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Libros sobre el tema "Bayesian Structural Time Series Models"

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Publicado: 10 de marzo de 2023

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1

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

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2

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

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3

C, Spall James, ed. Bayesian analysis of time series and dynamic models. Dekker, 1988.

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4

Queen, Catriona M. Bayesian graphical forecasting models for business time series. typescript, 1991.

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5

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

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6

Das, Monidipa, and Soumya K. Ghosh. Enhanced Bayesian Network Models for Spatial Time Series Prediction. 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. typescript, 1989.

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8

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

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9

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

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10

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

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11

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

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12

Bianchi, Marco. Time series modelling in the presence of structural change. CIACO, 1995.

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13

Clements, Michael P. Empirical analysis of macroeconomic time series: VAR and structural models. 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. Thesis Publishers, 1992.

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15

Muscatelli, V. Anton. Unemployment and growth: Some empirical evidence from structural time series models. 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. Statistisk sentralbyrå, 1995.

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17

Brock, William A. A dynamic structural model for stock return volatility and trading volume. 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. 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. Bank of Canada, 1996.

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20

Prado, Raquel. Multistate models for mental fatigue. Edited by Anthony O'Hagan and 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, customize
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21

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

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22

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

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23

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

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24

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

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25

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

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28

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

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29

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

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30

(Editor), Arnold Zellner, and Franz C. Palm (Editor), eds. 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 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., and 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., and 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, and 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 a
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39

Martin, Andrew D. Bayesian Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and 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 infer
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40

Geweke, John, Gary Koop, and Herman Van Dijk, eds. 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
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41

Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and 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 fo
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42

Clark, James S., Dave Bell, Michael Dietze, et al. Assessing the probability of rare climate events. Edited by Anthony O'Hagan and 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 m
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