The purpose of this paper is to receive a second order expansion of the t-statistic in AR(1) model in local to unity asjnnptotic approach. I show that Hansen's (1998) method for confidence set construction achieves a second order improvement in local to unity asymptotic approach compared with Stock's (1991) and Andrews ' (1993) methods. Key Words: autoregressive process, confidence set, local to unity asymptotics, uniform convergenc
We derive a strong approximation of a local polynomial estimator (LPE) in nonparametric autoregressi...
AbstractThis paper develops consistency and asymptotic normality of parameter estimates for a higher...
Often we are interested in the largest root of an autoregressive process. Available methods rely on ...
The purpose of this paper is to differentiate between several asymptotically valid methods for confi...
The purpose of this paper is to provide theoretical justification for some existing methods for cons...
In second-order statistical inference, an interval estimate of autocorrelation is a convenient repor...
In this paper, we propose a new method for constructing confidence intervals for the autoregressive ...
inference in autoregressive models with the potential presence of a unit root. By Anna Mikusheva 1 T...
In this short note I apply the methodology of game-theoretic probability to calculating non-asymptot...
This paper derives the exact distribution of the maximum likelihood estimator of a first-order linea...
A limit theory is established for autoregressive time series that smooths the transition between loc...
Using the asymptotic normality of the least-squares estimates for the autoregressive (AR) process wi...
This paper proves strong consistency, along with a rate, of a class of generalized M-estimators for ...
We derive a strong approximation of a local polynomial estimator (LPE) in nonparametric autoregressi...
Autoregressive models are commonly employed to analyze empirical time series. In practice, however, ...
We derive a strong approximation of a local polynomial estimator (LPE) in nonparametric autoregressi...
AbstractThis paper develops consistency and asymptotic normality of parameter estimates for a higher...
Often we are interested in the largest root of an autoregressive process. Available methods rely on ...
The purpose of this paper is to differentiate between several asymptotically valid methods for confi...
The purpose of this paper is to provide theoretical justification for some existing methods for cons...
In second-order statistical inference, an interval estimate of autocorrelation is a convenient repor...
In this paper, we propose a new method for constructing confidence intervals for the autoregressive ...
inference in autoregressive models with the potential presence of a unit root. By Anna Mikusheva 1 T...
In this short note I apply the methodology of game-theoretic probability to calculating non-asymptot...
This paper derives the exact distribution of the maximum likelihood estimator of a first-order linea...
A limit theory is established for autoregressive time series that smooths the transition between loc...
Using the asymptotic normality of the least-squares estimates for the autoregressive (AR) process wi...
This paper proves strong consistency, along with a rate, of a class of generalized M-estimators for ...
We derive a strong approximation of a local polynomial estimator (LPE) in nonparametric autoregressi...
Autoregressive models are commonly employed to analyze empirical time series. In practice, however, ...
We derive a strong approximation of a local polynomial estimator (LPE) in nonparametric autoregressi...
AbstractThis paper develops consistency and asymptotic normality of parameter estimates for a higher...
Often we are interested in the largest root of an autoregressive process. Available methods rely on ...