Inference of Seasonal Long‐memory Time Series with Measurement Error
DOI10.1111/sjos.12099zbMath1364.62228OpenAlexW1605355621MaRDI QIDQ5177955
Edward M. H. Lin, Heiko Rachinger, Henghsiu Tsai
Publication date: 9 March 2015
Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/sjos.12099
measurement errormodel uncertaintyspectral maximum likelihood estimatorseasonal autoregressive fractionally integrated moving-average models
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Parametric hypothesis testing (62F03) Point estimation (62F10)
Related Items (4)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Econometric estimation in long-range dependent volatility models: theory and practice
- Testing for measurement errors with discrete-time data sampled from a CARMA model
- Estimation of fractional integration in the presence of data noise
- A central limit theorem for stationary processes and the parameter estimation of linear processes
- Estimating the dimension of a model
- Quasi-maximum likelihood estimation of stochastic volatility models
- The detection and estimation of long memory in stochastic volatility
- Mean square prediction error for long-memory processes
- On the eigenstructure of generalized fractional processes.
- The quasi-likelihood approach to statistical inference on multiple time-series with long-range dependence
- Inference of seasonal long-memory aggregate time series
- Some consequences of superimposed error in time series analysis
- Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations
- Fractional differencing
- AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
- The likelihood ratio criterion for a composite hypothesis under a local alternative
- Miscellanea. Time series with additive noise
- A UNIFIED APPROACH TO THE MEASUREMENT ERROR PROBLEM IN TIME SERIES MODELS
- Efficient Estimation of Seasonal Long‐Range‐Dependent Processes
- On the Distribution of the Likelihood Ratio
- Seasonal long memory in the aggregate output
- A new look at the statistical model identification
This page was built for publication: Inference of Seasonal Long‐memory Time Series with Measurement Error