Time-varying cointegration and the Kalman filter
From MaRDI portal
Publication:5862506
DOI10.1080/07474938.2020.1861776zbMath1490.62241OpenAlexW3122385987MaRDI QIDQ5862506
J. Isaac Miller, Taner Yigit, Burak Alparslan Eroğlu
Publication date: 9 March 2022
Published in: Econometric Reviews (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/11693/75407
Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Extracting a common stochastic trend: theory with some applications
- Improving the reliability of bootstrap tests with the fast double bootstrap
- Heteroskedastic cointegration
- Stochastic cointegration: estimation and inference.
- Time-varying parameter models with endogenous regressors
- Evaluating trends in time series of distributions: a spatial fingerprint of human effects on climate
- Functional-coefficient cointegration models
- Nonlinear econometric models with cointegrated and deterministically trending regressors
- Bootstrapping State-Space Models: Gaussian Maximum Likelihood Estimation and the Kalman Filter
- Estimation in the Presence of Stochastic Parameter Variation
- A Sieve Bootstrap For The Test Of A Unit Root
- Some tests for parameter constancy in cointegrated VAR‐models
- ASYMPTOTICS FOR NONLINEAR TRANSFORMATIONS OF INTEGRATED TIME SERIES
- COINTEGRATING REGRESSIONS WITH TIME VARYING COEFFICIENTS
- Nonlinear Regressions with Integrated Time Series
- ADDENDUM TO “ASYMPTOTICS FOR NONLINEAR TRANSFORMATIONS OF INTEGRATED TIME SERIES”
- FURTHER RESULTS ON THE ASYMPTOTICS FOR NONLINEAR TRANSFORMATIONS OF INTEGRATED TIME SERIES
- TIME-VARYING COINTEGRATION
- Testing Cointegrating Relationships Using Irregular and Non‐Contemporaneous Series with an Application to Paleoclimate Data
- Stochastic Limit Theory
- Wavelet energy ratio unit root tests
This page was built for publication: Time-varying cointegration and the Kalman filter