scholarly journals Moment tests of independent components

SERIEs ◽  
2021 ◽  
Author(s):  
Dante Amengual ◽  
Gabriele Fiorentini ◽  
Enrique Sentana
Keyword(s):  
Finite Sample ◽  
Vector Autoregressions ◽  
Specification Tests ◽  
Cross Sectional ◽  
Finite Sample Size ◽  
Cross Sectional Dependence ◽  
Non Gaussian ◽  
Parameter Estimators ◽  
Consistent Parameter ◽  
Simulation Exercises

AbstractWe propose simple specification tests for independent component analysis and structural vector autoregressions with non-Gaussian shocks that check the normality of a single shock and the potential cross-sectional dependence among several of them. Our tests compare the integer (product) moments of the shocks in the sample with their population counterparts. Importantly, we explicitly consider the sampling variability resulting from using shocks computed with consistent parameter estimators. We study the finite sample size of our tests in several simulation exercises and discuss some bootstrap procedures. We also show that our tests have non-negligible power against a variety of empirically plausible alternatives.

Nonparametric panel data regression with parametric cross-sectional dependence

2021 ◽  
Author(s):  
Alexandra Soberon ◽  
Juan M Rodriguez-Poo ◽  
Peter M Robinson
Keyword(s):  
Panel Data ◽  
Monte Carlo Study ◽  
Generalized Least Squares ◽  
Linear Estimator ◽  
Dependence Structure ◽  
Finite Sample ◽  
Cross Sectional ◽  
Panel Data Regression ◽  
Local Linear Estimator ◽  
Cross Sectional Dependence

Abstract In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A Generalized Least Squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterizing the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analyzing the implications of the European Monetary Union for its member countries.

scholarly journals Monte Carlo Inference on Two-Sided Matching Models

Econometrics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 16
Author(s):  
Taehoon Kim ◽  
Jacob Schwartz ◽  
Kyungchul Song ◽  
Yoon-Jae Whang
Keyword(s):  
Monte Carlo ◽  
Finite Sample ◽  
Cross Sectional ◽  
Monte Carlo Simulation Study ◽  
Complicated Form ◽  
Cross Sectional Dependence ◽  
Matching Mechanism ◽  
Matching Models ◽  
Finite Sample Inference ◽  
Asymptotic Validity

This paper considers two-sided matching models with nontransferable utilities, with one side having homogeneous preferences over the other side. When one observes only one or several large matchings, despite the large number of agents involved, asymptotic inference is difficult because the observed matching involves the preferences of all the agents on both sides in a complex way, and creates a complicated form of cross-sectional dependence across observed matches. When we assume that the observed matching is a consequence of a stable matching mechanism with homogeneous preferences on one side, and the preferences are drawn from a parametric distribution conditional on observables, the large observed matching follows a parametric distribution. This paper shows in such a situation how the method of Monte Carlo inference can be a viable option. Being a finite sample inference method, it does not require independence or local dependence among the observations which are often used to obtain asymptotic validity. Results from a Monte Carlo simulation study are presented and discussed.

ROBUST INFERENCE IN AUTOREGRESSIONS WITH MULTIPLE OUTLIERS

2009 ◽  
Vol 25 (6) ◽  
pp. 1625-1661 ◽  
Author(s):  
Giuseppe Cavaliere ◽  
Iliyan Georgiev
Keyword(s):  
Unit Root ◽  
Ordinary Least Squares ◽  
Test Statistics ◽  
Robust Methods ◽  
Finite Sample ◽  
Dummy Variables ◽  
Asymptotic Size ◽  
Finite Sample Size ◽  
Quasi Maximum Likelihood ◽  
Consistent Parameter

We consider robust methods for estimation and unit root (UR) testing in autoregressions with infrequent outliers whose number, size, and location can be random and unknown. We show that in this setting standard inference based on ordinary least squares estimation of an augumented Dickey–Fuller (ADF) regression may not be reliable, because (a) clusters of outliers may lead to inconsistent estimation of the autoregressive parameters and (b) large outliers induce a jump component in the asymptotic distribution of UR test statistics. In the benchmark case of known outlier location, we discuss why the augmentation of the ADF regression with appropriate dummy variables not only ensures consistent parameter estimation but also gives rise to UR tests with significant power gains, growing with the number and the size of the outliers. In the case of unknown outlier location, the dummy-based approach is compared with a robust, mixed Gaussian, quasi maximum likelihood (QML) approach, novel in this context. It is proved that, when the ordinary innovations are Gaussian, the QML and the dummy-based approach are asymptotically equivalent, yielding UR tests with the same asymptotic size and power. Moreover, as a by-product of QML the outlier dates can be consistently estimated. When the innovations display tails fatter than Gaussian, the QML approach ensures further power gains over the dummy-based method. Simulations show that the QML ADF-typet-test, in conjunction with standard Dickey–Fuller critical values, yields the best combination of finite-sample size and power.

scholarly journals Specification tests for non‐Gaussian maximum likelihood estimators

2021 ◽  
Vol 12 (3) ◽  
pp. 683-742 ◽  
Author(s):  
Gabriele Fiorentini ◽  
Enrique Sentana
Keyword(s):  
Regression Models ◽  
Maximum Likelihood Estimators ◽  
Covariance Matrices ◽  
Finite Sample ◽  
Specification Tests ◽  
Financial Uncertainty ◽  
Asymptotic Covariance ◽  
The Business Cycle ◽  
Non Gaussian ◽  
The Relationship

We propose generalized DWH specification tests which simultaneously compare three or more likelihood‐based estimators in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for Garch models and in many empirically relevant macro and finance applications involving Vars and multivariate regressions. We determine the rank of the differences between the estimators' asymptotic covariance matrices under correct specification, and take into account that some parameters remain consistently estimated under distributional misspecification. We provide finite sample results through Monte Carlo simulations. Finally, we analyze a structural Var proposed to capture the relationship between macroeconomic and financial uncertainty and the business cycle.

scholarly journals Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence

2007 ◽  
Vol 7 (3) ◽  
pp. 281-312 ◽  
Author(s):  
Daniel Hoechle
Keyword(s):  
Least Squares ◽  
Fixed Effects ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Standard Errors ◽  
Least Squares Regression ◽  
Finite Sample ◽  
Cross Sectional ◽  
Finite Sample Properties ◽  
Cross Sectional Dependence

I present a new Stata program, xtscc, that estimates pooled ordinary least-squares/weighted least-squares regression and fixed-effects (within) regression models with Driscoll and Kraay (Review of Economics and Statistics 80: 549–560) standard errors. By running Monte Carlo simulations, I compare the finite-sample properties of the cross-sectional dependence–consistent Driscoll–Kraay estimator with the properties of other, more commonly used covariance matrix estimators that do not account for cross-sectional dependence. The results indicate that Driscoll–Kraay standard errors are well calibrated when cross-sectional dependence is present. However, erroneously ignoring cross-sectional correlation in the estimation of panel models can lead to severely biased statistical results. I illustrate the xtscc program by considering an application from empirical finance. Thereby, I also propose a Hausman-type test for fixed effects that is robust to general forms of cross-sectional and temporal dependence.

Extent Pursuit for Cross-Sectional Dependence in Large Panels

2019 ◽  
Author(s):  
Jiti Gao ◽  
Guangming Pan ◽  
Yanrong Yang ◽  
Bo Zhang
Keyword(s):  
Cross Sectional ◽  
Large Panels ◽  
Cross Sectional Dependence
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