Linear regression using both temporally aggregated and temporally disaggregated data

DOI10.1016/0304-4076(82)90009-4zbMath0504.62060OpenAlexW2059279781MaRDI QIDQ5917273

Franz C. Palm, Theo E. Nijman

Publication date: 1982

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://research.tilburguniversity.edu/en/publications/dc43de47-0865-4485-8f7c-8d7d5d5d90b8

zbMATH Keywords

ordinary least squares estimatortemporal aggregationgeneralized least squares estimatorasymptotic covariance-matrix of maximum likelihood-estimatorstemporally disaggregated data

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Linear regression; mixed models (62J05)

Related Items (3)

On BLU-estimation with data of different periodicity ⋮ Estimating trend from seasonal data: is daily, monthly or annual data best? ⋮ Efficiency gains due to using missing data procedures in regression models

Cites Work

• The use of incomplete observations in multiple regression analysis. A generalized least squares approach
• On an Efficient Two-Step Estimator for Dynamic Simultaneous Equations Models with Autoregressive Errors
• Maximum Likelihood Estimates for a Multivariate Normal Distribution when some Observations are Missing
• On the Problem of Missing Data in Linear Models
• Linear regression using both temporally aggregated and temporally disaggregated data

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