maximum likelihood estimation of the var(1) model parameters with missing observations

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ID: 149591
2013
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Abstract
Missing-data problems are extremely common in practice. To achieve reliable inferential results, we need to take into account this feature of the data. Suppose that the univariate data set under analysis has missing observations. This paper examines the impact of selecting an auxiliary complete data set—whose underlying stochastic process is to some extent interdependent with the former—to improve the efficiency of the estimators for the relevant parameters of the model. The Vector AutoRegressive (VAR) Model has revealed to be an extremely useful tool in capturing the dynamics of bivariate time series. We propose maximum likelihood estimators for the parameters of the VAR(1) Model based on monotone missing data pattern. Estimators’ precision is also derived. Afterwards, we compare the bivariate modelling scheme with its univariate counterpart. More precisely, the univariate data set with missing observations will be modelled by an AutoRegressive Moving Average (ARMA(2,1)) Model. We will also analyse the behaviour of the AutoRegressive Model of order one, AR(1), due to its practical importance. We focus on the mean value of the main stochastic process. By simulation studies, we conclude that the estimator based on the VAR(1) Model is preferable to those derived from the univariate context.
Reference Key
mourio2013mathematicalmaximum Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Helena Mouriño;Maria Isabel Barão
Journal journal of power sources
Year 2013
DOI
10.1155/2013/848120
URL
http://dx.doi.org/10.1155/2013/848120
Keywords
civil engineering (general)mathematics engineering (general)

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