Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimation of Dynamic Mixed Double Factors Model in High Dimensional
  Panel Data   The purpose of this article is to develop the dimension reduction techniques
in panel data analysis when the number of individuals and indicators is large.
We use Principal Component Analysis (PCA) method to represent large number of
indicators by minority common factors in the factor models. We propose the
Dynamic Mixed Double Factor Model (DMDFM for short) to re ect cross section and
time series correlation with interactive factor structure. DMDFM not only
reduce the dimension of indicators but also consider the time series and cross
section mixed effect. Different from other models, mixed factor model have two
styles of common factors. The regressors factors re flect common trend and
reduce the dimension, error components factors re ect difference and weak
correlation of individuals. The results of Monte Carlo simulation show that
Generalized Method of Moments (GMM) estimators have good unbiasedness and
consistency. Simulation also shows that the DMDFM can improve prediction power
of the models effectively.

0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin