Book ChapterDOI
Missing Observations in Dynamic Econometric Models: A Partial Synthesis
Andrew Harvey,Andrew Harvey,Colin McKenzie,Colin McKenzie +3 more
- pp 108-133
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TLDR
A number of methods for carrying out the maximum likelihood estimation of a dynamic econometric model with missing observations are examined and it is argued that in all cases the necessary computations can be carried out most efficiently by putting the model in state space form and applying the Kalman filter.Abstract:
A number of methods for carrying out the maximum likelihood estimation of a dynamic econometric model with missing observations are examined These include the approach suggested by Sargan and Drettakis and a method based on the EM algorithm The link between the different methods is explored and it is argued that in all cases the necessary computations can be carried out most efficiently by putting the model in state space form and applying the Kalman filterread more
Citations
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Journal ArticleDOI
Estimating Missing Observations in Economic Time Series
Andrew Harvey,Richard Pierse +1 more
TL;DR: In this paper, the maximum likelihood estimation of the parameters in an ARIMA model when some of the observations are missing or subject to temporal aggregation is considered, and both problems can be solved by setting up the model in state space form and applying the Kalman filter.
Journal ArticleDOI
Identification of ARX-models subject to missing data
TL;DR: Several approaches to the identification problem are presented, including a new method based on the EM (expectation maximization) algorithm, which can be considerable if many data are missing.
A Tensor Framework for Multidimensional Signal Processing
TL;DR: This thesis deals with ltering of multidimensional signals with a novel filtering method termed "Normalized convolution", an example of the signal/certainty - philosophy, and shows how false operator responses due to missing or uncertain data can be significantly reduced or eliminated using this technique.
A Recursive EM Algorithm for Identification of ARX-Models Subject to Missing Data
TL;DR: In this paper, several approaches to the identification problem are presented, including a new method based on the EM (expectation maximization) algorithm, and different approaches are tested and compared using Monte Carlo simulations.
Journal ArticleDOI
Estimating Dynamic Models Using Kalman Filtering
TL;DR: In this paper, the Kalman filter is used to estimate models of presidential approval and a test of rational expectations in approval shows the hypothesis not to hold, and the filter is also used to deal with missing data and to study whether interpolation of missing data is an adequate technique.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Stochastic Processes and Filtering Theory
TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Journal Article
Optimal Filtering
TL;DR: This book helps to fill the void in the market and does that in a superb manner by covering the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation.
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