Often we are interested in the largest root of an autoregressive process. Available methods rely on inverting t-tests to obtain confidence intervals. However, for large autoregressive roots, t-tests do not approximate asymptotically uniformly most powerful tests and do not have optimality properties when inverted for confidence intervals. We exploit the relationship between the power of tests and accuracy of confidence intervals, and suggest methods which are asymptotically more accurate than available interval construction methods. One interval, based on inverting the P(T) or Q(T) statistic, has good asymptotic accuracy and is easy to compute
This paper proposes a GMM-based method for asymptotic confidence interval construction in stationary...
Corrected confidence intervals are developed for an unknown parameter for data from a sequential exp...
textabstractThe aim of the paper is to obtain confidence intervals for the tail index and high quant...
© 2012 Dr. Muhammad Saqib ManzoorIn this dissertation, we revisit the method of Elliott and Stock (2...
SIGLEAvailable from British Library Document Supply Centre-DSC:6100.395(no 2000-15) / BLDSC - Britis...
In this paper, we propose a new method for constructing confidence intervals for the autoregressive ...
Using the asymptotic normality of the least-squares estimates for the autoregressive (AR) process wi...
This paper considers estimation and inference concerning the autoregressive coefficient () in a pane...
Starting from an unbiased estimating function and the corresponding quasi-likelihood, we consider a ...
The purpose of this paper is to provide theoretical justification for some existing methods for cons...
This paper considers estimation and inference concerning the autoregressive coefficient ( ρ ) in a p...
This paper presents discussion of properties of asymptotic confidence intervals based on a normalizi...
In this short note I apply the methodology of game-theoretic probability to calculating non-asymptot...
A new method is proposed for constructing confidence intervals in autoregressive models with linear ...
Sequential methods were used to solve testing problems more efficiently. But at the same time, they ...
This paper proposes a GMM-based method for asymptotic confidence interval construction in stationary...
Corrected confidence intervals are developed for an unknown parameter for data from a sequential exp...
textabstractThe aim of the paper is to obtain confidence intervals for the tail index and high quant...
© 2012 Dr. Muhammad Saqib ManzoorIn this dissertation, we revisit the method of Elliott and Stock (2...
SIGLEAvailable from British Library Document Supply Centre-DSC:6100.395(no 2000-15) / BLDSC - Britis...
In this paper, we propose a new method for constructing confidence intervals for the autoregressive ...
Using the asymptotic normality of the least-squares estimates for the autoregressive (AR) process wi...
This paper considers estimation and inference concerning the autoregressive coefficient () in a pane...
Starting from an unbiased estimating function and the corresponding quasi-likelihood, we consider a ...
The purpose of this paper is to provide theoretical justification for some existing methods for cons...
This paper considers estimation and inference concerning the autoregressive coefficient ( ρ ) in a p...
This paper presents discussion of properties of asymptotic confidence intervals based on a normalizi...
In this short note I apply the methodology of game-theoretic probability to calculating non-asymptot...
A new method is proposed for constructing confidence intervals in autoregressive models with linear ...
Sequential methods were used to solve testing problems more efficiently. But at the same time, they ...
This paper proposes a GMM-based method for asymptotic confidence interval construction in stationary...
Corrected confidence intervals are developed for an unknown parameter for data from a sequential exp...
textabstractThe aim of the paper is to obtain confidence intervals for the tail index and high quant...