Aggregation of space-time processes.
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Publication:1421310
DOI10.1016/S0304-4076(03)00132-5zbMath1034.62092OpenAlexW3021456546MaRDI QIDQ1421310
Clive W. J. Granger, Raffaella Giacomini
Publication date: 26 January 2004
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0304-4076(03)00132-5
Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Inference from spatial processes (62M30)
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Cites Work
- Unnamed Item
- Contracting in space: An application of spatial statistics to discrete-choice models
- A Three-Stage Iterative Procedure for Space-Time Modeling
- Predictions from ARMAX models
- Long memory relationships and the aggregation of dynamic models
- When is an aggregate of a time series efficiently forecast by its past?
- Forecasting aggregated vector ARMA processes
- Fitting autoregressive models for prediction
- Econometric Analysis of Aggregation in the Context of Linear Prediction Models
- Forecasting contemporal time series aggregates
- Predictions of multivariate autoregressive-moving average models
- Estimation and Prediction from Aggregate Data when Aggregates are Measured More Accurately than Their Components
- Blur-generated non-separable space–time models
- Forecasting and conditional projection using realistic prior distributions
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