Abstract: This note discusses how to compute the asymptotic covariance matrix for a forecast error variance decomposition. The theory relies on having an estimate of the asymptotic covari-ance matrix for the impulse response function and on the variance of structural shocks being normalized to unity. The results apply to a wide range of identification schemes, including con-temporaneous and long run restrictions
AbstractThis paper is concerned with the asymptotic covariance matrix (ACM) of maximum-likelihood es...
This paper presents a simple forecasting technique for variance covariance matrices. It relies signi...
Abstract—Expressions for the variance of an estimated fre-quency function are necessary for many iss...
Impulse response and forecast error variance matrix asymptotics are developed for VAR models with so...
This paper considers inference procedures in a system of linear simultaneous equations with errors g...
The paper questions the reasonability of using forecast error variance decompositions for assessing ...
The asymptotic probability distribution of identified black-box transfer function models is studied....
In this paper we compute the asymptotic variance-covariance matrix of the method of moments estimato...
This paper identifies Structural Vector Autoregressions (SVARs) through bound restrictions on the Fo...
We give new simple general expressions for the asymptotic covariance of the estimated system paramet...
This paper develops asymptotic distribution theory for generalized method of moments (GMM) estimator...
The mean prediction error of a classification or regression procedure can be estimated using resampl...
The objective of this contribution is to analyze statistical properties of estimated models of casca...
The coefficients of the moving average (MA) representation of a vector autoregressive (VAR) process ...
International audienceThe Sample Covariance Matrix (SCM) is widely used in signal processing applica...
AbstractThis paper is concerned with the asymptotic covariance matrix (ACM) of maximum-likelihood es...
This paper presents a simple forecasting technique for variance covariance matrices. It relies signi...
Abstract—Expressions for the variance of an estimated fre-quency function are necessary for many iss...
Impulse response and forecast error variance matrix asymptotics are developed for VAR models with so...
This paper considers inference procedures in a system of linear simultaneous equations with errors g...
The paper questions the reasonability of using forecast error variance decompositions for assessing ...
The asymptotic probability distribution of identified black-box transfer function models is studied....
In this paper we compute the asymptotic variance-covariance matrix of the method of moments estimato...
This paper identifies Structural Vector Autoregressions (SVARs) through bound restrictions on the Fo...
We give new simple general expressions for the asymptotic covariance of the estimated system paramet...
This paper develops asymptotic distribution theory for generalized method of moments (GMM) estimator...
The mean prediction error of a classification or regression procedure can be estimated using resampl...
The objective of this contribution is to analyze statistical properties of estimated models of casca...
The coefficients of the moving average (MA) representation of a vector autoregressive (VAR) process ...
International audienceThe Sample Covariance Matrix (SCM) is widely used in signal processing applica...
AbstractThis paper is concerned with the asymptotic covariance matrix (ACM) of maximum-likelihood es...
This paper presents a simple forecasting technique for variance covariance matrices. It relies signi...
Abstract—Expressions for the variance of an estimated fre-quency function are necessary for many iss...
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