J. Durbin and S.J. Koopman
- Published in print:
- 2012
- Published Online:
- December 2013
- ISBN:
- 9780199641178
- eISBN:
- 9780191774881
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199641178.003.0006
- Subject:
- Mathematics, Probability / Statistics
This chapter continues the discussion of the computational aspects of filtering and smoothing. It explains the estimation of a regression component of the model and intervention components; the ...
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This chapter continues the discussion of the computational aspects of filtering and smoothing. It explains the estimation of a regression component of the model and intervention components; the square root filter and smoother which may be used when the Kalman filter and smoother show signs of numerical instability; how multivariate time series can be treated as univariate series by bringing elements of the observational vectors into the system one at a time, with computational savings relative to the multivariate treatment in some cases; and further modifications where the observation vector is high-dimensional. The chapter concludes by discussing computer packages for state space methods.Less
This chapter continues the discussion of the computational aspects of filtering and smoothing. It explains the estimation of a regression component of the model and intervention components; the square root filter and smoother which may be used when the Kalman filter and smoother show signs of numerical instability; how multivariate time series can be treated as univariate series by bringing elements of the observational vectors into the system one at a time, with computational savings relative to the multivariate treatment in some cases; and further modifications where the observation vector is high-dimensional. The chapter concludes by discussing computer packages for state space methods.
Youseop Shin
- Published in print:
- 2017
- Published Online:
- September 2017
- ISBN:
- 9780520293168
- eISBN:
- 9780520966383
- Item type:
- book
- Publisher:
- University of California Press
- DOI:
- 10.1525/california/9780520293168.001.0001
- Subject:
- Sociology, Law, Crime and Deviance
This book focuses on fundamental elements of time series analysis that social scientists need to understand to employ time series analysis for their research and practice. Avoiding extraordinary ...
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This book focuses on fundamental elements of time series analysis that social scientists need to understand to employ time series analysis for their research and practice. Avoiding extraordinary mathematical materials, this book explains univariate time-series analysis step by step from the preliminary visual analysis through the modeling of seasonality, trends, and residuals to the prediction and the evaluation of estimated models. Then, this book explains smoothing, multiple time-series analysis, and interrupted time-series analysis. At the end of each step, this book coherently provides an analysis of the monthly violent crime rates as an example.Less
This book focuses on fundamental elements of time series analysis that social scientists need to understand to employ time series analysis for their research and practice. Avoiding extraordinary mathematical materials, this book explains univariate time-series analysis step by step from the preliminary visual analysis through the modeling of seasonality, trends, and residuals to the prediction and the evaluation of estimated models. Then, this book explains smoothing, multiple time-series analysis, and interrupted time-series analysis. At the end of each step, this book coherently provides an analysis of the monthly violent crime rates as an example.
Don Harding and Adrian Pagan
- Published in print:
- 2016
- Published Online:
- January 2018
- ISBN:
- 9780691167084
- eISBN:
- 9781400880935
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691167084.003.0004
- Subject:
- Economics and Finance, Econometrics
The chapter discusses a particular way of producing rules to summarize the nature of the recurrent events. These rules come from the idea that the data incorporating the recurrent event can be ...
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The chapter discusses a particular way of producing rules to summarize the nature of the recurrent events. These rules come from the idea that the data incorporating the recurrent event can be captured by models that specify a number of regimes, and then using the information provided by the fitted model to date the recurrent event. The chapter discusses variants of Markov switching models in the context where there is only a single series in which the recurrent event is observed. It then deals with dating cycles with univariate series. Finally, it considers model-based rules for dating events with multivariate series.Less
The chapter discusses a particular way of producing rules to summarize the nature of the recurrent events. These rules come from the idea that the data incorporating the recurrent event can be captured by models that specify a number of regimes, and then using the information provided by the fitted model to date the recurrent event. The chapter discusses variants of Markov switching models in the context where there is only a single series in which the recurrent event is observed. It then deals with dating cycles with univariate series. Finally, it considers model-based rules for dating events with multivariate series.
