Temporal disaggregation by state space methods: Dynamic regression methods revisited

DOI10.1111/j.1368-423X.2006.00189.xzbMath1147.62367MaRDI QIDQ3422389

Tommaso Proietti

Publication date: 13 February 2007

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

zbMATH Keywords

Marginal likelihoodKalman filter and smootherAutoregressive distributed lag models

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Economic time series analysis (91B84)

Related Items (9)

Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints ⋮ THE NEW ZEALAND BUSINESS CYCLE ⋮ Temporal disaggregation of economic time series: The view from the trenches ⋮ Temporal disaggregation by dynamic regressions: Recent developments in Italian quarterly national accounts ⋮ The Chow-Lin method extended to dynamic models with autocorrelated residuals ⋮ Multivariate temporal disaggregation with cross-sectional constraints ⋮ Forecasting data revisions of GDP: a mixed frequency approach ⋮ Pooling‐Based Data Interpolation and Backdating ⋮ Chow-Lin \(\times N\): how adding a panel dimension can improve accuracy

Uses Software

• Ox

Cites Work

• Unnamed Item
• Smoothing and Interpolation with the State-Space Model
• Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data
• Exact Initial Kalman Filtering and Smoothing for Nonstationary Time Series Models
• Maximum Likelihood Estimation of Regression Models With Stochastic Trend Components
• Adjustment of Monthly or Quarterly Series to Annual Totals: An Approach Based on Quadratic Minimization
• Temporal disaggregation using multivariate structural time series models

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