Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2023-08-23
Number: 23-049/III
Author-Name: Yicong Lin
Author-Workplace-Name: Vrije Universiteit Amsterdam
Author-Name: Mingxuan Song
Author-Workplace-Name: Vrije Universiteit Amsterdam
Title: Robust bootstrap inference for 
linear time-varying coefficient models: Some Monte Carlo evidence
Abstract: We propose two robust bootstrap-based simultaneous inference methods for time series models
featuring time-varying coefficients and conduct an extensive simulation study to assess their
performance. Our exploration covers a wide range of scenarios, encompassing serially correlated,
heteroscedastic, endogenous, nonlinear, and nonstationary error processes. Additionally, we
consider situations where the regressors exhibit unit roots, thus delving into a nonlinear cointegration framework. We find that the proposed moving block bootstrap and sieve wild bootstrap
methods show superior, robust small sample performance, in terms of empirical coverage and
length, compared to the sieve bootstrap introduced by Friedrich and Lin (2022) for stationary
models. We then revisit two empirical studies: herding effects in the Chinese new energy market
and consumption behaviors in the U.S. Our findings strongly support the presence of herding
behaviors before 2016, aligning with earlier studies. However, we diverge from previous research
by finding no substantial herding evidence between around 2018 and 2021. In the second example, we find a time-varying cointegrating relationship between consumption and income in the U.S.
Classification-JEL: C14, C22, C63, Q56
Keywords: time-varying models, bootstrap inference, simultaneous confidence bands, energy
market, nonlinear cointegration.
File-URL: https://papers.tinbergen.nl/23049.pdf
File-Format: application/pdf
File-Size: 1.622.626 bytes
Handle: RePEc:tin:wpaper:20230049