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Livres sur le sujet « Bayesian Structural Time Series Models »

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Auteur : Grafiati

Publié le 10 mars 2023

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1

Barber, David, A. Taylan Cemgil et Silvia Chiappa, dir. Bayesian Time Series Models. Cambridge : Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511984679.

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2

Barber, David. Bayesian time series models. Cambridge : Cambridge University Press, 2011.

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3

C, Spall James, dir. Bayesian analysis of time series and dynamic models. New York : Dekker, 1988.

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4

Queen, Catriona M. Bayesian graphical forecasting models for business time series. [s.l.] : typescript, 1991.

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5

Koop, Gary. Bayesian long-run prediction in time series models. Kraków : Cracow Academy of Economics, 1992.

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6

Das, Monidipa, et Soumya K. Ghosh. Enhanced Bayesian Network Models for Spatial Time Series Prediction. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27749-9.

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7

Barbosa, Emanuel Pimentel. Dynamic Bayesian models for vector time series analysis & forecasting. [s.l.] : typescript, 1989.

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8

1948-, Palm Franz C., et Zellner Arnold, dir. The structural econometric time series analysis approach. Cambridge : Cambridge University Press, 2004.

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9

Harvey, A. C. Forecasting, structural time series models and the Kalman filter. Cambridge : Cambridge University Press, 1989.

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10

Forecasting, structural time series models, and the Kalman filter. Cambridge : Cambridge University Press, 1990.

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11

Harvey, Andrew. Forecasting, structural time series models and the Kalman filter. Cambridge : Cambridge University Press, 1989.

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12

Bianchi, Marco. Time series modelling in the presence of structural change. Louvain-la-Neuve : CIACO, 1995.

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13

Clements, Michael P. Empirical analysis of macroeconomic time series : VAR and structural models. Southampton : University of Southampton, Dept. of Economics, 1990.

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14

Boswijk, H. Peter. Cointegration, identification, and exogeneity : Inference in structural error correction models. Amsterdam : Thesis Publishers, 1992.

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15

Muscatelli, V. Anton. Unemployment and growth : Some empirical evidence from structural time series models. Glasgow : Glasgow University, Department of Political Economy, 1995.

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16

Skjerpen, Terje. Seasonal adjustment of first time registered new passenger cars in Norway by structural time series analysis. Oslo : Statistisk sentralbyrå, 1995.

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17

Brock, William A. A dynamic structural model for stock return volatility and trading volume. Cambridge, MA : National Bureau of Economic Research, 1995.

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18

Sarantis, Nicholas. Structural and time series models of exchange rate determination : A comparison of their forecasting performance. Kingston upon Thames : Apex Centre, Kingston University, 1993.

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19

Canada, Bank of. A semi-structural method to estimate potential output : Combining economic theory with a time-series filter. Ottawa : Bank of Canada, 1996.

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20

Prado, Raquel. Multistate models for mental fatigue. Sous la direction de Anthony O'Hagan et Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.29.

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This article discusses the use of structured, multivariate Bayesian dynamic models in the analysis of experimental data involving large-scale electroencephalography (EEG) signals or time series generated on individuals subject to tasks inducing mental fatigue. It first provides an overview of the goals and challenges in the analysis of brain signals, using the EEG case as example, before describing the development and application of novel time-varying autoregressive and regime switching models, which incorporate relevant prior information via structured priors and fitted using novel, customized Bayesian computational methods. In the experiment, a subject was asked to perform simple arithmetic operations for a period of three hours. Prior to the experiment, the subject was confirmed to be alert. After the experiment ended, the subject was fatigued. The study demonstrates that Bayesian analysis is useful for real time detection of cognitive fatigue.

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21

Barber, David, Silvia Chiappa et A. Taylan Cemgil. Bayesian Time Series Models. Cambridge University Press, 2012.

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22

Barber, David, Silvia Chiappa et A. Taylan Cemgil. Bayesian Time Series Models. Cambridge University Press, 2011.

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23

Barber, David, Silvia Chiappa et A. Taylan Cemgil. Bayesian Time Series Models. Cambridge University Press, 2011.

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24

Zellner, Arnold, et Franz C. Palm. Structural Econometric Time Series Analysis Approach. Cambridge University Press, 2004.

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25

Zellner, Arnold, et Franz C. Palm. Structural Econometric Time Series Analysis Approach. Cambridge University Press, 2004.

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26

Structural Econometric Time Series Analysis Approach. University of Cambridge ESOL Examinations, 2011.

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27

Zellner, Arnold, et Franz C. Palm. Structural Econometric Time Series Analysis Approach. Cambridge University Press, 2009.

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28

Zellner, Arnold, et Franz C. Palm. Structural Econometric Time Series Analysis Approach. Cambridge University Press, 2006.

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29

Zellner, Arnold, et Franz C. Palm. Structural Econometric Time Series Analysis Approach. Cambridge University Press, 2004.

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30

(Editor), Arnold Zellner, et Franz C. Palm (Editor), dir. The Structural Econometric Time Series Analysis Approach. Cambridge University Press, 2004.

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31

Forecasting, structural time series models, and the Kalman filter. Cambridge : Cambridge University Press, 1996.

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32

Harvey, Andrew C. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, 2014.

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33

Harvey, Andrew C. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, 1990.

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34

Kulcsar, Bela. The forecasting accuracy of univariate and structural time series models. Department of Economics, Loughborough University of Technology, 1992.

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35

Multiscale Modeling : A Bayesian Perspective (Springer Series in Statistics). Springer New York, 2007.

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36

Ghosh, Soumya K., et Monidipa Das. Enhanced Bayesian Network Models for Spatial Time Series Prediction : Recent Research Trend in Data-Driven Predictive Analytics. Springer, 2020.

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37

Ghosh, Soumya K., et Monidipa Das. Enhanced Bayesian Network Models for Spatial Time Series Prediction : Recent Research Trend in Data-Driven Predictive Analytics. Springer, 2019.

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38

McDowall, David, Richard McCleary et 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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39

Martin, Andrew D. Bayesian Analysis. Sous la direction de Janet M. Box-Steffensmeier, Henry E. Brady et David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0021.

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This article surveys modern Bayesian methods of estimating statistical models. It first provides an introduction to the Bayesian approach for statistical inference, contrasting it with more conventional approaches. It then explains the Monte Carlo principle and reviews commonly used Markov Chain Monte Carlo (MCMC) methods. This is followed by a practical justification for the use of Bayesian methods in the social sciences, and a number of examples from the literature where Bayesian methods have proven useful are shown. The article finally provides a review of modern software for Bayesian inference, and a discussion of the future of Bayesian methods in political science. One area ripe for research is the use of prior information in statistical analyses. Mixture models and those with discrete parameters (such as change point models in the time-series context) are completely underutilized in political science.

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40

Geweke, John, Gary Koop et Herman Van Dijk, dir. The Oxford Handbook of Bayesian Econometrics. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199559084.001.0001.

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Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. The Oxford Handbook of Bayesian Econometrics is a single source about Bayesian methods in specialized fields. It contains articles by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with articles on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes articles on Bayesian principles and methodology.

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41

Quintana, José Mario, Carlos Carvalho, James Scott et Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility : The Credit Crisis of 2007–2008. Sous la direction de Anthony O'Hagan et Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.

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This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data for the year 2007 for three market indices: the Standard and Poor’s S&P500, the NASDAQ NDX100 and the financial equity index called XLF. Three models of financial time series are estimated: a model with stochastic volatility, a model with stochastic volatility that also incorporates jumps in volatility, and a Garch model.

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42

Clark, James S., Dave Bell, Michael Dietze, Michelle Hersh, Ines Ibanez, Shannon LaDeau, Sean McMahon et al. Assessing the probability of rare climate events. Sous la direction de Anthony O'Hagan et Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.16.

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This article focuses on the use of Bayesian methods in assessing the probability of rare climate events, and more specifically the potential collapse of the meridional overturning circulation (MOC) in the Atlantic Ocean. It first provides an overview of climate models and their use to perform climate simulations, drawing attention to uncertainty in climate simulators and the role of data in climate prediction, before describing an experiment that simulates the evolution of the MOC through the twenty-first century. MOC collapse is predicted by the GENIE-1 (Grid Enabled Integrated Earth system model) for some values of the model inputs, and Bayesian emulation is used for collapse probability analysis. Data comprising a sparse time series of five measurements of the MOC from 1957 to 2004 are analysed. The results demonstrate the utility of Bayesian analysis in dealing with uncertainty in complex models, and in particular in quantifying the risk of extreme outcomes.

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