Forecasting daily time series using periodic unobserved components time series models
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Publication:1010432
DOI10.1016/j.csda.2005.09.009zbMath1157.62505OpenAlexW2150413100MaRDI QIDQ1010432
Publication date: 6 April 2009
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://papers.tinbergen.nl/04135.pdf
Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05)
Related Items (8)
Functional approach to analysis of daily tax revenues ⋮ Stochastic and deterministic trend in state space models ⋮ Seasonal adjustment of daily time series ⋮ Signal extraction and filtering by linear semiparametric methods ⋮ A Bayesian analysis of moving average processes with time-varying parameters ⋮ Forecasting time series using principal component analysis with respect to instrumental variables ⋮ A time series bootstrap procedure for interpolation intervals ⋮ Periodic and seasonal (co-)integration in the state space framework
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- Exact Initial Kalman Filtering and Smoothing for Nonstationary Time Series Models
- Time Series Modelling of Daily Tax Revenues
- Periodic Time Series Models
- Periodic Seasonal Reg-ARFIMA–GARCH Models for Daily Electricity Spot Prices
- Evaluation of likelihood functions for Gaussian signals
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