bluuebunny/crossref_metadata_2025 huggingface dataset:
Learning to Build a Supply Network: An Exploration of Dynamic Business Models
Authors: Mason Katy J. , Leek Sheena
Published: May 2008

abstract  Firms are confronted with the challenge of learning how to develop and manage supply networks, which reduce their operating costs and maximize their effectiveness in the marketplace. In pursuit of such goals they are increasingly turning to the use of dynamic business models. Dynamic business models represent continuous change and therefore make firms learn constantly new and better ways of doing things. These changes are manifestations of inter‐firm knowledge transfer. The aim of this research is to explore dynamic business models as an example of inter‐firm knowledge transfer. Adopting a case study approach, we examine three components of dynamic business models – network structure, inter‐firm routines and knowledge forms – and describe their integration through a problem solving approach to building an offshore supply network. Our empirical findings suggest that dynamic business models help organizations identify and link key actors with each other (at the firm and individual level), and aid the identification and specification of appropriate knowledge types and knowledge transfer mechanisms for different actors, in different contexts.
Inter‐Organizational Knowledge Transfer: Current Themes and Future Prospects
Authors: Easterby‐Smith Mark , Lyles Marjorie A. , Tsang Eric W. K.
Published: May 2008

abstract  Many papers have been published recently in the fields of strategy and international business research incorporating the role of organizational knowledge as a basis of firm competitive advantage. While such knowledge is normally developed within the firm, it is important that firms possess the ability to learn from others in order to meet the increasing pace of competition. Knowledge transfer, defined here as an event through which one organization learns from the experience of another, has thus become an important research area within the broader domain of organizational learning and knowledge management. This paper presents a theoretical framework, identifies key themes covered by the six articles included in the Special Issue on Inter‐Organizational Knowledge Transfer, and then discusses priorities for future research.
SERVICE QUALITY IN REGULATED NETWORK INDUSTRIES
Authors: WADDAMS PRICE Catherine , BRIGHAM Bitten , FITZGERALD Lin
Published: May 2008

ABSTRACT**:  Economic regulators provide incentives for good quality of service as well as constraints on the prices or revenue which can be charged by firms with monopoly power. Economic theory suggests that regulators should choose standards according to consumers' valuation and the marginal cost of quality improvements, and that firms respond by equalizing the marginal costs from not making improvements (i.e. the regulatory penalty plus any loss in revenue) with the marginal costs of improvement. This paper explores the evidence for such economically rational behaviour by both regulators and regulatees. We use a specially constructed data set on service quality targets and achievements across the main UK utility sectors; documentary evidence from regulators; and interviews with managers in companies subject to those regulators. We conclude that regulators are motivated by political as well as economic factors. And that companies may not respond primarily to the regulator's financial rewards or penalties for their quality targets, with a consequent danger that regulated consumers pay for marketing in unregulated markets; the resulting level of service quality may be ‘too high’ in the economic sense.
Testing for differences in benefit transfer values between state and regional frameworks
Authors: Rolfe John , Windle Jill
Published: May 2008

Policy makers are often interested in transferring non‐market estimates of environmental values from a ‘source’ study to predict economic values at a ‘target’ site. While most applications of the benefit transfer process involve an opportunistic search for suitable source studies, there are some examples available of more systematic approaches to developing a framework of values for benefit transfer processes. A key issue in developing such a framework is to deal with adjustment factors, where value estimates might vary systematically according to the context of the trade‐offs. Previous research has identified that large differences in scope, such as between national and regional contexts, do affect values and hence benefit transfer. The research reported in this paper indicates that such differences are not significant for smaller scope variations, such as between state and regional contexts. These results provide some promise that systematic databases for benefit transfer can be developed.
Enhancing Discontinuous Innovation through Knowledge Combination: The Case of an Exploratory Unit within an Established Automotive Firm
Authors: Mahmoud‐Jouini Sihem Ben , Charue‐Duboc Florence
Published: May 2008

The literature on innovation management underlines the necessity to separate the exploratory unit that builds new businesses on the basis of radical innovation from the exploitation unit that emphasizes continuous improvement. However, little research focuses on the exploratory unit in itself: the very nature of its activity, its composition, etc. The aim of this article is to analyse the exploratory unit in mobilizing results highlighted by research on organizational creativity. It is argued that in order to enhance discontinuous innovation, knowledge combination should occur and be facilitated in the exploratory unit. Hence, the research question is what organizational design at a fine‐grained level and creativity processes are likely to enhance knowledge combination and thus discontinuous innovation? Based on an in‐depth study of an exploratory unit created in an established multidivisional firm pursuing the development of discontinuous innovation and which generated several actual breakthroughs, we highlighted four key factors that enhanced knowledge combination: (i) the definition of the scope of the unit, (ii) the composition of the unit and the dual roles of its members, (iii) the boundary objects that supported the interactions between these members during the creativity process, and (iv) the arenas where new knowledge was further created.
Direct Employee Involvement Quality (DEIQ)
Authors: Torka Nicole , Van Woerkom Marianne , Looise Jan‐Kees
Published: May 2008

This paper focuses on one aspect of human resource management (HRM) that is important for innovative employee behaviour: direct employee involvement quality (DEIQ). However, research has also shown that employee involvement is often in serious need of improvement. This paper presents evidence from three manufacturing companies in the Netherlands. Semi‐structured interviews were conducted with low‐ and medium‐educated blue‐collar workers, top managers (HRM, production and R&D) and direct supervisors. Using a qualitative approach, we were able to acquire insight into the ‘black‐box’ of well‐known pre‐conditions and contribute by identifying additional dimensions. The research supports various pre‐conditions critical for successful DEIQ including the critical role of the HR function and adequate preparation of direct supervisors. Furthermore, an under‐explored issue is highlighted: the employment relationship.
ROBUSTNESS AND THE NEW RIDDLE REVIVED
Authors: Roskies Adina L.
Published: May 2008

The problem of induction is perennially important in epistemology and the philosophy of science. In response to Goodman's ‘New Riddle of Induction’, Frank Jackson made a compelling case for there being no new riddle, by arguing that there are no nonprojectible properties. Although Jackson's denial of nonprojectible properties is correct, I argue here that he is mistaken in thinking that he thereby shows that there is no new riddle of induction, and demonstrate that his solution to the grue paradox fails to rule out the possibility of equally justified contradictory inductions. More importantly, in illuminating where Jackson's argument fails, the paper casts a new light on the problem of induction, locating the problem not in the nature of the next (unexamined) x, but in the counterfactual robustness of properties of already examined x's.
TIME IS OF THE ESSENCE? AN EMPIRICAL APPLICATION OF THE COROLLARY OF THE COASE THEOREM
Authors: Lai Lawrence W.C. , Wong S.K. , Ho Eric C.K. , Chau K.W.
Published: May 2008

Applying the corollary of the Coase Theorem to the development market developed in Lai et al. (2007a), we evaluate the proposition that procedural steps to use resources are not contingent on property rights assignment, unless the associated transaction costs are positive or property rights are ambiguous. Using aggregate statistics regarding planning applications for residential use, property prices, construction costs, share prices, interest rates, and application success rates in Hong Kong from 1985 to 2005, we evaluated, using regression techniques, the null hypothesis that there was no change in the relationship between readiness for submission of planning applications and property prices in response to changes in the time limits imposed on planning permissions. The relationship was more pronounced or strengthened (less obvious or weakened) when time limits were first imposed and shorter (longer). This can be explained in terms of the transaction costs of switching resource allocation according to the time limit.
MODELING REGIONAL ECONOMIC GROWTH WITH A FOCUS ON AMENITIES
Authors: Deller Steven C. , Lledo Victor , Marcouiller David W.
Published: May 2008

While the literature is clear that areas endowed with scenic beauty, lakes, forests, and wildlife among other natural and built amenities are experiencing robust economic growth, the theoretical foundations for this work are weak. In this study, we employ a Bayesian Modeling Average (BMA) approach to address the problem of model specification. Using data for US counties, we estimate a neoclassical growth model while looking at growth over the 1990s. We look at three separate ways of measuring amenities and recreational opportunities that build on those amenities. While the results suggest that higher amenity areas experience faster growth, still some level of value‐added development may be required to realize that growth.
Scale development to measure attitudes toward unauthorized migration into a foreign country
Authors: VAN DER VEER KEES , OMMUNDSEN REIDAR , KRUMOV KRUM , VAN LE HAO , LARSEN KNUD S.
Published: May 2008

This study reports on the development and cross‐national utility of a Likert type scale measuring attitudes toward unauthorized migration into a foreign country in two samples from “migrant‐sending” nations. In the first phase a pool of 86 attitude statements were administered to a sample of 505 undergraduate students in Bulgaria (22.5% male; M age = 23, SD = 4.8). Exploratory factor analysis resulted in six factors, and a reduction to 34 items. The results yielded an overall alpha of (0.92) and alpha for subscales ranging from 0.70 to 0.89. In the second phase the 34‐item scale was administered in a survey of 180 undergraduates from Sofia University in Bulgaria (16.7% male, M age = 23, SD = 4.8), plus 150 undergraduates from Hanoi State University in Vietnam (14.7% male, M age = 19, SD = 1.8). Results yielded a 19‐item scale with no gender differences, and satisfactory alpha coefficients for a Vietnamese and Bulgarian sample of 0.87 and 0.89 respectively. This scale, equally applicable in both samples, includes items that reflect salient topics of concept of attitudes towards unauthorized migration. An exploratory principal component analysis of the Bulgarian and Vietnamese version of the 19‐item scale yielded three factors accounting for 54% and 47% of the variance respectively. A procrustes analysis indicates high conceptual equivalence in the two samples for factor 1 and 2, and moderate for factor 3. This study lends support to the idea that despite different cultural meanings there is a common meaning space in culturally diverse societies.
Age‐related changes of phasic heart rate responses to affective pictures
Authors: AUPÉE ANNE‐MARIE , JÖNSSON PETER
Published: May 2008

This study examined age differences in phasic heart rate in response to neutral, negative and positive pictures. Heart rate changes and subjective ratings were analyzed in 22 middle‐aged (40–55 years) and 30 older (56–78 years) participants. The effects of valence on the HR pattern across time were similar to that obtained by Bradley and co‐workers. Conversely to previous studies, we did not report any age‐related reduction in cardiac reactivity. Instead, when viewing positive pictures, the triphasic wave form appeared in the group of older adults, but for younger participants, it was replaced by a sustained deceleration. These results were interpreted in the light of the socioemotional selectivity theory.
An investigation into the emotional reactions to inclusion of Ghanaian mainstream teachers
Authors: GYIMAH EMMANUEL KOFI , SUGDEN DAVID , PEARSON SUE
Published: May 2008

Literature is replete with evidence of considerable pressure that many mainstream teachers may experience in their bid to respond to the diverse needs of children with special educational needs and disabilities and to achieve ever better results. In this study, the results of 100 teachers from mainstream primary schools in three of the ten regions of Ghana were examined. The analysis involved five bi‐polar emotional reactions; namely: anxious/relaxed; encouraged/discouraged; confident/diffident; satisfied/dissatisfied; self‐assured/ worried. The results confirmed that in teaching children with SEN in the mainstream, teachers experienced psychological stress. On the basis of the findings, suggestions for more information about SEN, supply of resources and inter‐agency collaboration were made.
SPECTRAL RATIO DISCRIMINANTS AND INFORMATION THEORY
Authors: Dargahi‐Noubary G. R. , Laycock P. J.
Published: May 2008

Abstract.We examine the problem of discriminating in the frequency domain between zero mean stationary Gaussian processes. Use of the Kullback‐Liebler information measure enables the selection of the important frequency bands, and a special case relevant to seismic records leads to a justification for the use of spectral ratios in discrimination. Methods are given for combining the ratios when there are more than two relevant bands, and for estimating the error rates when using spectral ratios. The results are extended to a special class of non‐stationary models.
CONFIDENCE INTERVALS FOR ROBUST ESTIMATES OF THE FIRST ORDER AUTOREGRESSIVE PARAMETER
Authors: Birch Jeffrey B. , Martin R. Douglas
Published: May 2008

Abstract. The confidence interval properties of several estimators of the transition parameter, φ, in the first order autoregressive model are examined by a Monte Carlo study. The least squares confidence interval estimator, as well as two forms of a proposed robust confidence interval based on a generalized M‐estimator, are examined under two model alternatives to the classical time series approach: the innovations model (the time series is observed ‘perfectly’) and the additive effects model (the time series is observed plus an added ‘effect’). Samples were generated from a number of symmetric distributions, including the Gaussian and a variety of contaminated distributions with mild to heavy contamination. Over a range of outlier models, values of φ (.25 to.9), and sample sizes (20 to 100), it was found that the GM‐estimators possess desirable confidence interval robustness properties in terms of validity and efficiency. In general, the least squares confidence interval is not robust against symmetric heavy‐tailed contamination in the innovations model or against the additive effects model.
AN INVESTIGATION OF ANDREWS‘ PLOTS TO SHOW TIME VARIATION OF MODEL PARAMETERS
Authors: Herzberg Agnes M. , Hickie J. S.
Published: May 2008

Abstract.Andrews (1972) introduced a method of plotting high‐dimensional data in two dimensions. This method is exploited as a graphical technique for the examination of changes over time in the parameters of a time series model. Two sets of data are used to illustrate the method. Both polynomial and Fourier series models are used. Some statistical properties of the method are discussed.
NONLINEAR TIME SERIES REGRESSION FOR A CLASS OF AMPLITUDE MODULATED CONSINUSOIDS
Authors: Hasan T.
Published: May 2008

Abstract. A central limit theorem is proved for estimates of the unknown parameters in a time series which is a sum of amplitude modulated consinusoids observed subject to error. The amplitude function depends upon unknown parameters as well as the length of the series. The frequency for each cosinusoid is also assumed to be unknown. Estimates and standard errors are obtained through nonlinear least squares in the frequency domain.
COMPONENTS OF PREDICTION ERRORS FOR A STATIONARY PROCESS WITH ESTIMATED PARAMETERS
Authors: Fotopoulos S. B. , Ray W. D.
Published: May 2008

Abstract.An alternative approach to the estimation of prediction error using linear time series models, whose parameters are also estimated, is presented. Recurrence relationships are given which are rather less unwieldy operationally than the Kronecker matrix forms used by Yamamoto and Reinsel. We extend somewhat the class of models to other than the purely finite autoregressive models studied by these authors.
GENERALIZED SEASONAL ARIMA PROCESSES: REGULARITY/SINGULARITY CRITERIA AND LINEAR PREDICTION
Authors: Truong‐Van B.
Published: May 2008

Abstract. Criteria for any generalized seasonal ARIMA model to be a regular or to be a singular process are given and a new basic form of predictors for ARIMA processes is obtained, that can be computed in a simple way.
A METHOD FOR DIAGNOSTIC CHECKING OF TIME SERIES MODELS
Authors: Hokstad Per
Published: May 2008

Abstract. Suppose a tentative ARMA (p, q)‐model has been fitted to a stationary time series. A diagnostic check for this model is suggested, using the estimated cross correlation function (CCF) between the observed series and the residuals. The CCF may also indicate how the model can be improved. The method is applied to the Wolfer sunspot series. For AR (p)‐processes the asymptotic covariance matrix of the estimated cross correlations is obtained.
A NOTE ON APPROXIMATING THE DISTRIBUTION OF THE DURBIN‐WATSON STATISTIC
Authors: Ali Mukhtar M.
Published: May 2008

Abstract. This note considers approximating the distribution of the d statistic proposed by Durbin and Watson (1950,1951) by four‐parameter Pearson distribution. It is shown that the approximations are remarkably close to the exact distributions for four sets of published data considered by Durbin and Watson (1971).
MULTIPLICATIVE EXPONENTIAL MODELS FOR STATIONARY TIME SERIES
Authors: Milhøj Anders
Published: May 2008

Abstract. A class of models for one dimensional time series is presented. The spectrum of such a model is obtained by raising the spectrum of a known parameterized model to an exponent, allowed to attain arbitrary real values. For a moving average model this for example means that the roots of the moving average operator are allowed to have any real order. This method adds a further flexibility to the model which for example allows us to model long memory time series using only a few parameters. The exponent is parameterized in a special way to make the estimation of the parameter determining the exponent asymptotically independent of the estimation of the other model‐parameters. The asymptotic distribution of the estimators is derived. The idea is also used for multiplicative models with an exponent for each seasonal factor. In this case the estimators are only approximately independent for a large season length. Finally an application of the model is given using the Beveridge wheat price index.
ARMA MODELS WITH ARCH ERRORS
Authors: Weiss Andrew A.
Published: May 2008

Abstract.This paper considers the class of ARMA models with ARCH errors. Maximum Likelihood and Least Squares estimates of the parameters of the model and their covariance matrices are noted and incorporated into techniques for model building based upon the application of the usual Box‐Jenkins methodology of identification, estimation and diagnostic checking to the ARMA equation, the ARCH equation, and the full model. The techniques are applied to 16 U.S. macroeconomic time series and it is seen that in many of the series, models from this class can be constructed.
A UNIFIED APPROACH TO THE STUDY OF SUMS, PRODUCTS, TIME‐AGGREGATION AND OTHER FUNCTIONS OF ARMA PROCESSES
Authors: Engel E. M. R. A.
Published: May 2008

Abstract.Conditions under which sums, products and time‐aggregation of ARMA processes follow ARMA models are derived from a single theorem. This characterizes these processes in terms of difference equations satisfied by their autocovariance function. From this we obtain necessary and sufficient conditions for a function of a Gaussian ARMA process and the product of two possibly dependent Gaussian ARMA processes to be ARMA. We show that the sum and product of two ARMA processes related by a Box and Jenkins transfer function model belong to the ARMA family.
RANDOM COEFFICIENT AUTOREGRESSIVE PROCESSES:A MARKOV CHAIN ANALYSIS OF STATIONARITY AND FINITENESS OF MOMENTS
Authors: Feigin Paul D. , Tweedie Richard L.
Published: May 2008

Abstract. Simple yet practically efficient conditions for the ergodicity of a Markov chain on a general state space have recently been developed. We illustrate their application to non‐linear time series models and, in particular, to random coefficient autoregressive models.

As well as ensuring the existence of a unique stationary distribution, geometric rates of convergence to stationarity are ensured. Moreover, sufficient conditions for the existence and convergence of moments can be determined by a closely related method. The latter conditions, in particular, are new.
CONDITIONING IN DYNAMIC MODELS
Authors: Florens J.‐P. , Mouchart M.
Published: May 2008

Abstract. A statistical model is generally defined through a probability on some variables conditionally on other variables and refers to some parameters of interest. Therefore, it seems natural to ask under which conditions such a model does not lose information with respect to a model describing more variables and implying more parameters. Admissibility conditions for reductions by conditioning are investigated both in one‐shot and in dynamic models. By so doing, concepts of ‘exogeneity’ and of ‘non‐causality’ are integrated into a general framework. This paper is essentially a non‐technical introduction to the theory of reduction developed more formally in other papers. It also supplies various examples of the concepts introduced in that theory.
ASYMPTOTIC PROPERTIES OF THE SAMPLE AUTOCORRELATIONS AND PARTIAL AUTOCORRELATIONS OF A MULTIPLICATIVE ARIMA PROCESS
Authors: Yajima Yoshihiro
Published: May 2008

Abstract. We shall investigate the asymptotic behaviour of the sample autocorrelations and partial autocorrelations of a multiplicative ARIMA process and derive their limiting distributions. Some simulations are presented to illustrate the results obtained.
TESTS FOR COMPARING TWO ESTIMATED SPECTRAL DENSITIES
Authors: Coates D. S. , Diggle P. J.
Published: May 2008

Abstract. This paper was motivated by a problem in the gas industry and describes a number of periodogram‐based tests of the hypothesis that two independent time‐series are realizations of the same stationary process. Non‐parametric tests analogous to the maximum periodogram ordinate and cumulative periodogram tests for white noise are compared with a likelihood ratio test based on a postulated quadratic model for the log spectral ratio. The latter is found to be generally more powerful against alternatives in which the two series are realizations of different low order AR processes. The operation of the likelihood ratio test is illustrated by two sets of data, the classic Beveridge wheat price series and a set of data supplied by British Gas.
REGRESSION, AUTOREGRESSION MODELS
Authors: Hannan E. J. , Kavalieris L.
Published: May 2008

Abstract.The accuracy of least squares fitted regression autoregression models as approximations to more general stochastic structures is considered, attention being paid to the accuracy of the estimates of coefficients, of the innovations sequence and to the behaviour of the order (i.e., maximum lag) as determined by methods such as IAC, BIC. A key part is played by an accurate evaluation of the quantity where ετ(t) is the estimated innovation sequehce from anhTth‐order regression, autoregression. Attempts are made to attain results near to the best possible and to establish almost sure convergence rates.
A FREQUENCY DOMAIN APPROACH TO LAGRANGE MULTIPLIER TEST FOR AUTOREGRESSIVE MOVING AVERAGE MODELS
Authors: Tuan Pham Dinh
Published: May 2008

Abstract. The paper derives a goodness of fit test for autoregressive moving average models using the frequency domain approximation to the log likelihood and the Lagrange multiplier approach. The test statistic is based on the sample autocovariances and can be quickly computed through a recursive procedure.
THE CRITERION AUTOREGRESSIVE TRANSFER FUNCTION OF PARZEN
Authors: Bhansali R. J.
Published: May 2008

Abstract.A mathematical derivation of the Criterion Autoregressive Transfer Function (CAT) of Parzen (1974) is given and a generalization of this criterion is introduced. The asymptotic distribution of the orders selected by CAT, this generalized criterion, and a new version of CAT introduced by Parzen (1977a) are derived. The joint finite sample behaviour of these three criteria and of the FPEα,‐criterion of Bhansali and Downham (1977) is examined by means of a simulation study.
ON THE CONSISTENCY OF LEAST SQUARES ESTIMATORS FOR A THRESHOLD AR(1) MODEL
Authors: Petruccelli Joseph D.
Published: May 2008

Abstract. For the SETAR (2; 1,1) model

where {at(i)} are i.i.d. random variables with mean 0 and variance σ2(i), i = 1,2, and {at(l)} is independent of {at(2)}, we consider estimators of φ1, φ2 and r which minimize weighted sums of the sum of squares functions for σ2(1) and σ2(2). These include as a special case the usual least squares estimators. It is shown that the usual least squares estimators of φ1, φ2 and r are consistent. If σ2(1) ≠σ2(2) conditions on the weights are found under which the estimators of r and φ1 or φ2 are not consistent.
LINEAR AND QUADRATIC SERIAL RANK TESTS FOR RANDOMNESS AGAINST SERIAL DEPENDENCE
Authors: Hallin Marc. , Ingenbleek Jean‐François , Puri Madan L.
Published: May 2008

Abstract. The problem of testing randomness against specified and unspecified contiguous ARMA alternatives is considered. In the case of specified alternative, the linear serial rank tests proposed by the authors (Hallin, Ingenbleek and Puri 1985) are shown to be asymptotically most powerful within the class of all possible tests (at the required level). In the case of unspecified alternative, however, any of the above optimal tests is unfortunately completely insensitive against a whole subclass of the alternative. Quadratic serial rank statistics, providing tests of the χ2‐type are therefore introduced. Their asymptotic distributions are derived, under the null hypothesis as well as under contiguous ARMA alternatives. The asymptotically maximin most powerful quadratic serial rank tests (for a given density) are then obtained. Because of their close similarity with the Box‐Pierce parametric test, we call them rank portmanteau tests. The asymptotic relative efficiencies (AREs) of the rank portmanteau tests with respect to one another and their AREs with respect to the corresponding Box‐Pierce and quadratic Spearman tests are derived.
SLOWLY CHANGING PROCESSES AND HARMONIZABILITY
Authors: Joyeux Roselyne.
Published: May 2008

Abstract.It is shown that oscillatory sequences and slowly changing processes are strongly harmonizable. Their theoretical harmonizable spectra are also derived.
EXPERIENCES WITH THE BRILLINGER SPECTRAL ESTIMATOR APPLIED TO SIMULATED IRREGULARLY OBSERVED PROCESSES
Authors: Moore Mike I. , Visser Andy W. , Shirtcliffe Tim G. L.
Published: May 2008

Abstract.Shannon interpolation is used to assign values from a readily simulated discrete time process to the times of a point process, simulated by Ogata's thinning technique. The result is a set of unequally spaced samples from a hypothetical continuous time process with spectrum equal to that of the discrete time process for frequencies |ω| ≤π/Δ and identically equal to zero for |ω| > π/Δ, where Δ is the discrete time step. The spectra are theoretically known both for the sampled process and for the sampling point process. We calculate Brillinger spectral estimates for examples of a process with autoregressive spectrum, sampled at the times of a Hawkes Self Exciting Point Process. The success of the Brillinger estimator is demonstrated but it is shown to have an inherently high variance. An approximate confidence interval is discussed.
ASYMPTOTIC SIMULTANEOUS CONFIDENCE BANDS FOR AUTOREGRESSIVE SPECTRAL DENSITY
Authors: Tomášek Ladislav.
Published: May 2008

Abstract.On the basis of the asymptotic behaviour of autoregressive parameter estimates, the properties of autoregressive spectral density estimates are deduced. The simultaneous confidence bands for the spectra are derived using Šidák's inequality. Simulation experiments are used to illustrate the quality of the derived confidence bands.
DETERMINING THE BANDWIDTH OF A KERNEL SPECTRUM ESTIMATE
Authors: BeltraTo Kaizô I. , Bloomfield Peter
Published: May 2008

Abstract. A cross‐validated form of Whittle's frequency domain approximation to the likelihood function of a stationary Gaussian process is described, and proposed as a criterion for choosing the bandwidth in a kernel spectrum estimate. The criterion is shown to be equivalent, in large samples, to the mean integrated squared error. The statistical properties of the spectrum estimate whose bandwidth maximizes the criterion have been explored in a limited simulation.
THE ASYMPTOTIC PROPERTIES OF THE SAMPLE AUTOCORRELATIONS FOR A MULTIPLE AUTOREGRESSIVE PROCESS WITH ONE UNIT ROOT
Authors: Samaranayake V. A. , Hasza David P.
Published: May 2008

Abstract. In this paper the large sample behaviour of the sample autocorrelation matrix Rn(h), (h being the lag, n the sample size), of a multivariate autoregressive time series with one of its characteristic roots equal to unity and the rest of the roots lying inside the unit circle is studied. It is shown that Rn(h) converges almost surely to a constant matrix. Further, the asymptotic distribution of Rn(h) is characterized as that of a random matrix which is a function of jointly normal random variables.
ESTIMATION OF MULTIVARIATE TIME SERIES
Authors: Shea B. L.
Published: May 2008

Abstract.The algorithm proposed here is a multivariate generalization of a procedure discussed by Pearlman (1980) for calculating the exact likelihood of a univariate ARMA model. Ansley and Kohn (1983) have shown how the Kalman filter can be used to calculate the exact likelihood function when not all the observations are known. In Shea (1983) it is shown that this algorithm is much quicker than that of Ansley and Kohn (1983) for all ARMA models except an ARMA (2, 1) and a couple of low‐order AR processes and therefore when we have no missing observations this algorithm should be used instead. The Fortran subroutine G13DCF in the NAG (1987) Library fits a vector ARMA model using an adaptation of this algorithm. Experience in the use of this routine suggests that having reasonably good initial estimates of the ARMA parameter matrices, and in particular the residual error covariance matrix, can not only substantially reduce the computing time but more important improve the convergence properties of the minimization procedure. We therefore propose a method of calculating initial estimates of the ARMA parameters which involves using a generalization of the concept of inverse cross covariances from the univariate to the multivariate case. Finally theory is put into practice with the fitting of a bivariate model to a couple of real‐life time series.
A PROTOTYPICAL SEASONAL ADJUSTMENT MODEL
Authors: Maravall Agustin , Pierce David A.
Published: May 2008

Abstract. The paper analyses unobserved‐components modelling and estimation for the simplest ARIMA process that accepts a full decomposition into trend, seasonal and irregular components. This prototypical model exemplifies many features of and issues arising in model‐based seasonal adjustment that are less transparent in more complex seasonal time series models. In particular the analysis illuminates the major issues surrounding the specification of the component models and the identification of a unique structure for them. In so doing, the relationship between reduced‐ and structural‐form approaches to unobserved components estimation is illustrated within an ARIMA‐modelling framework. Finally, the properties of the minimum mean‐squared‐error estimators of the unobserved components are examined and the two main types of estimation error, revisions in the preliminary estimator and error in the final estimator, are analysed.
ON RISSANEN'S LOWER BOUND ON THE ACCUMULATED MEAN‐SQUARE PREDICTION ERROR
Authors: Kabaila Paul
Published: May 2008

Abstract. Rissanen (1984) presents a lower bound on the accumulated mean square prediction error for a Gaussian ARMA process. Rissanen proves this result using coding theory. In this paper we show that this bound is related to the Cramer‐Rao bound and Fisher's bound on asymptotic variances, for the case of a Gaussian AR process. This provides some insight into Rissanen's lower bound. Analogues of Rissanen's bound, for nonaccumulated prediction error and a Gaussian AR process, are also presented and discussed.
A FORMULA FOR THE INVERSE AUTOCORRELATION FUNCTION OF AN AUTOREGRESSIVE PROCESS
Authors: Kanto Antti J.
Published: May 2008

Abstract. The determination of the inverse autocorrelation function of a weakly stationary autoregressive process using the autocorrelation function is considered. Usually this is carried out either by using frequency domain methods or by solving first the parameters of the process and then using them. In this paper we give a simple formula by which the inverse autocorrelation function can be determined directly from the autocorrelation function.
ASYMPTOTIC MEAN SQUARE PREDICTION ERROR FOR A MULTIVARIATE AUTOREGRESSIVE MODEL WITH RANDOM COEFFICIENTS
Authors: Ray D.
Published: May 2008

In this paper, an expression for the asymptotic mean square error in predicting more than one step ahead from a p‐variate autoregressive model with random coefficients is derived. Two cases are investigated: (i) when the parameters are known, and (ii) when the parameters are estimated.
NON‐LINEAR TIME SERIES ANALYSIS OF BLOWFLY POPULATION
Authors: Tsay Ruey S.
Published: May 2008

Abstract.In recent years there has been a growing interest in studying non‐linear time series and various non‐linear models have been proposed in the literature. In this paper, I consider non‐linear time series modelling via a case study. Several important issues concerning non‐linear time series models and data analysis emerge from the study.
ON THE LAGRANGE MULTIPLIER TEST FOR AUTOREGRESSIVE MOVING‐AVERAGE MODELS
Authors: Ljung Greta M.
Published: May 2008

Abstract.This paper examines the score or Lagrange multiplier statistic for testing the adequacy of a fitted autoregressive moving‐average model and gives a simple closed‐form expression for this test statistic. Some singularities arising as the order of the alternative model is increased are examined.
A NOTE ON THE GENERATION OF INDEPENDENT REALIZATIONS OF A VECTOR AUTOREGRESSIVE MOVING‐AVERAGE PROCESS
Authors: Shea B. L.
Published: May 2008

Abstract. Barone has described a method for generating independent realizations of a vector autoregressive moving‐average (ARMA) process which involves recasting the ARMA model in state space form. We discuss a direct method of computing the initial state covariance matrix T0 which, unless the number of time series is large, is usually faster than using the doubling algorithm of Anderson and Moore. Our numerical comparisons are particularly valuable because T0 must also be computed when calculating the likelihood function. A number of other computational refinements are described. In particular, we advocate the use of Choleski factorizations rather than spectral decompositions. For a pure moving‐average process computational savings can be achieved by working directly with the ARMA model rather than with its state space representation.
ON GENERALIZED FRACTIONAL PROCESSES
Authors: Gray Henry L. , Zhang Nien‐Fan , Woodward Wayne A.
Published: May 2008

Abstract. A class of stationary long‐memory processes is proposed which is an extension of the fractional autoregressive moving‐average (FARMA) model. The FARMA model is limited by the fact that it does not allow data with persistent cyclic (or seasonal) behavior to be considered. Our extension, which includes the FARMA model as a special case, makes use of the properties of the generating function of the Gegenbauer polynomials, and we refer to these models as Gegenbauer autoregressive moving‐average (GARMA) models. While the FARMA model has a peak in the spectrum at f= 0, the GARMA process can model long‐term periodic behavior for any frequency 0 f0.5. Properties of the GARMA process are examined and techniques for generation of realizations, model identification and parameter estimation are proposed. The use of the GARMA model is illustrated through simulated examples as well as with classical sunspot data.
IDENTIFICATION OF UNOBSERVED COMPONENTS MODELS
Authors: Hotta Luiz Koodi
Published: May 2008

Abstract. Unobserved components ARIMA models are common in time series applications. However, fitting models of this type leads to problems of model identification. In this paper we derive a methodology to check whether a proposed model is identifiable. We show that this kind of identification can be checked using the autocovariance generating function and/or the (pseudo‐)spectral generating function.
A GENERALIZED LEAST‐SQUARES APPROACH FOR ESTIMATION OF AUTOREGRESSIVE MOVING‐AVERAGE MODELS
Authors: Koreisha Sergio , Pukkila Tarmo
Published: May 2008

Abstract.In this paper we present a generalized least‐squares approach for estimating autoregressive moving‐average (ARMA) models. Simulation results based on different model structures with varying numbers of observations are used to contrast the performance of our procedure with that of maximum likelihood estimates. Existing software packages can be utilized to derive these estimates.
A DISTANCE MEASURE FOR CLASSIFYING ARIMA MODELS
Authors: Piccolo Domenico
Published: May 2008

Abstract. In a number of practical problems where clustering or choosing from a set of dynamic structures is needed, the introduction of a distance between the data is an early step in the application of multivariate statistical methods. In this paper a parametric approach is proposed in order to introduce a well‐defined metric on the class of autoregressive integrated moving‐average (ARIMA) invertible models as the Euclidean distance between their autoregressive expansions. Two case studies for clustering economic time series and for assessing the consistency of seasonal adjustment procedures are discussed. Finally, some related proposals are surveyed and some suggestions for further research are made.
STABLE ALGORITHMS FOR THE STATE SPACE MODEL
Authors: Jong Piet De
Published: May 2008

Abstract. Numerically stable algorithms are developed for filtering, likelihood evaluation, generalized least squares computation and smoothing where data are generated by a state space model. The algorithms handle diffuse initial states in a numerically safe way. Singular innovation covariance matrices, such as those which arise in series with missing values, are dealt with. The algorithms generalize stable algorithms for ordinary least‐squares computations.
OPTIMALITY OF THE MAXIMUM LIKELIHOOD ESTIMATOR IN FIRST‐ORDER AUTOREGRESSIVE PROCESSES
Authors: Mikulski Piotr W. , Monsour Michael J.
Published: May 2008

Abstract. Let ρρ* be the maximum likelihood estimator (MLE) of the parameter ρ in the first‐order autoregressive process with normal errors. The problem of optimality in the sense of weighted squared error is considered rather than moments of asymptotic distributions. Many unbiased estimators can be constructed, but the two‐dimensional sufficient statistic is incomplete. It is shown that dEρ* ‐ ρ→ 0 uniformly in ρ, and that dE(ρ*)/dρ→ 1 for all |ρ| > 1. For |ρ| > 1, it is known that the asymptotic distribution of {I(ρ)}12(ρ* ‐ ρ), where I(ρ) is the Fisher information, is Cauchy. It follows that the Cramèr‐Rao inequality will not yield useful results for investigating the limit of exact efficiency of any asymptotically unbiased estimator, including the MLE. For all |ρ| < 1, ρ* is asymptotically optimal in the sense of minimizing expected weighted squared error. In addition, for |ρ| < 1, ρ* minimizes the variance asymptotically.
INITIALIZING THE KALMAN FILTER FOR NONSTATIONARY TIME SERIES MODELS
Authors: Bell William , Hillmer Steven
Published: May 2008

Abstract.The problem of initializing the Kalman filter for nonstationary time series models is considered. Ansley and Kohn have developed a ‘modified Kalman filter’ for use with nonstationary models to produce estimates from what they call a ‘transformation approach’. We show that the same results can be obtained with a suitable initialization of the ordinary Kalman filter. Assuming there aredstarting values for the nonstationary series, we initialize the Kalman filter using data through timedwith the transformation approach estimate of the state vector and its associated error covariance matrix at timed. We give details of the initialization for ARIMA models, ARIMA component models and dynamic linear models. We present an example to illustrate how the results may differ from results obtained under more naive initializations that have been suggested.
RECURSIVE GENERALIZED M ESTIMATES FOR AUTOREGRESSIVE MOVING‐AVERAGE MODELS
Authors: Allende Hector , Heiler Siegfried
Published: May 2008

Abstract.Outliers in time series seriously affect conventional parameter estimates. In this paper a robust recursive estimation procedure for the parameters of auto‐regressve moving‐average models with additive outliers is proposed. Using ‘cleaned’ residuals from an initial robust fit of an autoregression of high order as input, bounded influence regression is applied recursively. The proposal follows certain ideas of Hannan and Rissanen, who suggested a three‐stage procedure for order and parameter estimation in a conventional setting.

A Monte Carlo study is performed to investigate the robustness properties of the proposed class of estimates and to compare them with various other suggestions, including least squares, M estimates, residual autocovariance and truncated residual autocovariance estimates. The results show that the recursive generalized M estimates compare favourably with them. Finally, possible modifications to master even vigourous situations are suggested.
MAXIMUM LIKELIHOOD ESTIMATORS IN THE MULTIVARIATE AUTOREGRESSIVE MOVING‐AVERAGE MODEL FROM A GENERALIZED LEAST SQUARES VIEWPOINT
Authors: Reinsel Gregory C. , Basu Sabyasachi , Yap Sook Fwe
Published: May 2008

Abstract.Explicit expressions are derived for the gradient vector and (approximate) Hessian matrix of the log likelihood function for the multivariate autoregressive moving‐average (ARMA) model. Based on these expressions an explicit description of the Gauss‐Newton iterative procedure to obtain maximum likelihood (ML) estimates of the parameters in the multivariate ARMA model is presented. The resulting computational procedure has the form of a generalized least squares (GLS) estimation involving lagged values of the observed vector series and of the residual series as independent variables. This direct form of the estimator is found to be appealing and useful in understanding and interpreting the ML estimation procedure from a regression point of view, and in comparing the ML procedure with other ‘linear’ estimation procedures that have recently been presented. Simulation results are also presented for a univariate and a multivariate ARMA model to illustrate the ML‐GLS estimation procedure and to compare it with other linear estimation procedures.
BOOTSTRAPPING STATIONARY AUTOREGRESSIVE MOVING‐AVERAGE MODELS
Authors: Kreiss Jens‐Peter , Franke Jürgen
Published: May 2008

Abstract. In this paper we develop an asymptotic theory for application of the bootstrap to stationary stochastic processes of autoregressive moving‐average (ARMA) type, with known order (p, q). We give a proof of the asymptotic validity of the bootstrap proposal applied to M estimators for the unknown parameter vector of the process. For this purpose we derive an asymptotic expansion for M estimators in ARMA models and construct an estimate for the unknown distribution function of the residuals which in principle are not observable. A small simulation study is also included.
APPROXIMATE SIMULTANEOUS SIGNIFICANCE INTERVALS FOR RESIDUAL AUTOCORRELATIONS OF AUTOREGRESSIVE MOVING‐AVERAGE TIME SERIES MODELS
Authors: Hosking J. R. M. , Ravishanker Nalini
Published: May 2008

Abstract.Bonferroni‐type inequalities are used to approximate probabilities of the joint distribution of residual autocorrelation coefficients from an autoregressive moving‐average time series model. The approximations are useful for testing the goodness of fit of the model:they can be used to find critical values of a test of whether the largest residual autocorrelation is significantly different from zero. The approximation based on the first‐order Bonferroni inequality is simple to use and adequate in practice.
A TIME SERIES MODEL WITH SUDDENLY CHANGING PARAMETERS
Authors: Anděl Jiří
Published: May 2008

Abstract. A process Xt=θt+e, is investigated where et is a strict white noise and θt is a Markov chain with two real states. A realization of Xt fluctuates around two levels which correspond to the two states of θt. Formulae for the extrapolation of the process Xt and for the mean square error of the extrapolation are derived. The moments of Xt and its covariance function are calculated. The results are used to derive moment estimators for the parameters of the model.
NON‐STATIONARY AUTOREGRESSIVE MOVING‐AVERAGE PROCESSES WITH INFINITE VARIANCE
Authors: Nassiuma Dankit
Published: May 2008

Abstract.In this paper we discuss some properties of non‐stationary autoregressive moving‐average processes with Λ‐stationary (0 < Λ < 2) stable innovations. Linear forecasts and the dispersion of theh‐steps‐ahead forecast error are obtained and some examples are given. The problem of parameter estimation is discussed.
THE DISTRIBUTION OF NONSTATIONARY AUTOREGRESSIVE PROCESSES UNDER GENERAL NOISE CONDITIONS
Authors: Spall James C.
Published: May 2008

Abstract. In this paper we consider the long‐run distribution of a multivariate autoregressive process of the form xn=An‐1xn‐1+ noise, where the noise has an unknown (possibly nonstationary and nonindependent) distribution and An is a (generally) time‐varying transition matrix. It can easily be shown that the process xn need not have a known long‐run distribution (in particular, central limit theorem effects do not generally hold). However, if the distribution of the noise approaches a known distribution as n gets large, we show that the distribution of xn may also approach a known distribution for large n. Such a setting might occur, for example, when transient effects associated with the early stages of a system's operation die out. We first present a general result that applies for arbitrary noise distributions and general An. Several special cases are then presented that apply for noise distributions in the infinitely divisible class and/or for asymptotically constant coefficient An. We illustrate the results on a problem in characterizing the asymptotic distribution of the estimation error in a Kalman filter.