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Libros sobre el tema "BOX-JENKINS"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 4 de febrero de 2022

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1

Evaluation mit Hilfe der Box-Jenkins-Methode: Eine Untersuchung zur Überprüfung der Wirksamkeit einer legislativen Massnahme zur Erhöhung der richterlichen Arbeitseffektivität im Bereich der Zivilgerichtsbarkeit. Frankfurt am Main: P. Lang, 1986.

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2

Geōrganta, Zōē. Hē prosengisē Box-Jenkins stēn analysē kai provlepsē chronologikōn seirōn. Athēna: Kentro Programmatismou kai Oikonomikōn Ereunōn, 1987.

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3

Abou-El-Fetouh, Mohamed Fathi. Forecasting the number of external pilgrims: A Box-Jenkins approach. [Riyadh, Saudi Arabia]: King Saud University, College of Administrative Sciences, Research Center, 1987.

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4

Bedoya, Manuel Otarola. El ciclo cuantitativo: Una aplicación metodológica no estacionaria para el corto plazo en el caso peruano, período 1989-1992. Lima, Perú: Facultad de Economía, Universidad de Lima, 1993.

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5

Löderbusch, Bernhard. Modelle zur Aktienkursprognose auf der Basis der Box/Jenkins-Verfahren: Eine empirische Untersuchung. Krefeld: G. Marchal und H.-J. Matzenbacher Wissenschaftsverlag, 1985.

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6

Werner, Fuchs. Box-Jenkins-Prognosen der kurzfristigen Produktionsentwicklung: Dargestellt am Beispiel ausgewählter Branchen des verarbeitenden Gewerbes in der Bundesrepublik Deutschland zwischen 1976 und 1985. Bergisch Gladbach: J. Eul, 1989.

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7

Babeshko, Lyudmila y Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."
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8

Suitability of Box-Jenkins Modeling for Navy Repair Parts. Storming Media, 1996.

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9

Pankratz, Alan. Forecasting with Univariate Box - Jenkins Models: Concepts and Cases. Wiley & Sons, Incorporated, John, 2009.

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10

Zappe. Forecasting with Univariate Box-Jenkins Models: Co Ncepts and Cases, Second Edition. John Wiley & Sons Inc, 2008.

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11

Tan, Raphael C. H. Use of Census II and Box-Jenkins approaches to forcast U.K. outgoing tourists. Bradford, 1987.

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12

Lerman, David. A model for the generation and study of electromyographic signals. 1991.

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13

McCleary, Richard, David McDowall y Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial autocorrelation function (PACF). Parameters of the tentative ARIMA noise model are estimated with maximum likelihood methods. If the estimates lie within the stationary-invertible bounds and are statistically significant, the residuals of the tentative model are diagnosed to determine whether the model’s residuals are not different than white noise. If the tentative model’s residuals satisfy this assumption, the statistically adequate model is accepted. Otherwise, the identification-estimation-diagnosis ARIMA noise model-building strategy continues iteratively until it yields a statistically adequate model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models built in Chapter 3 are re-introduced in later chapters. Chapter 3 concludes with a discussion and demonstration of auxiliary modeling procedures that are not part of the Box-Jenkins strategy. These auxiliary procedures include the use of information criteria to compare models, unit root tests of stationarity, and co-integration.
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14

McCleary, Richard, David McDowall y Bradley Bartos. Design and Analysis of Time Series Experiments. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.001.0001.

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Design and Analysis of Time Series Experiments develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioral, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing, and model selection. The validity of causal inferences is approached from two complementary directions. The four-validity system of Cook and Campbell relies on ruling out discrete threats to statistical conclusion, internal, construct, and external validity. The Rubin system causal model relies on the identification of counterfactual time series. The two approaches to causal validity are shown to be complementary and are illustrated with a construction of a synthetic control time series. Example analyses make optimal use of graphical illustrations. Mathematical methods used in the example analyses are explicated in technical appendices, including expectation algebra, sequences and series, maximum likelihood, Box-Cox transformation analyses and probability.
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15

McDowall, David, Richard McCleary y Bradley J. Bartos. Interrupted Time Series Analysis. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190943943.001.0001.

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Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioural, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing and model selection. New developments, including Bayesian hypothesis testing and synthetic control group designs are described and their prospects for widespread adoption are discussed. Example analyses make optimal use of graphical illustrations. Mathematical methods used in the example analyses are explicated assuming only exposure to an introductory statistics course. Design and Analysis of Time Series Experiments (DATSE) and other appropriate authorities are cited for formal proofs. Forty completed example analyses are used to demonstrate the implications of model properties. The example analyses are suitable for use as problem sets for classrooms, workshops, and short-courses.
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