When is an aggregate of a time series efficiently forecast by its past?

DOI10.1016/0304-4076(82)90087-2zbMath0487.62083OpenAlexW2047732062MaRDI QIDQ1165546

S. Singh

Publication date: 1982

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

Full work available at URL: https://doi.org/10.1016/0304-4076(82)90087-2

zbMATH Keywords

predictionnecessary and sufficient conditionsGaussian stationary processautoregressionlinear combination of elements of vector time series

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (10)

Testing for poolability of the space-time autoregressive moving-average model ⋮ Integrated hierarchical forecasting ⋮ Comparing aggregate and disaggregate forecasts of first order moving average models ⋮ Aggregation of space-time processes. ⋮ Effect of aggregation on the estimation of trend in mortality ⋮ The value of sharing disaggregated information in supply chains ⋮ Optimal combination forecasts for hierarchical time series ⋮ Application of wavelet decomposition in time-series forecasting ⋮ Linear transformations of vector ARMA processes ⋮ The efficiency of dynamic linear model estimators applied to a linearly aggregated time series

Cites Work

• Unnamed Item
• Forecasting contemporal aggregates of multiple time series
• Likelihood Function of Stationary Multiple Autoregressive Moving Average Models
• Forecasting contemporal time series aggregates

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