Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter

DOI10.1016/j.csda.2007.08.001zbMath1452.62092OpenAlexW2040177272MaRDI QIDQ1020898

J. Martínez

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.08.001

zbMATH Keywords

business cyclestime seriesARIMA modelstime aggregationtrend estimationfiltering and smoothing

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Computational methods for problems pertaining to statistics (62-08) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (7)

Trend filtering via empirical mode decompositions ⋮ Temporal disaggregation of economic time series: The view from the trenches ⋮ The volatility structure of the fixed income market under the HJM framework: a nonlinear filtering approach ⋮ Computing the mean square error of unobserved components extracted by misspecified time series models ⋮ Editorial: 2nd special issue on statistical signal extraction and filtering ⋮ Trend estimation of financial time series ⋮ Temporal aggregation of cyclical models with business cycle applications

Cites Work

• Unnamed Item
• Exact formulas for the Hodrick-Prescott filter
• Seasonal Adjustment by Signal Extraction
• Improved frequency selective filters
• New algorithms for dating the business cycle
• An application of the TRAMO-SEATS automatic procedure; direct versus indirect adjustment
• Econometric methods of signal extraction
• Low frequency filtering and real business cycles
• Encompassing univariate models in multivariate time series. A case study
• Effects of the Hodrick-Prescott filter on trend and difference stationary time series
• Measuring business cycles in economic time series
• Alternative definitions of the business cycle and their implications for business cycle models: A reply to Torben Mark Pederson.
• Some consequences of temporal aggregation and systematic sampling for ARMA and ARMAX models
• TEMPORAL AGGREGATION IN THE ARIMA PROCESS
• Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
• Asymptotic behaviour of temporal aggregates of time series

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