Add to ORKGWe investigate the asymptotic behavior of the OLS estimator for regressions with two slowly varying regressors. It is shown that the asymptotic distribution is normal one-dimensional and may belong to one of four types depending on the relative rates of growth of the regressors. The analysis establishes, in particular, a new link between slow variation and $L_p$-approximability. A revised version of this paper has been published in Econometrics Journal (2011), volume 14, pp. 304--320
AbstractWe find the asymptotic distribution of the OLS estimator of the parameters β and ρ in the mi...
In this paper we study the asymptotic normality in high-dimensional linear regression. We focus on t...
This paper considers estimation and hypothesis testing in linear time series models when some or all...
We investigate the asymptotic behavior of the OLS estimator for regressions with two slowly varying ...
Slowly varying regressors are asymptotically collinear in linear regression. Usual regression formul...
This paper considers the regression model with a slowly varying (SV) regressor in the presence of a ...
Standardized slowly varying regressors are shown to be $L_p$-approximable. This fact allows one to r...
We propose a general method of modeling deterministic trends for autoregressions. The method relies ...
The most part of the paper is about modeling (or approximating) nonstochastic regressors. Examples o...
We consider a mixed vector autoregressive model with deterministic exogenous regressors and an autor...
We show that OLS and GLS are asymptotically equivalent in the linear regression model with AR(p)-9is...
We find the asymptotics of the OLS estimator of the parameters $\beta$ and $\rho$ in the spatial aut...
This thesis deals with asymptotic properties of least squares estimators of regression coefficients ...
Weak consistency and asymptotic normality of the ordinary least-squares estimator in a linear regres...
We find the asymptotic distribution of the OLS estimator of the parameters $% \beta$ and $\rho$ in t...
AbstractWe find the asymptotic distribution of the OLS estimator of the parameters β and ρ in the mi...
In this paper we study the asymptotic normality in high-dimensional linear regression. We focus on t...
This paper considers estimation and hypothesis testing in linear time series models when some or all...
We investigate the asymptotic behavior of the OLS estimator for regressions with two slowly varying ...
Slowly varying regressors are asymptotically collinear in linear regression. Usual regression formul...
This paper considers the regression model with a slowly varying (SV) regressor in the presence of a ...
Standardized slowly varying regressors are shown to be $L_p$-approximable. This fact allows one to r...
We propose a general method of modeling deterministic trends for autoregressions. The method relies ...
The most part of the paper is about modeling (or approximating) nonstochastic regressors. Examples o...
We consider a mixed vector autoregressive model with deterministic exogenous regressors and an autor...
We show that OLS and GLS are asymptotically equivalent in the linear regression model with AR(p)-9is...
We find the asymptotics of the OLS estimator of the parameters $\beta$ and $\rho$ in the spatial aut...
This thesis deals with asymptotic properties of least squares estimators of regression coefficients ...
Weak consistency and asymptotic normality of the ordinary least-squares estimator in a linear regres...
We find the asymptotic distribution of the OLS estimator of the parameters $% \beta$ and $\rho$ in t...
AbstractWe find the asymptotic distribution of the OLS estimator of the parameters β and ρ in the mi...
In this paper we study the asymptotic normality in high-dimensional linear regression. We focus on t...
This paper considers estimation and hypothesis testing in linear time series models when some or all...