Robust Estimation of Linear Fixed Effects Panel Data Models with an Application to the Exporter Productivity Premium

Verardi, Vincenzo; Wagner, Joachim

doi:10.1515/jbnst-2011-0407

Cited by 23 publications

(21 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Cases in point are the study for Germany by Verardi and Wagner (2011) and by Vogel and Wagner (2011) that apply robust methods to deal with extreme observations (outliers) in an adequate way and that finds a dramatic reduction in the estimated exporter productivity premium, and the paper by Powell and Wagner (2011)-also for Germany-that points to different exporter productivity premia at different parts of the productivity distribution. The lack of replication studies with data from other countries, however, makes it impossible to judge whether these results are specific for firms from manufacturing in Germany only or whether they are of a wider relevance.…”

Section: Resultsmentioning

confidence: 99%

International trade and firm performance: a survey of empirical studies since 2006

2012

Self Cite

View full text Add to dashboard Cite

The literature on international trade and firm performance grows exponentially. This paper attempts to summarize what we learn from this literature to guide future empirical and theoretical work in this area. The focus is on the empirical part of the literature that consists of recently published papers using data for firms from manufacturing or services industries to study the links between international trade (exports and imports) and dimensions of firm performance (productivity, wages, profitability and survival).

show abstract

Section: Resultsmentioning

confidence: 99%

International trade and firm performance: a survey of empirical studies since 2006

2012

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, WTD can actually introduce new sample problems. Bollinger and Chandra (2005) and Verardi and Wagner (2011) find that trimming can lead to biased coefficient estimates even in samples without outliers. Moreover, arbitrarily winsorizing or removing observations with large values creates a sample selection problem (Heckman, 1979).…”

Section: Limitations Of Univariate Outlier Treatmentsmentioning

confidence: 99%

Identifying and treating outliers in finance

Adams

Hayunga

Mansi

et al. 2019

Financial Management

Self Cite

View full text Add to dashboard Cite

Outliers represent a fundamental challenge in the empirical finance research. We investigate whether the routine techniques used in finance research to identify and treat outliers are appropriate for the data structures we observe in practice. Specifically, we propose a multivariate identification strategy that can effectively detect outliers. We also introduce an estimator that minimizes the bias outliers caused in both cross‐sectional and panel regressions and provide outlier mitigation guidance. Using replications of four recently published studies in premier finance journals, we show how adjusting for multivariate outliers can lead to significantly different results.

show abstract

“…Their presence only slightly affects the intercept estimates and regression coefficients but affects inference. When analyzing panel data, the fourth outlier category must be considered, namely blocking concentrated outliers that correspond to situations where most of the outermost observations are concentrated in a limited number of time series (Bramati andCroux, 2007 in Verardi &Wagner, 2010) Advances in Social Science,Education and Humanities Research,volume 292 Outlier detection using scatter plots and box plot, results in a scatter plot between Poverty, Fiscal Decentralization and Human Development Index with residual values, which can be seen in Figure VII. According to Soemartini (2007) based on the image above, there are several data that are located far from the overall data collection pattern, therefore indicating an outlier.…”

Section: E Outliermentioning

confidence: 99%

Do Fiscal Decentralization and Human Development Index Affect Poverty in Indonesia?

Varlitya¹,

Masbar²,

Jamal³

et al. 2019

Proceedings of the 1st Aceh Global Conference (AGC 2018)

View full text Add to dashboard Cite

The study aim is to analyze how the effects of fiscal decentralization and Human Development Index (HDI) on poverty in Indonesia. This study employ secondary data in the form of time series from 2010 to 2017, and cross section data consisting of 33 provinces in Indonesia, so that the type of polled data can be categorized that is a combination of time series data (for 8 years) with cross section data 33 Province in Indonesia. The analytical method used is PLS (Panel Least Square) with a comparison of two models namely the Fixed Effect and Robust Least Square models. From the results of the Fixed Effect regression model shows that DDF has a negative and insignificant effect on Poverty, while HDI has a negative and significant effect on Poverty. The study found that each additional unit of DDF reduced poverty by 10.97 percent if the HDI remains. In addition, the Human Development Index unit reduced the number of poor by 79.83 percent if the DDF remains. These findings implied that to reduce the poverty, efforts to improve HDI need to be focused.

show abstract

Robust Estimation of Linear Fixed Effects Panel Data Models with an Application to the Exporter Productivity Premium

Cited by 23 publications

References 24 publications

International trade and firm performance: a survey of empirical studies since 2006

International trade and firm performance: a survey of empirical studies since 2006

Identifying and treating outliers in finance

Do Fiscal Decentralization and Human Development Index Affect Poverty in Indonesia?

Contact Info

Product

Resources

About