2010
DOI: 10.1016/j.jeconom.2009.10.024
|View full text |Cite
|
Sign up to set email alerts
|

Indirect inference for dynamic panel models

Abstract: a b s t r a c tMaximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size and large cross section sample size asymptotics. This paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference, shows unbiasedness and analyzes efficiency. Monte Carlo studies show that our procedure achieves substantial bias reductions with only mild increases in variance,… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
92
0

Year Published

2014
2014
2019
2019

Publication Types

Select...
5
3
1

Relationship

1
8

Authors

Journals

citations
Cited by 104 publications
(92 citation statements)
references
References 38 publications
0
92
0
Order By: Relevance
“…This will produce evidence on what elements of the feasible asymptotic tests may cause any inaccuracies in finite samples. So, next to (13), (19) and (20) we will also examine…”
Section: Particular Test Proceduresmentioning
confidence: 99%
See 1 more Smart Citation
“…This will produce evidence on what elements of the feasible asymptotic tests may cause any inaccuracies in finite samples. So, next to (13), (19) and (20) we will also examine…”
Section: Particular Test Proceduresmentioning
confidence: 99%
“…For example: the simulation results in Arellano and Bover [3], Hahn and Kuersteiner [4], Alvarez and Arellano [5], Hahn et al [6], Kiviet [7], Kruiniger [8], Okui [9], Roodman [10], Hayakawa [11] and Han and Phillips [12] just concern the panel AR(1) model under homoskedasticity. Although an extra regressor is included in the simulation studies in Arellano and Bond [1], Kiviet [13], Bowsher [14], Hsiao et al [15], Bond and Windmeijer [16], Bun and Carree [17,18], Bun and Kiviet [19], Gouriéroux et al [20], Hayakawa [21], Dhaene and Jochmans [22], Flannery and Hankins [23], Everaert [24] and Kripfganz and Schwarz [25], this regressor is (weakly-)exogenous and most experiments just concern homoskedastic disturbances and stationarity regarding the impact of individual effects. Blundell et al [26] and Bun and Sarafidis [27] include an endogenous regressor, but their design does not allow us to control the degree of simultaneity; moreover, they stick to homoskedasticity.…”
Section: Introductionmentioning
confidence: 99%
“…The second approach, consisting of three estimators, corrects for the estimation bias either analytically, or by simulation. Specifically, these estimators either develop bias correction formulas in the (fixed-effects) least-squares dummy variable model (hereafter LSDVC) (Kiviet, 1995;Bruno, 2005), or approximate the bias function and search for unbiased estimates using an iterative bootstrap-based correction procedure (hereafter BC) (Everaert and Pozzi, 2007), or a simulation-based indirect inference method (hereafter II) (e.g., Gouriéroux et al, 2010). Although these advanced methods should, in theory, reduce the POLS and FE bias, little is known about their performance in the presence of the complex issues listed above.…”
Section: Introductionmentioning
confidence: 99%
“…The parameters of such models can be informed through calibration with moments from firm-level datasets, or can be estimated through methods of indirect inference (see e.g. Dridi et al (2007) or Gouriroux et al (2010). …”
Section: Conclusion and Research Agendamentioning
confidence: 99%