Testing distributional assumptions using a continuum of moments
DOI10.1016/j.jeconom.2020.04.033zbMath1464.62223OpenAlexW2908327070WikidataQ114666101 ScholiaQ114666101MaRDI QIDQ2227064
Dante Amengual, Enrique Sentana, Marine Carrasco
Publication date: 9 February 2021
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jeconom.2020.04.033
characteristic functionTikhonov regularizationgoodness-of-fitGMMconsistent testscontinuum of moment conditionscomplex Gaussian process
Applications of statistics to economics (62P20) Asymptotic distribution theory in statistics (62E20) Parametric hypothesis testing (62F03) Asymptotic properties of parametric tests (62F05)
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Cites Work
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- Large Sample Properties of Generalized Method of Moments Estimators
- Consistent model specification tests
- Testing normality: a GMM approach
- Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and nonstandard asymptotics
- A consistent characteristic function-based test for conditional independence
- Characteristic function-based hypothesis tests under weak dependence
- Efficient estimation of general dynamic models with a continuum of moment conditions
- Testing epidemic changes of infinite dimensional parameters
- A consistent test for multivariate normality based on the empirical characteristic function
- Limit behaviour of the empirical characteristic function
- The weak approximation of the empirical characteristic function process when parameters are estimated
- Asymptotic comparison of Cramér-von Mises and nonparametric function estimation techniques for testing goodness-of-fit
- Asymptotic power properties of the Cramer-von Mises test under contiguous alternatives
- The empirical characteristic function and its applications
- Linear integral equations.
- Comparing nonparametric versus parametric regression fits
- Goodness-of-fit tests for a multivariate distribution by the empirical characteristic function
- Empirically relevant critical values for hypothesis tests: A bootstrap approach
- Global power functions of goodness of fit tests.
- GENERALIZATION OF GMM TO A CONTINUUM OF MOMENT CONDITIONS
- CONSISTENT MODEL SPECIFICATION TESTS
- ON THE ASYMPTOTIC EFFICIENCY OF GMM
- ON THE LACK OF POWER OF OMNIBUS SPECIFICATION TESTS
- CHARACTERISTIC FUNCTION–BASED TESTING FOR MULTIFACTOR CONTINUOUS-TIME MARKOV MODELS VIA NONPARAMETRIC REGRESSION
- Applications of empirical characteristic functions in some multivariate problems
- Asymptotic Statistics
- A Conditional Kolmogorov Test
- Asymptotic Theory of Integrated Conditional Moment Tests
- Hypothesis Testing in Time Series via the Empirical Characteristic Function: A Generalized Spectral Density Approach
- HETEROSKEDASTICITY-AUTOCORRELATION ROBUST TESTING USING BANDWIDTH EQUAL TO SAMPLE SIZE
- Limit Theory and Inference About Conditional Distributions
- INTEGRATED CONDITIONAL MOMENT TESTS FOR PARAMETRIC CONDITIONAL DISTRIBUTIONS
- Testing Statistical Hypotheses
- Computing the distribution of quadratic forms in normal variables
- Measures of multivariate skewness and kurtosis with applications
- Weighted simulated integrated conditional moment tests for parametric conditional distributions of stationary time series processes
- A test for normality based on the empirical characteristic function
- A test for normality based on the empirical characteristic function
- Smoothing noisy data with spline functions
- Estimation of affine asset pricing models using the empirical characteristic function
- Goodness-of-fit tests for kernel regression with an application to option implied volatilities
- The complex multinormal distribution, quadratic forms in complex random vectors and an omnibus goodness-of-fit test for the complex normal distribution
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