Inference of time-varying regression models
By Ting Zhang, Wei Biao Wu. 2012
92 citations recorded by the source corpus.
We consider parameter estimation, hypothesis testing and variable selection for partially time-varying coefficient models. Our asymptotic theory has the useful feature that it can allow dependent, nonstationary error and covariate processes. With a two-stage method, the parametric component can be estimated with a $n^{1/2}$-convergence rate. A simulation-assisted hypothesis testing procedure is proposed for testing significance and parameter constancy. We further propose an information criterion that can consistently select the true set of significant predictors. Our method is applied to autoregressive models with time-varying coefficients. Simulation results and a real data application are provided.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/0747a5f0f068ef00cc16f3a673b04ef1979879b4
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Citations in the corpus
- Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models (2018, background): “Meanwhile, there has also been increasing interest on nonparametric estimation of the dynamic time series models under the assumption of local stationarity (see Kim 2001; Giraitis, Kapetanios, and Yates 2014; Zhang and Wu 2012).”
- Estimating Smooth Structural Change in Cointegration Models (2017, unknown): “Nonparametric inference about time-varying parameters has received attention for modeling stationary or locally stationary time series data - see, for instance, Robinson (1989), Cai (2007), Li et al (2011), Chen and Hong (2012), and Zhang and Wu (2012).”