J. Bai, and S. Ng. Econometrica, 72 (4):
1127-1127(2004)
Abstract
This paper develops a new methodology that makes use of the factor
structure of large dimensional panels to understand the nature of
nonstationarity in the data. We refer to it as PANIC-Panel Analysis
of Nonstationarity in Idiosyncratic and Common components. PANIC
can detect whether the nonstationarity in a series is pervasive,
or variable-specific, or both. It can determine the number of independent
stochastic trends driving the common factors. PANIC also permits
valid pooling of individual statistics and thus panel tests can
be constructed. A distinctive feature of PANIC is that it tests
the unobserved components of the data instead of the observed series.
The key to PANIC is consistent estimation of the space spanned by
the unobserved common factors and the idiosyncratic errors without
knowing a priori whether these are stationary or integrated processes.
We provide a rigorous theory for estimation and inference and show
that the tests have good finite sample properties.
%0 Journal Article
%1 Bai2004
%A Bai, Jushan
%A Ng, Serena
%D 2004
%J Econometrica
%K Econometrics
%N 4
%P 1127-1127
%T A PANIC Attack on Unit Roots and Cointegration
%U http://www.blackwell-synergy.com/links/doi/10.1111/j.1468-0262.2004.00528.x/abs
%V 72
%X This paper develops a new methodology that makes use of the factor
structure of large dimensional panels to understand the nature of
nonstationarity in the data. We refer to it as PANIC-Panel Analysis
of Nonstationarity in Idiosyncratic and Common components. PANIC
can detect whether the nonstationarity in a series is pervasive,
or variable-specific, or both. It can determine the number of independent
stochastic trends driving the common factors. PANIC also permits
valid pooling of individual statistics and thus panel tests can
be constructed. A distinctive feature of PANIC is that it tests
the unobserved components of the data instead of the observed series.
The key to PANIC is consistent estimation of the space spanned by
the unobserved common factors and the idiosyncratic errors without
knowing a priori whether these are stationary or integrated processes.
We provide a rigorous theory for estimation and inference and show
that the tests have good finite sample properties.
@article{Bai2004,
abstract = {This paper develops a new methodology that makes use of the factor
structure of large dimensional panels to understand the nature of
nonstationarity in the data. We refer to it as PANIC-Panel Analysis
of Nonstationarity in Idiosyncratic and Common components. PANIC
can detect whether the nonstationarity in a series is pervasive,
or variable-specific, or both. It can determine the number of independent
stochastic trends driving the common factors. PANIC also permits
valid pooling of individual statistics and thus panel tests can
be constructed. A distinctive feature of PANIC is that it tests
the unobserved components of the data instead of the observed series.
The key to PANIC is consistent estimation of the space spanned by
the unobserved common factors and the idiosyncratic errors without
knowing a priori whether these are stationary or integrated processes.
We provide a rigorous theory for estimation and inference and show
that the tests have good finite sample properties. },
added-at = {2006-08-16T16:21:59.000+0200},
author = {Bai, Jushan and Ng, Serena},
biburl = {https://www.bibsonomy.org/bibtex/2a701784a303a2769c2e6ca99d65f723d/gerhard},
eprint = {http://www.blackwell-synergy.com/links/doi/10.1111/j.1468-0262.2004.00528.x/pdf},
interhash = {5cf140291e393d45e3bca659f32db388},
intrahash = {a701784a303a2769c2e6ca99d65f723d},
journal = {Econometrica},
keywords = {Econometrics},
number = 4,
owner = {Gerhard},
pages = {1127-1127},
pdf = {papers\Bai2004.pdf},
timestamp = {2006-08-16T16:21:59.000+0200},
title = {A PANIC Attack on Unit Roots and Cointegration},
url = {http://www.blackwell-synergy.com/links/doi/10.1111/j.1468-0262.2004.00528.x/abs},
volume = 72,
year = 2004
}