A fixed-bandwidth view of the pre-asymptotic inference for kernel smoothing with time series data

DOI10.1016/J.JECONOM.2016.11.008zbMath1422.62151OpenAlexW2277027138MaRDI QIDQ515139

Yixiao Sun, Min Seong Kim, Jingjing Yang

Publication date: 10 March 2017

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2016.11.008

zbMATH Keywords

kernel density estimatorcalibrationfixed-smoothing asymptoticstemporal dependencelocal polynomial estimator\(t\)-approximationfixed-bandwidth asymptoticsheteroskedasticity and autocorrelation robust variancetesting-optimal smoothing-parameter choice

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Economic time series analysis (91B84)

Related Items (2)

Should we go one step further? An accurate comparison of one-step and two-step procedures in a generalized method of moments framework ⋮ Regression discontinuity and heteroskedasticity robust standard errors: evidence from a fixed-bandwidth approximation

Cites Work

• Unnamed Item
• Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
• Heteroskedasticity, autocorrelation, and spatial correlation robust inference in linear panel models with fixed-effects
• On the central limit question under absolute regularity
• Mixing: Properties and examples
• Nonlinear time series. Nonparametric and parametric methods
• Heteroskedasticity and spatiotemporal dependence robust inference for linear panel models with fixed effects
• Sieve inference on possibly misspecified semi-nonparametric time series models
• Let's fix it: fixed-\(b\) asymptotics versus small-\(b\) asymptotics in heteroskedasticity and autocorrelation robust inference
• FIXED-b ASYMPTOTICS FOR SPATIALLY DEPENDENT ROBUST NONPARAMETRIC COVARIANCE MATRIX ESTIMATORS
• THE MOVING BLOCKS BOOTSTRAP FOR PANEL LINEAR REGRESSION MODELS WITH INDIVIDUAL FIXED EFFECTS
• The Bias and Moment Matrix of the General k-Class Estimators of the Parameters in Simultaneous Equations
• NONPARAMETRIC ESTIMATORS FOR TIME SERIES
• A NEW ASYMPTOTIC THEORY FOR HETEROSKEDASTICITY-AUTOCORRELATION ROBUST TESTS
• A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix
• BOOTSTRAPPING THE LONG RUN
• Automatic Lag Selection in Covariance Matrix Estimation
• Local Polynomial Estimation of Regression Functions for Mixing Processes
• Simple Robust Testing of Regression Hypotheses
• Fixed-Smoothing Asymptotics in a Two-Step Generalized Method of Moments Framework
• HETEROSKEDASTICITY-AUTOCORRELATION ROBUST TESTING USING BANDWIDTH EQUAL TO SAMPLE SIZE
• Fixed-smoothing Asymptotics and Asymptotic F and t Tests in the Presence of Strong Autocorrelation
• Bandwidth Selection in Nonparametric Kernel Testing
• Optimal Bandwidth Selection in Heteroskedasticity–Autocorrelation Robust Testing
• The Error in Rejection Probability of Simple Autocorrelation Robust Tests
• Heteroskedasticity-Autocorrelation Robust Standard Errors Using The Bartlett Kernel Without Truncation
• Some Limit Theorems for Stationary Processes
• The bootstrap and Edgeworth expansion

This page was built for publication: A fixed-bandwidth view of the pre-asymptotic inference for kernel smoothing with time series data