Sieve bootstrap inference for time-varying coefficient models

Arbeitspapier

We propose a sieve bootstrap framework to conduct pointwise and simultaneous inference for time-varying coefficient regression models based on a nonparametric local linear estimator. The asymptotic validity of the sieve bootstrap in the presence of autocorrelation is established. We find that it automatically produces a consistent estimation of nuisance parameters, both at the interior and boundary points. In addition, we develop a bootstrap test for parameter constancy and show that it is asymptotically correctly sized. An extensive simulation study supports our findings. The proposed methods are applied to assess the price development of CO2 certificates in the European Emissions Trading System (EU ETS). We find evidence of time variation in the relationship between allowance prices and their fundamental price drivers.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. TI 2021-107/III

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Energy: Government Policy ; Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
• Subject: sieve bootstrap ; nonparametric estimation ; simultaneous confidence bands ; energy economics ; emission trading

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Friedrich, Marina ; Lin, Yicong
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2021

Further information

• Handle: http://hdl.handle.net/10419/248789
• Last update: 10.03.2025, 11:42 AM CET