCH process may, in turn, be decomposed into components. In fact, the conditional variance of the money supply series is the sum of a weak linear trend, a strong non-linear seasonal component, and a moderate non-linear irregular component. This information has policy implications: for example, there are periods in the year when policy can be more assertive because information is more precise. Finally, looking at the non-linear components of the money supply it is seen how linear combinations of non-linear series can produce series that behave linearly.     

Model averaging estimation for conditional volatility models with an application to stock market volatility forecast

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight-choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1-day-ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility.     

Forecasting Austrian IPOs: An application of linear and neural network error-correction models

In this paper we apply cointegration and Granger-causality analyses to construct linear and neural network error-correction models for an Austrian Initial Public Offerings IndeX (IPOX_ATX). We use the significant relationship between the IPOX_ATX and the Austrian Stock Market Index ATX to forecast the IPOX_ATX. For prediction purposes we apply augmented feedforward neural networks whose architecture is determined by Sequential Network Construction with the Schwartz Information Criterion as an estimator for the prediction risk. Trading based on the forecasts yields results superior to Buy and Hold or Moving Average trading strategies in terms of mean-variance considerations.     

Can cointegration‐based forecasting outperform univariate models? An application to Asian exchange rates

Conventional wisdom holds that restrictions on low‐frequency dynamics among cointegrated variables should provide more accurate short‐ to medium‐term forecasts than univariate techniques that contain no such information; even though, on standard accuracy measures, the information may not improve long‐term forecasting. But inconclusive empirical evidence is complicated by confusion about an appropriate accuracy criterion and the role of integration and cointegration in forecasting accuracy. We evaluate the short‐ and medium‐term forecasting accuracy of univariate Box–Jenkins type ARIMA techniques that imply only integration against multivariate cointegration models that contain both integration and cointegration for a system of five cointegrated Asian exchange rate time series. We use a rolling‐window technique to make multiple out of sample forecasts from one to forty steps ahead. Relative forecasting accuracy for individual exchange rates appears to be sensitive to the behaviour of the exchange rate series and the forecast horizon length. Over short horizons, ARIMA model forecasts are more accurate for series with moving‐average terms of order >1. ECMs perform better over medium‐term time horizons for series with no moving average terms. The results suggest a need to distinguish between ‘sequential’ and ‘synchronous’ forecasting ability in such comparisons. Copyright © 2002 John Wiley & Sons, Ltd.     

The accuracy of extrapolation (time series) methods: Results of a forecasting competition

In the last few decades many methods have become available for forecasting. As always, when alternatives exist, choices need to be made so that an appropriate forecasting method can be selected and used for the specific situation being considered. This paper reports the results of a forecasting competition that provides information to facilitate such choice. Seven experts in each of the 24 methods forecasted up to 1001 series for six up to eighteen time horizons. The results of the competition are presented in this paper whose purpose is to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.     

The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries

The delayed release of the National Account data for GDP is an impediment to the early understanding of the economic situation. In the short run, this information gap may be at least partially eliminated by bridge models (BM) which exploit the information content of timely updated monthly indicators. In this paper we examine the forecasting ability of BM for GDP growth in the G7 countries and compare their performance to that of univariate and multivariate statistical benchmark models. We run four alternative one‐quarter‐ahead forecasting experiments to assess BM performance in situations as close as possible to the actual forecasting activity. BM are estimated for GDP both for single countries (USA, Japan, Germany, France, UK, Italy and Canada), and area‐wide (G7, European Union, and Euro area). BM forecasting ability is always superior to that of benchmark models, provided that at least some monthly indicator data are available over the forecasting horizon. Copyright © 2007 John Wiley & Sons, Ltd.     

Local to unity,long‐horizon forecasting thresholds for model selection in the AR(1)

This article introduces a novel framework for analysing long‐horizon forecasting of the near non‐stationary AR(1) model. Using the local to unity specification of the autoregressive parameter, I derive the asymptotic distributions of long‐horizon forecast errors both for the unrestricted AR(1), estimated using an ordinary least squares (OLS) regression, and for the random walk (RW). I then identify functions, relating local to unity ‘drift’ to forecast horizon, such that OLS and RW forecasts share the same expected square error. OLS forecasts are preferred on one side of these ‘forecasting thresholds’, while RW forecasts are preferred on the other. In addition to explaining the relative performance of forecasts from these two models, these thresholds prove useful in developing model selection criteria that help a forecaster reduce error. Copyright © 2004 John Wiley & Sons, Ltd.     

Forecasting with latent structure time series models: an application to nominal interest rates

In this paper we develop a latent structure extension of a commonly used structural time series model and use the model as a basis for forecasting. Each unobserved regime has its own unique slope and variances to describe the process generating the data, and at any given time period the model predicts a priori which regime best characterizes the data. This is accomplished by using a multinomial logit model in which the primary explanatory variable is a measure of how consistent each regime has been with recent observations. The model is especially well suited to forecasting series which are subject to frequent and/or major shocks. An application to nominal interest rates shows that the behaviour of the three‐month US Treasury bill rate is adequately explained by three regimes. The forecasting accuracy is superior to that produced by a traditional single‐regime model and a standard ARIMA model with a conditionally heteroscedastic error. Copyright © 1999 John Wiley & Sons, Ltd.     

The use of canonical correlation analysis to identify the order of multivariate ARMA models: simulation and application

This paper is concerned with how canonical correlation can be used to identify the structure of a linear multivariate time series model. We describe briefly methods that use the canonical correlation technique and present simulation results in order to compare and evaluate the performance of these methods. The methods are also applied to a well‐known multivariate time series. Copyright © 2000 John Wiley & Sons, Ltd.     

(R)-(Z,E)-9,11-octadecadien-13-olide: An intriguing lactone fromHeliconius pachinus (Lepidoptera)

Summary A fourteen-membered lactone,(R)-(Z,E)-9,11-octadecadien-13-olide, was isolated from extruded abdominal glands of a Neotropical, nymphalid butterfly,Heliconius pachinus (Lepidoptera). This compound was obtained from mature adults of both sexes, but was not detected in young adults or pupae.     

Comparing ex-ante forecasts from a sem and var model: An application to the italian economy

The predictive performance of a large-scale structural econometric model (SEM) of the Italian economy the Prometeia model is compared in this paper with a vector autoregressive (VAR) model estimated for a selection of six main variables of interest. The paper concentrates on the quarterly ex-ante forecasts of GDP growth rate and the annual forecasts of GDP growth and inflation rate, over the period 1980-85. It concludes that no forecaster is systematically better than the other. In particular, the VAR model outperforms the SEM in short-run forecasts, suggesting that, for the latter, more careful attention should be addressed to questions of dynamic specification. On the other hand, for longer intervals, the SEM forecasts are more accurate than the VAR forecasts, in that they can benefit from the judgemental interventions of the model users and the model can pick up the non-linearities of the economy which cannot be captured by the VAR. Given the different kinds of information that can be extracted from the two approaches, it seems more reasonable to consider them as complementary rather than alternative tools for modelling and forecasting. Therefore, rather than attempting to establish the superiority of one type of model over the other, this kind of comparisons should be seen as a useful diagnostic tool for detecting types of model misspecification.     

Modifying quantitative forecasts of livestock production using expert judgments: An application to the australian lamb industry

The judgmental modification of quantitative forecasts has become increasingly adopted in the production of agricultural commodity outlook information. Such modifications allow current period information to be incorporated into the forecast value, and ensure that the forecast is realistic in the context of current industry trends. This paper investigates the potential value of this approach in production forecasting in the Australian lamb industry. Several individual and composite econometric models were used to forecast a lamb-slaughtering series with a selected forecast being given to a panel of lamb industry specialists for consideration and modification. The results demonstrate that this approach offers considerable accuracy advantages in the short-term forecasting of livestock market variables, such as slaughtering, whose values can be strongly influenced by current industry conditions.     

Philosophy in the trenches: from naturalized to experimental philosophy (of science)

Recent years have seen the development of an approach both to general philosophy and philosophy of science often referred to as ‘experimental philosophy’ or just ‘X-Phi’. Philosophers often make or presuppose empirical claims about how people would react to hypothetical cases, but their evidence for claims about what ‘we’ would say is usually very limited indeed. Philosophers of science have largely relied on their more or less intimate knowledge of their field of study to draw hypothetical conclusions about the state of scientific concepts and the nature of conceptual change in science. What they are lacking is some more objective quantitative data supporting their hypotheses. A growing number of philosophers (of science), along with a few psychologists and anthropologists, have tried to remedy this situation by designing experiments aimed at systematically exploring people’s reactions to philosophically important thought experiments or scientists’ use of their scientific concepts. Many of the results have been surprising and some of the conclusions drawn from them have been more than a bit provocative. This symposium attempts to provide a window into this new field of philosophical inquiry and to show how experimental philosophy provides crucial tools for the philosopher and encourages two-way interactions between scientists and philosophers.     

A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China

Air pollution has received more attention from many countries and scientists due to its high threat to human health. However, air pollution prediction remains a challenging task because of its nonstationarity, randomness, and nonlinearity. In this research, a novel hybrid system is successfully developed for PM_2.5 concentration prediction and its application in health effects and economic loss assessment. First, an efficient data mining method is adopted to capture and extract the primary characteristic of PM_2.5 dataset and alleviate the noises' adverse effects. Second, Harris hawks optimization algorithm is introduced to tune the extreme learning machine model with high prediction accuracy, then the optimized extreme learning machine can be established to obtain the forecasting values of PM_2.5 series. Next, PM_2.5-related health effects and economic costs was estimated based on the predicted PM_2.5 values, the related health effects, and environmental value assessment methods. Several experiments are designed using three daily PM_2.5 datasets from Beijing, Tianjin, and Shijiazhuang. Lastly, the corresponding experimental results showed that this proposed system can not only provide early warning information for environmental management, assist in the formulation of effective measures to reduce air pollutant emissions, and prevent health problems but also help for further research and application in different fields, such as health issues due to PM_2.5 pollutant.     

Glucose does not influence the insulin-like growth factor (IGF) binding to carrier proteins (IGFBPs): Analysis of rat and human serum by western ligand blotting

The insulin-like growth factors (IGFs) circulate bound to specific proteins (termed IGFBP-1 through IGFBP-6) that modulate IGF bioactivity in tissues. The aim of this study was to analyse the effects of glucose on IGF binding to IGFBPs in rat and human serum by means of western ligand blotting. Serum samples were incubated with increasing concentrations of glucose (0 to 50 mmol/l), and EDTA (25 mmol/l) was added to inhibit protease activity. To analyse the effect of glucose on protection of IGFBPs from protease activity, serum from pregnant women (reported to be very rich in proteolytic activity against IGFBPs) was added to rat serum previously incubated with glucose. Glucose did not affect the¹²⁵I-IGF binding to rat and human serum IGFBPs. The intensity of IGFBP-3 bands decreased considerably during the incubation. This appeared to be due to endogenous protease activity, since the decrease was blocked by addition of EDTA. The incubationi of rat serum with pregnant human serum produced a marked attenuation of IGFBP-3 and disappearance of IGFBP-4 bands. In conclusion, our study shows that glucose does not influence the IGF binding to IGFBP-3 either in rat or in human serum, confirms the presence of endogenous proteolytic activity in normal non-pregnant serum, and demonstrates that glucose has no protective action against protease activity.     

Fantastic Beasts and where (not) to find them: Local gravitational energy and energy conservation in general relativity

This paper critically examines energy-momentum conservation and local (differential) notions of gravitational energy in General Relativity (GR). On the one hand, I argue that energy-momentum of matter is indeed locally (differentially) conserved: Physical matter energy-momentum 4-currents possess no genuine sinks/sources. On the other hand, global (integral) energy-momentum conservation is contingent on spacetime symmetries. Local gravitational energy-momentum is found to be a supererogatory notion. Various explicit proposals for local gravitational energy-momentum are investigated and found wanting. Besides pseudotensors, the proposals considered include those of Lorentz and Levi-Civita, Pitts and Baker. It is concluded that the ontological commitment we ought to have towards gravitational energy in GR mimics the natural anti-realism/eliminativism towards apparent forces in Newtonian Mechanics.