Similar Search Results

Multivariate Regression Shrinkage and Selection by Canonical Correlation Analysis

An, Baiguo - 2012

end, a novel relationship between multivariate regression and canonical correlation is discovered. Subsequently, its …

Persistent link: https://www.econbiz.de/10013096103

Simulation Study on Estimation Bias in Spatial Lag Model from Omitted Variable Correlated with Regressors

Yokoi, Takahisa - 2012

In this research, the omitted variable problem in a spatial autoregressive model is analyzed by simulation. We examine the performances of estimators when an omitted variable is correlated with explanatory variables. In the literature, theoretical aspects of estimating spatial autoregressive...

Persistent link: https://www.econbiz.de/10013098186

Testing Covariates in High Dimensional Regression

Lan, Wei - 2013

In a high dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically, we employ the partial covariances between the response variable and the tested covariates to obtain a test statistic. The resulting...

Persistent link: https://www.econbiz.de/10013082410

Regression with Imputed Covariates : A Generalized Missing Indicator Approach

Peracchi, Franco - 2014

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations...

Persistent link: https://www.econbiz.de/10013070713

Uniform Consistency of Nonstationary Kernel-Weighted Sample Covariances for Nonparametric Regression

Li, Degui - 2013

We obtain uniform consistency results for kernel-weighted sample covariances in a nonstationary multiple regression framework that allows for both fixed design and random design coefficient variation. In the fixed design case these nonparametric sample covariances have different uniform...

Persistent link: https://www.econbiz.de/10013072455

Long Run Canonical Correlations : Estimation and Inference

Dovonon, Prosper - 2009

This paper proposes methods for both the consistent estimation of so-called long run canonical correlations (LRCCs) and also testing the null hypothesis that a subset of LRCCs are zero. Two test statistics are proposed and their limiting distribution is derived under the null hypothesis. It is...

Persistent link: https://www.econbiz.de/10013155084

Nonparametric Estimation of Large Covariance Matrices with Conditional Sparsity

Wang, Hanchao - 2020

This paper studies estimation of covariance matrices with conditional sparse structure. We overcome the challenge of estimating dense matrices using a factor structure, the challenge of estimating large-dimensional matrices by postulating sparsity on the covariance of the random noises, and the...

Persistent link: https://www.econbiz.de/10012844599

Inference on the Correlation between Permanent and Transitory Shocks for Unidentified Unobserved Components Models

Nagakura, Daisuke - 2009

In this paper, I propose a simple methodology for inferring the correlation between permanent and transitory shocks in … unidentified unobserved components (UC) models, where the correlation is not identified. However, I show that there is an upper … bound of the correlation implied from the unrestricted ARIMA reduced form. I apply the proposed methodology to GDP data of …

Persistent link: https://www.econbiz.de/10012721353

Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case

Hafner, Christian - 2018

We propose a Kronecker product model for correlation or covariance matrices in thelarge dimensional case. The number of …

Persistent link: https://www.econbiz.de/10012936141

Analyzing Multiple Vector Autoregressions Through Matrix-Variate Normal Distribution with Two Covariance Matrices

Wichitaksorn, Nuttanan - 2017

This paper proposes a new approach to analyze multiple vector autoregressive (VAR) models that render us a newly constructed matrix autoregressive (MtAR) model based on a matrix-variate normal distribution with two covariance matrices. The MtAR is a generalization of VAR models where the two...

Persistent link: https://www.econbiz.de/10012943981