A forecasting comparison of classical and Bayesian methods for modelling logistic diffusion

A Bayesian procedure for forecasting S‐shaped growth is introduced and compared to classical methods of estimation and prediction using three variants of the logistic functional form and annual times series of the diffusion of music compact discs in twelve countries. The Bayesian procedure was found not only to improve forecast accuracy, using the medians of the predictive densities as point forecasts, but also to produce intervals with a width and asymmetry more in accord with the outcomes than intervals from the classical alternative. While the analysis in this paper focuses on logistic growth, the problem is set up so that the methods are transportable to other characterizations of the growth process. Copyright © 2001 John Wiley & Sons, Ltd.     

Parameter Space Restrictions in State Space Models

The state space model is widely used to handle time series data driven by related latent processes in many fields. In this article, we suggest a framework to examine the relationship between state space models and autoregressive integrated moving average (ARIMA) models by examining the existence and positive‐definiteness conditions implied by auto‐covariance structures. This study covers broad types of state space models frequently used in previous studies. We also suggest a simple statistical test to check whether a certain state space model is appropriate for the specific data. For illustration, we apply the suggested procedure in the analysis of the United States real gross domestic product data. Copyright © 2011 John Wiley & Sons, Ltd.     

A simultaneous test of unit root and level change

Testing the existence of unit root and/or level change is necessary in order to understand the underlying processes of time series. In many studies carried out so far, the focus was only on a single aspect of unit root and level change, therefore limiting a full assessment of the given problems. Our study aims to find a solution to the given problems by testing the two hypotheses simultaneously. We derive the likelihood ratio test statistic based on the state space model, and their distributions are created by the simulation method. The performance of the proposed method is validated by simulated time series and also applied to two Korean macroeconomic time series to confirm its practical application. This analysis can provide a solution to determine the underlying structure of arguable time series. Copyright © 2009 John Wiley & Sons, Ltd.     

A non‐Gaussian generalization of the Airline model for robust seasonal adjustment

In their seminal book Time Series Analysis: Forecasting and Control, Box and Jenkins (1976) introduce the Airline model, which is still routinely used for the modelling of economic seasonal time series. The Airline model is for a differenced time series (in levels and seasons) and constitutes a linear moving average of lagged Gaussian disturbances which depends on two coefficients and a fixed variance. In this paper a novel approach to seasonal adjustment is developed that is based on the Airline model and that accounts for outliers and breaks in time series. For this purpose we consider the canonical representation of the Airline model. It takes the model as a sum of trend, seasonal and irregular (unobserved) components which are uniquely identified as a result of the canonical decomposition. The resulting unobserved components time series model is extended by components that allow for outliers and breaks. When all components depend on Gaussian disturbances, the model can be cast in state space form and the Kalman filter can compute the exact log‐likelihood function. Related filtering and smoothing algorithms can be used to compute minimum mean squared error estimates of the unobserved components. However, the outlier and break components typically rely on heavy‐tailed densities such as the t or the mixture of normals. For this class of non‐Gaussian models, Monte Carlo simulation techniques will be used for estimation, signal extraction and seasonal adjustment. This robust approach to seasonal adjustment allows outliers to be accounted for, while keeping the underlying structures that are currently used to aid reporting of economic time series data. Copyright © 2006 John Wiley & Sons, Ltd.     

Multiple prediction intervals for time series: Comparison of simultaneous and marginal intervals

Simultaneous prediction intervals for forecasts from time series models that contain L (L ≤ 1) unknown future observations with a specified probability are derived. Our simultaneous intervals are based on two types of probability inequalities, i.e. the Bonferroni- and product-types. These differ from the marginal intervals in that they take into account the correlation structure between the forecast errors. For the forecasting methods commonly used with seasonal time series data, we show how to construct forecast error correlations and evaluate, using an example, the simultaneous and marginal prediction intervals. For all the methods, the simultaneous intervals are accurate with the accuracy increasing with the use of higher-order probability inequalities, whereas the marginal intervals are far too short in every case. Also, when L is greater than the seasonal period, the simultaneous intervals based on improved probability inequalities will be most accurate.     

A comparison of adaptive structural forecasting methods for electricity sales

This paper presents the results of a study to determine whether new forecasting technologies might be of use to electric utilities for sales forecasting up to 3 years into the future. The methods considered included ordinary least squares on dynamic structural models, autocorrelated error models, adaptive variance and adaptive parameter models. Overall, the more adaptive models performed best, but most of the methods proved vastly superior to simple least squares models which do not take dynamics into account.     

A comparison of methods for the inactivation of third component of guinea-pig complement

Riassunto Sono stati esaminati comparativamente tre reagenti per la titolazione di C₃, rispettivamente allestiti con siero di cavia inattivato con zymosan, formalina e «Liquoid». Le differenze esistenti tra essi sono state analizzate e discusse in base alle attuali conoscenze sulla natura del terzo componente complementare.

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Supported in part by Contract DA-49-007-MD-696 with the Office of the Surgeon General, Department of the Army.     

A comparison of methods for the extraction of amino acids from fungal mycelium

Résumé En comparant plusieurs techniques pour extraire l'amino-acide libre de la mycose fongique on a observé de grandes différences entre elles. L'eau bouillante et la méthyléthylcétone HCl se sont montrés les solvents les plus efficaces et donnant chromatogrammes les plus clairs.     

Using the bootstrap for improved ARIMA model identification

This paper presents a new method of identifying ARIMA time-series models. We use the bootstrap technique in estimating the distribution of sample autocorrelations both separately and in a simultaneous inference setting. The bootstrap has the advantage of being nonparametric and thus free of reliance on asymptotic normality, which may not hold for short or medium-size series. The simultaneous procedure is unique, as it has no feasible parametric alternatives. An application to exchange rates illustrates our methodology. In the example chosen, we are able to produce better forecasts using the model identified via the bootstrap technique.     

Alternative regime switching models for forecasting inflation

US inflation appears to undergo shifts in its mean level and variability. We evaluate the performance of three useful models for capturing such shifts. The models studied are the Markov switching models, state space models with heavy‐tailed errors, and state space models with compound error distributions. Our study shows that all three models have very similar performance when evaluated in terms of the mean squared or mean absolute forecast errors. However, the latter two models are considerably more parsimonious, and easily beat the more profligately parameterized Markov switching models in terms of model selection criteria, such as the AIC or the SBC. Thus, these may serve as useful continuous alternatives to the popular discrete Markov switching models for capturing shifts in time series. Copyright © 2001 John Wiley & Sons, Ltd.     

A state space approach to forecasting the final vintage of revised data with an application to the index of industrial production

There is considerable interest in the index of industrial production (IIP) as an indicator of the state of the UK's industrial base and, more generally, as a leading economic indicator. However, this index, in common with a number of key macroeconomic time series, is subject to revision as more information becomes available. This raises the problem of forecasting the final vintage of data on IIP. We construct a state space model to solve this problem which incorporates bias adjustments, a model of the measurement error process, and a dynamic model for the final vintage of IIP. Application of the Kalman filter produces an optimal forecast of the final vintage of data.     

Forecasting the economic recession in the U.K. 1979-1982: A comparison of model-based ex ante Forecasts

This paper compares the forecasts of recession and recovery made by five non-government U.K. teams modelling the economy (Cambridge Econometrics, the London Business School, the National Institute of Economic and Social Research, the Cambridge Economic Policy Group and the Liverpool Research Group). The paper concentrates on annual ex ante projections as published over the period 1978-1982, i.e. forecasts made, before the event, of the onset, length, depth and character of the economic recession in the U.K. which began in 1979. The comparison is in terms of year by year changes in production, unemployment, prices and other variables. It concludes that no group was systematically better or worse than other groups (confirming U.S. experience) and that the groups tended to perform better in their chosen areas of specialization, e.g. medium-term groups did better at forecasting the medium-term outcome.     

A comparison of approximate Bayesian forecasting methods for non‐Gaussian time series

We present the results on the comparison of efficiency of approximate Bayesian methods for the analysis and forecasting of non‐Gaussian dynamic processes. A numerical algorithm based on MCMC methods has been developed to carry out the Bayesian analysis of non‐linear time series. Although the MCMC‐based approach is not fast, it allows us to study the efficiency, in predicting future observations, of approximate propagation procedures that, being algebraic, have the practical advantage of being very quick. Copyright © 2000 John Wiley & Sons, Ltd.     

New proposals for the quantification of qualitative survey data

In this paper we propose and evaluate two new methods for the quantification of business surveys concerning the qualitative assessment of the state of the economy. The first is a nonparametric method based on the spectral envelope, originally proposed by Stoffer, Tyler and McDougall (Spectral analysis for categorical time series: scaling and the spectral envelope, Biometrika 80 : 611–622) to the multivariate time series of the counts in each response category. Secondly, we fit by maximum likelihood a cumulative logit unobserved components models featuring a common cycle. The conditional mean of the cycle, which can be evaluated by importance sampling, offers the required quantification. We assess the validity of the two methods by comparing the results with a standard quantification based on the balance of opinions and with a quantitative economic indicator. Copyright ? 2010 John Wiley & Sons, Ltd.     

A partial elucidation of the gauge principle

The elucidation of the gauge principle “is the most pressing problem in current philosophy of physics” said Michael Redhead in 2003. This paper argues for two points that contribute to this elucidation in the context of Yang–Mills theories. (1) Yang–Mills theories, including quantum electrodynamics, form a class. They should be interpreted together. To focus on electrodynamics is potentially misleading. (2) The essential role of gauge and BRST symmetries is to provide a local field theory that can be quantized and would be equivalent to the quantization of the non-local reduced theory. If this is correct, the gauge symmetry is significant, not so much because it implies ontological consequences, but because it allows us to quantize theories that we would not be able to quantize otherwise. Thus, in the context of Yang–Mills theories, it is essentially a pragmatic principle. This does not seem to be the case for the gauge symmetry in general relativity.     

A unified view of statistical forecasting procedures

A large number of statistical forecasting procedures for univariate time series have been proposed in the literature. These range from simple methods, such as the exponentially weighted moving average, to more complex procedures such as Box–Jenkins ARIMA modelling and Harrison–Stevens Bayesian forecasting. This paper sets out to show the relationship between these various procedures by adopting a framework in which a time series model is viewed in terms of trend, seasonal and irregular components. The framework is then extended to cover models with explanatory variables. From the technical point of view the Kalman filter plays an important role in allowing an integrated treatment of these topics.     

Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank

The main focus of this paper is to model the daily series of banknotes in circulation. The series of banknotes in circulation displays very marked seasonal patterns. To the best of our knowledge the empirical performance of two competing approaches to model seasonality in daily time series, namely the ARIMA‐based approach and the Structural Time Series approach, has never been put to the test. The application presented in this paper provides valid intuition on the merits of each approach. The forecasting performance of the models is also assessed in the context of their impact on the liquidity management of the Eurosystem. Copyright © 2008 John Wiley & Sons, Ltd.     

An interpretation of the non-symmetric responding logistic model in terms of price and experience effects

This paper presents an alternative derivation and a generalization of the non-symmetric responding logistic model of Easingwood, Mahajan and Muller (1981) based upon a combination of experience curve and price elasticity effects.     

Localization and the interface between quantum mechanics, quantum field theory and quantum gravity II: The search of the interface between QFT and QG

The main topics of this second part of a two-part essay are some consequences of the phenomenon of vacuum polarization as the most important physical manifestation of modular localization. Besides philosophically unexpected consequences, it has led to a new constructive “outside-inwards approach” in which the pointlike fields and the compactly localized operator algebras which they generate only appear from intersecting much simpler algebras localized in noncompact wedge regions whose generators have extremely mild almost free field behavior.Another consequence of vacuum polarization presented in this essay is the localization entropy near a causal horizon which follows a logarithmically modified area law in which a dimensionless area (the area divided by the square of dR where dR is the thickness of a light-sheet) appears. There are arguments that this logarithmically modified area law corresponds to the volume law of the standard heat bath thermal behavior. We also explain the symmetry enhancing effect of holographic projections onto the causal horizon of a region and show that the resulting infinite dimensional symmetry groups contain the Bondi–Metzner–Sachs group. This essay is the second part of a partitioned longer paper.