Principles of Forecasting: A Handbook for Researchers and Practitioners, by J. Scott Armstrong is now available from Kluwer. It uses knowledge fromexperts and empirical studies to provide forecasting principles. The 30chapters cover all types of forecasting methods: judgmental, such as Delphi,role-playing, and intentions; and quantitative, such as conjoint analysis,econometric methods, expert systems, and extrapolation. There is anintroductory price that is good to August 31. Details at

Data Mining on Time Series: An Illustration Using Fast-Food Restaurant Franchise Data, by Liu, L.-M, Bhattacharyya, S., Sclove, S., Chen, R., Lattyak, W. (2001) is pending publicationin Computational Statistics and Data Analysis. Given the widespread use of modern information technology, a large number of time series may be collected during normal business operations. We use a fast-food restaurant franchiseas a case to illustrate how data mining can be applied to such time series, and help the franchise reap the benefits of such an effort. Time series data mining at both the store level and corporate level are discussed. Box-Jenkins seasonal ARIMA models are employed to analyze and forecast the time series. Instead of a traditional manual approach of Box-Jenkins modeling, an automatic time series modeling procedure is employed to analyze a large number of highly periodic time series. In addition, an automatic outlier detection and adjustment procedure is used for both model estimation and forecasting. The improvementin forecast performance due to outlier adjustment is demonstrated. Adjustment of forecasts based on stored historical estimates of like-events is also discussed. Outlier detection also leads to information that can be used not only for better inventory management and planning, but also to identify potential sales opportunities. To illustrate the feasibility and simplicity of the above automatic procedures for time series data mining, the SCA Statistical System is employed to perform the related analysis.

Keywords: Automatic time series modeling, Automatic outlier detection, Outliers, Forecasting, Expert system, Knowledge discovery

A Multiple-Input Transfer Function Model of Okun's Misery Index: An Empirical Test of the Maharishi Effect by Cavanaugh, K., King, K.D., Ertuna, C., was a presented paper at the 1989 meeting of the American Statistical Association. This paper analyzes the time seriesbehavior of Okun's economic "misery index" of inflation and unemployment, defined as the sumof the inflation rate and unemployment rate. We identify and estimate a multiple-inputtransfer function model of the monthly misery index for the U.S. during the period 1979 to1988 using Liu's (1985) Linear Transfer Function (LTF) method supplemented by the use of Akaike Information Criterion (AIC) to provide an objective criterion of model selection.Maximum likelihood estimates of the model are used to further test a novel hypothesis suggested by a new field-theoretic pardadigm of consciousness and socioeconomic behaviorproposed by Maharishi Mahesh Yogi. The results of this study suggest that the substantialimprovement in this index of economic performance and economic quality of life over this period was significantly influenced by the collective practice of a subjective technologyof consciousness, the Transcendental Meditation and TM-Sidhi program. Controlling for theeffects of monetary growth, growth in crude materials prices, and the rate of change ofindustrial production, the null hypothesis of no effect of the TM and TM-Sidhi group on the misery index must be decisively rejected for these data, thus lending strong supportto the Maharishi effect hypothesis.

Keywords: Multiple time series, Inflation, Unemployment, AIC, TranscendentalMeditation Program

An Approach To Detection and Treatment Of Outliers in Multiple Time Seriesby Pankratz, A. was presented in as a poster session at the 1992 meeting of the AmericanStatistical Association. This paper offers a first approach to the detection of outliersin multiple time series by examining the case of transfer function plus autocorrelated disturbance, with one imput described by an ARIMA process. Special attention is given to effects on the output series of outliers arising in the input series. These effects mayor may not be passed through the transfer function to the output. I extend a single-seriesmethod to include multivariate least squares estimates to detect certain multiple seriesoutliers. I illustrate using a real data set.

Keywords: ARIMA, Dynamic regression, Intervention, Multiple time series, Multivariate restricted least squares, outliers, transfer function.

A (Relatively) Simple Sectoral Employment Level Forecasting Model For Small Regional Economies by Weller, B. was presented at the 36th North American Meetings of the Regional Science Association. This paper illustrates the use of a small multi-input/multi-equation transfer function model to generate a set of internallyconsistent regional employment as well as employment in two major sub-sectors of theeconomy, specifically manufacturing and non-manufacturing. In addition to providinginternally consistent forecasts, the model is relatively easy to develop since it usesreadily available national aggregates as drivers. Values for the driver variables are, themselves, forecast using univariate ARIMA models. Thus, the model is self-contained, requiring no user supplied forecasts of the input variables. Further, the model is moreintuitively appealing than pure time series approaches since it incorporates some aspectsof causality. Post-fit tests of forecast accuracy, spanning intervals of widely varyingdegress of instability, indicate the multiple-equation transfer function model outperformsunivariate sectoral forecasting models over horizons ranging from one through twelvemonths. Altough most pronounced during periods of continuity or relative stability, theaccuracy advantage of te multi-equation model is apprarent regardless of whether theforecast interval is highly unstable or relatively stable.

Keywords: Employment forecasting, Leading indicators, Multi-equation transfer function models, Regional forecasting.

A Statistical Assessment of the Effect of theCar Inspection/Maintenance Program on Ambient CO Air Quality in Phoenix, Arizona by Tiao, G.C., Liu, L.-M., Hudak, G. appeared in Environmental Science & Technology inJuly 1989. As mandated by Clean Air Act Ammendments, several states have implementedcar inspection and maintenance (I/M) programs to reduce automobile emission. This paperinvestigates such a program on the ambient CO concentrations in Phoenix, AZ. Empiricalmodels are formulated to describe the observed concentrations as a function of trafficand meteorological variables. These models help determine the existence of any effects due to the I/M program after adjusting for changes in traffic and meteorologicalconditions. In particular, by comparing our empirical results with emission factorsderived from MOBILE3 analysis, we find some evidence to support the hypothesis that theI/M program has reduced ambient CO levels.

Water treatment control using the joint estimation outlier detection methodby Christine Wright and David Booth appeared in Environmental Modeling and Assessment 6:77-82, 2001. In many industries, it is important to determine when the process is out-of-control, (i.e., when significant adverse process changes occur). The idea is to discover theseadverse process changes while they are still relatively minor, before substandard productor significant pollution is produced. One example of an important chemical process controlproblem is wastewater data. The objective of the research was to determine if out-of-controlobservations (i.e., abnormal states) could be detected by JE in the period when they firstoccurred. The process control method reported herein can be used for any compound for whichan analytical chemical detection method exists. The method that we consider, Joint Estimation(JE), has the potential to be extremely important to both general pollution control and statistical process control.

Effectiveness of Joint Estimation When the Outlier Is the Last Observationin an Autocorrelated Short Time Series by Wright, C., Hu, M.Y., and Booth, D.E., appeared in Decision Sciences (1999), Volume 30, Number 3. The Effectiveness of the jointestimation (JE) outlier detection method as a process control technique for short autocorrelated time series is investigated and compared with exponentially weightedmoving average (EWMA). The research goal is to determine the effectiveness of themethod for detecting out-of-control observations when they are the last observation in ashort autocorrelated time series. This is an important problem because detecting anoutlier in the period when it occurs, rather than several periods after it occurs, willpreclude the production of more defective units. Two cases are investigated: short simulated time series when normality is assumed, and short real time series whenthe assumption is violated. The results show that JE is effective for short time series,particularly for autoregressive series when normality is violated. Joint estimation isalso effective for moving average series under the normality assumption and lesseffective when the assumption is violated. In all cases, JE is found to be more effectivethan EWMA.

Asymptotic Least Squares Estimation Efficiency Considerations And Applications by Kodde, D.A., Palm, F.C., and Pfann, G.A., appeared in Journal of Applied Econometrics (March 1989). This paper is concerned with the large sample efficiency of the asymptotic least squares (ALS) estimatorsintroduced by Gourieroux, Monfort, and Trognon (1982, 1985). We show how the efficiency of these estimatorsis affected when additional information is incorporated into the estimation procedure. The relationshipbetween ALS and maximum likelihood is discussed. It is shown that ALS can be used to obtain asymptoticallyefficient estimates for a large range of econometric models. Many results from the literature on estimationare special cases of the framework adopted in this paper.

An application of ALS to a dynamic rational expectations factor demand model in the manufacturing sector is the Netherlands demonstrates the potential ofthe method in the estimation of the parameters in models which are subject to non-linear cross-equation restrictions.

A Time Series Analysis of Gonorrhea Surveillance Data by Schnell, D., Zaidi, A., and Reynolds, G., appeared in Statistics In Medicine, Vol. 8, Page 343-352 (September 1989). Gonorrhea is the most frequently reported communicable disease in the United States. In response to rapidly rising ratesin the late 1960s, the Public Health Service instituted a gonorrhea control programme. An important component of theprogramme is the screening of women for gonococcal infections. We use a time series intervention model to estimate theinitial increase in reporting of cases in women associated with the control programme. From the middle 1970s to the middle1980s, a regular seasonal pattern in the data is conspicuous. We use a second time series model to quantify the seasonalvariation during this period and to construct forecasts.

Keywords: Time series analysis, Intervention analysis, Gonorrhea control programme, Seasonal effects.

Back-end Manager: An Interface Between A Knowledge-based Front End And Its Application Subsystems by Prat, A., Lores, J., Fletcher, P., and Catot, J.M.,appeared in Seccion de Tecnicas Cuantitativa de Gestion, Vol. 3, No. 4 (December 1990). Front Ends for Open and Closed User Systems (FOCUS) is an ESPRIT/2 (no. 2620) project aimed at designing toolsand techniques for the construction of knowledge-based front ends (KBFEs) for open-user systems (reusable softwarecomponents, libraries, etc.) and closed user-systems (free-standing software, packages, etc.). An important partof the project involves the establishment of an architecture for KBFEs and the specification of the KBFE/back-end interface. This paper describes the properties and related issues of such an interface, known as the back-end manager (BEM), and its relationship to the proposed KBFE architecture.

Keywords: Knowledge-based front end, interface separability, back-end manager, user interface.

Combining Forecasts With Contemporaneously Correlated Errors Using Benchmarking by Pankratz, A.,Dept. of Economics and Management, DePauw University (June 1989). Combining forecasts from different sources often improves forecast accuracy. Benchmarking methods have beenproposed for combining forecasts that are stated across different time spans. For example, a set of monthlyforecasts may be adjusted to reflect an annual forecast for the same series obtained from another source. Previous authors presenting benchmarking methods have taken the forecast errors from the different sources to be independent, but it could easily happen that these errors are correlated. This paper gives a benchmarkingmethod for combining forecasts which allows for contemporaneous correlation among the forecast errors from thedifferent sources. The solution is found by adapting generalized least squares estimation of linear model parametersin the presence of extraneous information to the problem of combining forecasts. It is shown that combinedforecasts based on benchmarking methods may be interpreted as modified weighted averages. A vector-ARMA model-basedapproach is used for implmentation; however, the results may be adapted to other extrapolative model forecasts. An empirical example is given.

Keywords: ARIMA models, composite forecasts, distributed lag regression, dynamic regression, extraneousinformation, extrapolative forecasts, generalized least squares, mixed estimation, transfer function, vector ARMA models.

Death Rated & Real Wages: An Analysis of Granger Causality with Post Sample Data & Different Forecast Horizons by Hagnell, M. and Salomonsson, A.,Dept. of Statistics, University of Lund, (June 1989). For yearly Swedish data 1751-1850 we investigated whether an index of the real wages was a Granger cause of the death ratein ages 25-50 years. The investigation was done by comparing the post sample forecasting performance of on one hand a univariate ARIMA model for the death rate and on the other hand a bivariate time series model, where the death rate wasexplained by the real wages index. As bivariate time series model both a transfer function and a distrubuted-lag model was used. Different post sample periods were used and the effect of outliers in the death rate was taken into consideration. Inthe evaluation of forecasting performance we used multi-step ahead forecasts. This approach proved valuable in supportingthe evidence for the hypothesis of causality. On the whole, the analysis showed that the real wages index was a Grangercause of the death rate, although the evidence was weaker when the effect of outliers was eliminated.

Linear Combination of Restrictions and Forecasts in Time Series Analysis by Guerrero, V.M. and Pena, D.,Journal of Forecasting, Vol. 19, Page 103-122, 2000. An important tool in time series analysis is that of combining information in an optimal way. Here we establish a basic combiningrule of linear predictors and show that such problems as forecast updating, missing value estimation, restricted forecasting with bindingconstraints, analysis of outliers and temporal disaggregation can be viewed as problems of optimal linear combination of restrictionsand forecasts. A compatibility test statistic is also provided as a companion tool to check that the linear restrictions arecompatible with the forecasts generated from the historical data.

Keywords: Compatibility testing; disaggregation; missing data; outliers; restricted forecasts.

Measuring Intervention Effects on Multiple Time Series Subjected to Linear Restrictions: A Banking Example by Guerrero, V.M., Pena, D., and Poncela, P.,Journal of Business & Economic Statistics, Vol. 16, No. 4, October 1998.We consider the problem of estimating the effects of an intervention on a time series vector subjected to a linear constraint. Minimum variance linear and unbiased estimators are provided for two different formulations of the problem - (1) when amultivariate intervention analysis is carried out and an adjustment is needed to fulfill the restriction and (2) when a univariate intervention analysis was performed on the aggregate series obtained from the linear constraint, previous tothe multivariate analysis, and the results of both analyses are required to be made compatible with each other. A bankingexample that motivates this work illustrates our solutions.

Keywords: Accounting constraint; Linear estimators; Multivariate intervention; Restricted estimation; VARMA models.

Dynamic Relationship Analysis of US Gasoline and Crude Oil Prices by Liu, L.-M.,Journal of Forecasting, Vol. 10, Page 521-547, 1991.This paper studies the dynamic relationships between U.S. gasoline prices, crude oil prices, and the stock of gasoline. Usingmonthly data between January 1973 and December 1987, we find that the US gasoline price is mainly influenced by the price ofcrude oil. The stock of gasoline has little or no influence on the price of gasoline during the period before the second energy crisis, and seems to have some influence during the period after. We also find that the dynamic relationship between the prices of gasoline and crude oil changes over time, shifting from a longer lag response to a shorter lag response. Box-Jenkins ARIMAand transfer function models are employed in this study. These models are estimated using estimation procedure with and without outlier adjustment. For model estimation with outlier adjustment, an iterative procedure for the joint estimation of modelparameters and outlier effects is employed. The forecasting performance of these models is carefully examined. For the purpose of illustration, we also analyze these time series using classical white-noise regression models. The results show the importanceof using appropriate time-series methods in modeling and forecasting when the data is serially correlated. This paper also demonstrates the problems of time-series modeling when outliers are present.

Keywords: Transfer function model; ARIMA models; Linear transfer function method; Outlier detection; White-noise regressionmodels; Gasoline prices; Crude oil price, Stock of gasoline; Energy crisis.

Dynamic Structural Analysis And Forecasting Of Residential Electricity Consumption by Liu, L.-M., Harris, J.L.,International Journal of Forecasting, Vol. 9, Page 437-455, 1993.This paper studies the dynamic relationships between electricity consumption and several potentially relevant variables,such as weather, price, and consumer income. Monthly data from January 1969 to December 1990 for all-electric residencesin the southeast United States are used for this study. Because of the nature of the annual weather cycle, several of thesetime series are highly seasonal. Multiple-input transfer function models are employed to analyze the data for their dynamic structure and to evaluate future levels of electricity consumption. The linear transfer function (LTF) method is employed in the identification of transfer function models for structural analysis and forecasting. A major finding is that priceplays a major role in explaining conservation behavior by electricity consumers. This result has important implications for forecasting the consumption of electric energy. This paper also demonstrates the appropriate construction of models for economictime series with strong seasonality.

Keywords: Transfer function models; ARIMA models; Seasonality; Electricity consumption; Electricity prices; Energy conservation; Weather conditions.

Econometric and Time Series Analysis of Competitive Marketing Behavior by Takada, H. and Bass, F.M.,School of Management, The University of Texas at Dallas, September 1990.We propose an analytical framework unifying multiple time series analysis and econometric methods to analyze and modelcompetitive market behavior. Empirical analysis is conducted by applying this framework to comprehensive time series dataof a particular industry where three firms are competing in an oligopolistic market. The framework is proven to capturea dynamic competitive structure of the market with a parsimonious model. Major findings of empirical analysis indicate that media advertising, promotion, new variety activity and price are the primary arenas of competition in this industry.

Estimation of Time Series Parameters in the Presence of Outliers by Chang, I., Tiao, G.C., and Chen, C.,Technometrics, Vol. 30, No. 2, May 1988.Outliers in time series can be regarded as being generated by dynamic intervention models at unknown time points.Two special cases, innovational outlier (IO) and additive outlier (AO), are studied in this article. The likelihoodratio criteria for testing the existence of outliers of both types, and the criteria for distinguishing between themare derived. An iterative procedure is proposed for detecting IO and AO in practice and for estimating the time seriesparameters in autoregressive-integrated-moving-average models in the presence of outliers. The powers of the procedurein detecting outliers are investigated by simulation experiments. The performance of the proposed procedure for estimatingthe autoregressive coefficient of a simple AR(1) model compares favorably with robust estimation procedures proposed in the literature. Two real examples are presented.

Keywords: Additive outlier; Innovational outlier; ARIMA model; Intervention; Robust estimate.

Forecasting Time Series With Outliers by Chen, C. and Liu, L.-M.,Journal of Forecasting, Vol. 12, Page 13-35, 1993.Time-series data are often contaminated with outliers due to the influenceof unusual and non-repetitive events. Forecast accuracy in such situationsis reduced due to (1) a carry-over effect of the outlier on the point forecastand (2) a bias in the estimates of model parameters. Hilimer (1984) andLedolter (1989) studied the effect of additive outliers on forecasts. It wasfound that forecast intervals are quite sensitive to additive outliers, butthat point forecasts are largely unaffected unless the outlier occurs near theforecast origin. In such a situation the carry-over effect of the outlier canbe quite substantial. In this study, we investigate the issues of forecastingwhen outliers occur near or at the forecast origin. We propose a strategywhich first estimates the model parameters and outlier effects using theprocedure of Chen and Liu (1993) to reduce the bias in the parameterestimates, and then uses a lower critical value to detect outliers near theforecast origin in the forecasting stage. One aspect of this study is on thecarry-over effects of outliers on forecasts. Four types of outliers areconsidered: innovational outlier, additive outlier, temporary change, andlevel shift. The effects due to a misidentification of an outlier type areexamined. The performance of the outlier detection procedure is studiedfor cases where outliers are near the end of the series. In such cases, wedemonstrate that statistical procedures may not be able to effectivelydetermine the outlier types due to insufficient information. Some strategiesare recommended to reduce potential difficulties caused by incorrectlydetected outlier types. These findings may serve as a justification forforecasting in conjunction with judgment. Two real examples areemployed to illustrate the issues discussed.

Keywords: Misidentification; Forecast errors; Innovation outlier;Additive outlier; Temporary change; Level shift; Outlier test statistics; Critical value.

Forecasting Residential Consumption Of Natural Gas Using Monthly And Quarterly Time Series by Liu, L.-M. and Lin, M.-W., December 1989.This paper studies the consumption of natural gas in Taiwan within the residential sector.In this study, we explore the dynamic relationships among several potentially relevant timeseries variables and develop appropriate models for forecasting. It is apparent that thetemperature of service areas and the price of natural gas are important factors in forecastingthe residential consumption of natural gas. Because of the government price control policy,however, we find that the price variable employed in modeling and forecasting of natural gasconsumption needs to be used judiciously. Otherwise, inappropriate models and poor forecastsmay occur. We also study the inclusion of the price variable using an intervention model andan outlier detection and adjustment method. We find both approaches provide more accurateforecasts and reveal useful information on the dynamics of the controlled variable. Bothmonthly and quarterly time series of the data are studied. We find it is easier to obtainappropriate models using quarterly data. However, the performance of quarterly models maynot be as good as that of monthly models. In this study, however, we find the loss ofperformance efficiency in using quarterly data is not too great. This is probably due to thefact that the consumption of natural gas is subjected to moving holiday effects and the use ofquarterly data may conveniently avoid such systematic disturbances. Both the traditional CCFmethod and the LTF method for transfer function model identification are employed in thisstudy, we find the LTF method is more efficient and easier to use than the CCF method.

Keywords: ARIMA models; transfer function models; aggregation; linear transfer functionmethod; natural gas consumption; service area temperature; natural gas price.

Identification Of Multiple-Input Transfer Function Models by Liu, L.-M., and Hanssens, D.M., Communications in Statistics, Marcel Dekker, Inc., 1982.This paper proposes a procedure for transfer function identification (specification) based on least-squares estimates oftransfer function weights using the original or filtered series.The corner method is then used to identify a parsimonious rationalform of the transfer function. The procedure is illustrated in asimulated example; it is shown how this straightforward approachoutperforms other identification methods such as Box and Jenkins'prewhitening and Haugh and Box' double prewhitening techniques.

Hierarchical Bayes Models For Micro-Marketing Strategies by Montgomery, A.L., Case Studies in Bayesian Statistics, Lecture Notes in Statistics, Springer.Micro-marketing refers to the customization of marketingmix variables to the store-level. We show how prices could be customizedto the store-level, rather than adopting a uniform marketing policy acrossall stores. A basis for these customized pricing strategies is a result of differences in interbrand competition across stores. These changes in interbrandcompetition are related to demographic and competitive characteristics ofthe store's trading area. This study finds that profitable micro-marketingpricing strategies can be implemented. These pricing strategies can increaseexpected operating profits by 25%.

Identification Of Seasonal ARIMA Models Using A Filtering Method by Liu, L.-M., Communications in Statistics, Marcel Dekker, Inc., 1989.This paper proposes an identification method of ARIMA models for seasonaltime series using an intermediary model and a filtering method. This method is foundto be useful when conventional methods, such as using sample ACF and PACF, failto reveal a clear-cut model. This filtering identification method is also found to beparticularly effective when a seasonal time series is subjected to calendar variations,moving-holiday effects, and interventions.

Identification Of Time Series Models In The Presence Of Calendar Variation by Liu, L.-M., International Journal of Forecasting 2, Page 357-372, 1986.Monthly time series data are frequently subject to calendar variation, such as trading day and holiday effects. Calendarvariation effects often make model identification difficult, even in single time mhes analysis. This paper presents acomprehensive and easy-to-use method for the identification of the degree of differencing and appropriate ARMA model inunivariate ARIMA modeling when these effects are present. The method can be readily applied to the identification ofintervention and transfer function models which may also be subject to calendar variation.

Keywords: ARIMA models, Model identification, Calendar variation, Holiday effects, Trading day effects.

Identifying And Locating Treatments In Univariate Series: Using A Robust Procedure by Prasad, S., and Tata, J.The detection and identification of treatments in an interrupted time series process is importantacross diverse fields such as management, medicine, and social sciences. Treatments can at times exertan influence on the time series parameter estimates, and accordingly affect the test statistic. As a result,the impact of a particular treatment may be masked, or spurious readings may be obtained. In either case,the results of the analysis could be questionable. In this paper we employ a robust procedure and showthe importance of accommodating for such aberrations. In addition, this procedure is superior to thetraditional intervention component modelling approach in that the timing and type of intervention doesnot have to be pre-specified.

Keywords: Treatments, Time Series, Robust, Outliers.

Incorporation Expertise In Time Series Modeling: The STATXPS Systemby Prat, A., Marti, M., and Catot, J.M., Statistical Software Newsletter 2, 1985, Vol. 11, No. 2, August 1985.Most of the statistical software available at present, is of high quality. Any criticisms which have arisen have mainly been of the fact that a considerable amount of statistical and technical (computeo knowledge is required in order to achieve maximum efficiency. The term 'user friendliness' is in fact a relatively new one, and most packages in widespread use at present, are of rather poor quality in this aspect.

In this paper, we discuss the actual status and future developments of the STATXPS System, an intelligent interfacebetween the final user and some statistical packages, which guides, instructs, and helps the final user in analysing dataand in the interpretation of the most relevant statistical results, in the way that the expert user has prepared it previously. Some of the actual capabilities and the system's philosophy are presented with a working case study, including snapshots of the computees screen. The future developments of STATXPS as an expert system are briefly indicated.

Integrated Time Series Analysis And Forecasting Using The SCA Statistical Systemby Liu, L.-M., Invited Paper 19.3, 46th Session of the ISI.The SCA Statistical System is an integrated software system that containsadvanced capabilities for time series analysis and forecasting, quality andproductivity improvement, and general statistical analysis. The SCA System hasbeen developed under the advisory direction of several leading statisticians inthese fields of research and application, including Professors G.E.P. Box,G.C. Tiao, M.E. Muller, and others. A wide variety of statistical methods andmodels are available in the SCA System for forecasting and time seriesanalysis. This paper highlights some of the advanced features available in theSystem.

The SCA System is designed to be flexible and easy-to-use for time seriesanalysts and forecasters. With its advanced time series capabilities and thebuilt-in flexibility of the System, the SCA System has also been found to be apowerful too] for research and development of new methods in forecasting andtime series analysis. Since its release in 1983, a number of useful modellingtechniques have been developed using the SCA System.

The time series capabilities of the SCA System include both time domainand frequency (spectral) analyses. In the time domain approach, the SCA Systemallows for several major classes of models, such as Box-Jenkins ARIMA models(Box and Jenkins 1970), intervention models (Box and Tiao 1975), single-equation transfer function models (Box and Jenkins 1970), multi-equationtransfer function models (Wall 1976, Hanssens and Liu 1983), and vector ARMAmodels (Tiao and Box 1981, Jenkins and Alavi 1981). In addition, traditionalmethods such as seasonal adjustment (X-11, X-11-ARIMA, and a canonicaldecomposition method, see Dagum 1983, and Hillmer, Bell and Tiao 1981). andgeneral exponential smoothing methods are also provided. Frequency domainanalyses available in the SCA System include covariance-based, periodogram-based, and ARIMA-model-based methods for computing spectra and cross-spectra ofunivariate and bivariate time series.

Interrelated Demand Rational Expectations Models For Two Types of Laborby Palm, F.C., and Pfann, G.A., Oxford Bulletin Of Economics And Statistics, 52, 1, 1990.This paper examines interrelated labour demand decisions taken by firmswhose expectation formation is assumed to be rational. In line with theapproach adopted by Hansen and Sargent (1980, 1981) among others, wederive optimal employment decision schemes for blue and white collarworkers in closed form solution (CFS). We assume that the driving forces ofthe labour demand model are the predetermined capital stock, incorporatingtechnological innovations, and the real wage costs of blue and white collarlabour. As we assume price-taking behaviour, product demand is notincluded as a determinant of factor demand.

Intervention Analysis To Assess The Impact of Price Changes on Ridership in a Transit System: A Case Studyby Narayan, J., and Considine, J., Logistics and Transportation Review, Vol. 25, No. 1.This paper presents a case study to show how Intervention analysis can be used toobtain accurate estimates of the Impact of two price changes on ridership In a transitsystem. A method lsproposed In which thecomponents of a time series togetherwith theIntervention components are explicitly modeled In a multiple Input transfer functionmodel. The method thus combines techniques from regression analysis with those fromthe ARIMA methodology. It Is shown that both price Increases are accompanied bysignificant reductions In ridership. The case study compares the results of this methodwith those from the usual Intervention analysis of Box and Tiao.

New Forecasting Method for the Residual Demand Curves using Time Series (ARIMA) Modelsby Agustín Martín Calmarza, José Ignacio de la Fuente, presented at the PMAPPS 2002 Conference.In this paper a new methodology to forecast the day ahead electricity market behaviour is presented. This behaviour can be easily modelled by means of the so called residual demand curves (RDC’s) . The pattern of these curves (as the spot market is an hourly market there is one RDC for each hour) changes greatly according with the type of the day (labour-non labour) and the hour (peak ,valley, plateau,...) so this fact must be taken into account. Firstly, a classical ARIMA analysis without explanatory variables is carried out. Afterwards, adequate explanatory variables are searched in order to build a more accurate Transfer Function Model. Next a new procedure called weighted estimation is developed and the differences between these two methods are pointed out. Finally, a case study is presented in order to check the validity of the weighted estimation model.

Forecasting Next-Day Electricity Prices by Time Series Modelsby Nogales, F.J., Contreras, J., Conejo, A.J., and Espínola R.. In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real-world case studies based on the electricity markets of mainland Spain and California are presented.

ARIMA Models to Predict Next-Day Electricity Pricesby Contreras, J., Espínola, R., Nogales, F.J., and Conejo, A.J.. Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit. This paper provides a method to predict next-day electricity prices based on the ARIMA methodology. ARIMA techniques are used to analyze time series and, in the past, have been mainly used for load forecasting, due to their accuracy and mathematical soundness. A detailed explanation of the aforementioned ARIMA models and results from mainland Spain and Californian markets are presented.

Analysis of Financial Time Series   by Tsay, R.S.   This book will be useful as a text of time series analysis for MBA students with finance concentration or senior undergraduate and graduate students in business, economics, mathematics, and statistics who are interested in financial econometrics. The book is also a useful reference for researchers and practitioners in business, finance, and insurance facing Value at Risk calculation, volatility modeling, and analysis of serially correlated data.

A Course in Time Series Analysis  edited by Pena, D., Tiao, G.C., and Tsay, R.S.   This book is based on the lectures of the ECAS '97 Course in Time Series Analysis held at El Escorial, Madrid, Spain from September 15 to September 19, 1997. The book consists of three main components. The first component concerns basic materials of univariate time series analysis presented in the first eight chapters. It includes recent developments in outlier detection, automatic model selection, and seasonal adjustment. The second component addresses advanced topics in univariate time series analysis such as conditional heteroscedastic models, nonlinear models, Bayesian analysis, nonparametric methods, and nueral networks. This component represents current research activities in univariate time series analysis. The third and final component of the book concerns with multivariate time series, including vector ARMA models, cointegration, and linear systems.

Impacts of federal ephedrine and psuedoephedrine regulations on methamphetamine-related hospital admissions  by Cunningham, J.K. and Liu, L.-M,   To determine whether the federal regulation of ephedrine and pseudoephedrine, precursors used in illicit methamphetamine production, reduced mathamphetamine-related acute care hospital admissions. Methods used in the study based on ARIMA-intervention time series analysis.

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