Robust inference on infinite and growing dimensional time-series regression
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Publication:6536576
DOI10.3982/ecta17918zbMATH Open1541.62363MaRDI QIDQ6536576
Abhimanyu Gupta, Myung Hwan Seo
Publication date: 13 May 2024
Published in: Econometrica (Search for Journal in Brave)
nonparametric regressiongrowing number of restrictionshigh-order long-run varianceinfinite-order autoregression
Applications of statistics to economics (62P20) Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)
Cites Work
- Unnamed Item
- Unnamed Item
- Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
- Inference in regression models with many regressors
- Markov chains and stochastic stability
- Hypothesis testing in linear regression when \(k/n\) is large
- Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression
- Series estimation under cross-sectional dependence
- Bootstrapping autoregressions with conditional heteroskedasticity of unknown form
- Asymptotically efficient selection of the order of the model for estimating parameters of a linear process
- Invariance principles for mixing sequences of random variables
- Time series: theory and methods.
- Exact mean integrated squared error
- Consistent autoregressive spectral estimates
- Convergence rates and asymptotic normality for series estimators
- Nonparametric specification testing via the trinity of tests
- Asymptotic inference for dynamic panel estimators of infinite order autoregressive processes
- Weak convergence of the empirical process of residuals in linear models with many parameters
- What is an oil shock?
- Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions
- Tests for Parameter Instability and Structural Change With Unknown Change Point
- Tests of Equality Between Sets of Coefficients in Two Linear Regressions
- Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models
- Root-N-Consistent Semiparametric Regression
- A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix
- Simple Robust Testing of Regression Hypotheses
- Inference in Linear Regression Models with Many Covariates and Heteroscedasticity
- Fixed-Smoothing Asymptotics in a Two-Step Generalized Method of Moments Framework
- TESTING FOR ZERO AUTOCORRELATION IN THE PRESENCE OF STATISTICAL DEPENDENCE
- Consistent Specification Testing Via Nonparametric Series Regression
- Leave‐Out Estimation of Variance Components
- An asymptotically F-distributed Chow test in the presence of heteroscedasticity and autocorrelation
- Regression coefficient and autoregressive order shrinkage and selection via the lasso
- Asymptotic and Bootstrap Inference for AR(∞) Processes with Conditional Heteroskedasticity
- Heteroskedasticity-Autocorrelation Robust Standard Errors Using The Bartlett Kernel Without Truncation
- Testing stochastic dominance with many conditioning variables
- Robust inference on infinite and growing dimensional time-series regression
- HAR Inference: Recommendations for Practice
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