A Stein-like estimator for linear panel data models

Wang, Yun; Zhang, Yonghui; Zhou, Qiankun

doi:10.1016/j.econlet.2016.02.016

Cited by 10 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using Hausman test statistic,

{\overset{false~}{H}}_{N}

, given by equation , the following pretest estimator can also be considered

{\overset{β}{true}}_{normalPretest} = {\overset{β}{true}}_{P} + 1 ({\overset{false~}{H}}_{N} > χ_{k, 1 - italicτ}^{2}) ({\overset{false^}{bold-italicβ}}_{italicFE} - {\overset{false^}{bold-italicβ}}_{P}),

where 1( A ) is the indicator function which takes the value of unity if A >0 and zero otherwise, τ is the nominal level of the chi‐square distribution with k degrees of freedom

({italicχ}_{k}^{2})

. Following the line of proof in Guggenberger (), Kabaila, Mainzer and Farchione () and Wang, Zhang and Zhou (), the asymptotic distribution of

{\overset{false^}{bold-italicβ}}_{Pretest}

is established in the following proposition, and the proof is provided in the Appendix.…”

Section: A Pretest Estimatormentioning

confidence: 99%

“…where 1 A is the indicator function which takes the value of unity if A > 0 and zero otherwise, is the nominal level of the chi-square distribution with k degrees of freedom ( 2 k ). Following the line of proof in Guggenberger (2010), Kabaila, Mainzer and Farchione (2015) and Wang, Zhang and Zhou (2016), the asymptotic distribution ofˆ Pretest is established in the following proposition, and the proof is provided in the Appendix.…”

Section: A Pretest Estimatormentioning

confidence: 99%

“…The derivation is straightforward by following Wang et al (2016). For (i), one can apply the Cramer-Wold device, since it is shown in the Proposition 2,…”

Section: Proof Of Propositionmentioning

confidence: 99%

See 2 more Smart Citations

To Pool or Not to Pool: Revisited

Pesaran

Zhou²

2017

Oxf Bull Econ Stat

Self Cite

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. To Pool or not to Pool: Revisited Abstract This paper provides a new comparative analysis of pooled least squares and fixed effects estimators of the slope coefficients in the case of panel data models when the time dimension (T) is fixed while the cross section dimension (N) is allowed to increase without bounds. The individual effects are allowed to be correlated with the regressors, and the comparison is carried out in terms of an exponent coefficient, δ, which measures the degree of pervasiveness of the fixed effects in the panel. It is shown that the pooled estimator remains consistent so long as δ < 1, and is asymptotically normally distributed if δ < 1/2, for a fixed T and as N → ∞. It is further shown that when δ < 1/2, the pooled estimator is more efficient than the fixed effects estimator. Monte Carlo evidence provided supports the main theoretical findings and gives some indications of gains to be made from pooling when δ < 1/2. The problem of how to estimate δ in short T panels is not considered in this paper. Terms of use: Documents inJEL-Code: C010, C230, C330.

show abstract

“…Using Hausman test statistic,

{\overset{false~}{H}}_{N}

, given by equation , the following pretest estimator can also be considered

{\overset{β}{true}}_{normalPretest} = {\overset{β}{true}}_{P} + 1 ({\overset{false~}{H}}_{N} > χ_{k, 1 - italicτ}^{2}) ({\overset{false^}{bold-italicβ}}_{italicFE} - {\overset{false^}{bold-italicβ}}_{P}),

where 1( A ) is the indicator function which takes the value of unity if A >0 and zero otherwise, τ is the nominal level of the chi‐square distribution with k degrees of freedom

({italicχ}_{k}^{2})

. Following the line of proof in Guggenberger (), Kabaila, Mainzer and Farchione () and Wang, Zhang and Zhou (), the asymptotic distribution of

{\overset{false^}{bold-italicβ}}_{Pretest}

is established in the following proposition, and the proof is provided in the Appendix.…”

Section: A Pretest Estimatormentioning

confidence: 99%

Section: A Pretest Estimatormentioning

confidence: 99%

See 1 more Smart Citation

To Pool or Not to Pool: Revisited

Pesaran

Zhou²

2017

Oxf Bull Econ Stat

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to the existing researches, the clustering methods may be divided into three kinds of partition-based, densitybased, hierarchy-based, model-based, and grid-based. Specifically, there are fuzzy c-means clustering [14], Ward clustering [14,15], regression clustering [16][17][18], correlation matrix hierarchical clustering [19][20][21], numerical analysis clustering [10,22,23], and grey relational clustering [11,12,[24][25][26][27][28]. On basis of "absolute index," "incremental index," and "fluctuation index," Li et al [15] reconstructed the distance function and Ward clustering algorithm of similarity measure of panel data and proposed a panel data-adaptive weight clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Stanila et al [20] firstly divided the EU countries into two groups by the hierarchical clustering method, then used panel data to estimate the impact factors of each group on employment rate, and finally constructed the employment rate prediction model of EU member states. In order to accurately measure the parameters of the panel clustering model, Wang et al [22] proposed the Stein form estimation for the linear panel data model and constructed the asymptotic distribution of the Stein form estimation. e results show that within the local asymptotic framework, the asymptotic risk estimated by Stein is strictly smaller than that estimated by the fixed effect.…”

Section: Introductionmentioning

confidence: 99%

An Improved Grey Clustering Model with Multiattribute Spatial-Temporal Feature for Panel Data and Its Application

Jian

Chen

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Due to the complexity and uncertainty of the objective world and the limitation of cognition, it is difficult to extract the information and rules contained in the panel data effectively based on the traditional panel data clustering method. Given this, considering that the absolute amount level, increasing amount level, and volatility level are the main indicators to represent the spatial-temporal feature of the panel data, a novel grey clustering model with the multiattribute spatial-temporal feature of panel data is established, and then it is applied in the regional high-tech industrialization in China. The results show that the proposed model can make full use of the spatial-temporal feature information of the panel data, identify the problems existing in the clustering objects, and make the clustering results more objective and practical.

show abstract

A weighted average limited information maximum likelihood estimator

Qasim

2023

Stat Papers

View full text Add to dashboard Cite

In this article, a Stein-type weighted limited information maximum likelihood (LIML) estimator is proposed. It is based on a weighted average of the ordinary least squares (OLS) and LIML estimators, with weights inversely proportional to the Hausman test statistic. The asymptotic distribution of the proposed estimator is derived by means of local-to-exogenous asymptotic theory. In addition, the asymptotic risk of the Stein-type LIML estimator is calculated, and it is shown that the risk is strictly smaller than the risk of the LIML under certain conditions. A Monte Carlo simulation and an empirical application of a green patent dataset from Nordic countries are used to demonstrate the superiority of the Stein-type LIML estimator to the OLS, two-stage least squares, LIML and combined estimators when the number of instruments is large.

show abstract

A Stein-like estimator for linear panel data models

Cited by 10 publications

References 11 publications

To Pool or Not to Pool: Revisited

To Pool or Not to Pool: Revisited

An Improved Grey Clustering Model with Multiattribute Spatial-Temporal Feature for Panel Data and Its Application

A weighted average limited information maximum likelihood estimator

Contact Info

Product

Resources

About