Bootstrapping regression models with locally stationary disturbances

A linear regression model with errors following a time-varying process is considered. In this class of models, the smoothness condition both in the trend function and in the correlation structure of the error term ensures that these models can be locally approximated by stationary processes, leading...

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Bibliographic Details
Published inTest (Madrid, Spain) Vol. 30; no. 2; pp. 341 - 363
Main Authors Ferreira, Guillermo, Mateu, Jorge, Vilar, Jose A., Muñoz, Joel
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021
Springer Nature B.V
Subjects
Approximation
Confidence intervals
Data analysis
Economics
Finance
Insurance
Management
Mathematics and Statistics
Original Paper
Regression analysis
Regression models
Smoothness
Stationary processes
Statistical analysis
Statistical Theory and Methods
Statistics
Statistics for Business
Bootstrapping
62M10
62J05
Local stationarity
Non-stationarity
Time-varying models
62H12
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Summary:A linear regression model with errors following a time-varying process is considered. In this class of models, the smoothness condition both in the trend function and in the correlation structure of the error term ensures that these models can be locally approximated by stationary processes, leading to a general class of linear regression models with locally stationary errors. We focus here on the bootstrap approximation to the distribution of the least-squares estimator for such class of regression models. We compare and discuss the results on both the classical and bootstrap confidence intervals through an intensive simulation study. The trend is also discussed through a real data analysis on time series of monthly inflation in US with locally stationary errors.
ISSN:1133-0686
1863-8260
DOI:10.1007/s11749-020-00721-3