A quantile regression approach for estimating panel data models using instrumental variables

Harding, Matthew; Lamarche, Carlos

doi:10.1016/j.econlet.2009.04.025

Cited by 128 publications

(91 citation statements)

References 5 publications

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“…While this is technically feasible in the FE-QR context (cf. Harding and Lamarche, 2008), this would however require instruments which vary over time and hence make the search for appropriate instruments even more challenging. Finally, the range of unobservables accounted for in our approach is naturally limited to time-invariant characteristics.…”

Section: Concluding Discussionmentioning

confidence: 99%

The Informal Sector Wage Gap: New Evidence Using Quantile Estimations on Panel Data

Bargain

Kwenda

2014

Economic Development and Cultural Change

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This paper provides new evidence on the wage gap between informal and formal salary workers in South Africa, Brazil and Mexico. We use rich datasets that allow us to define informality in a relatively comparable fashion across countries. We compute precise wage differentials by accounting for taxes paid in the formal sector. For each country, we analyze how the sector wage gap varies within groups, between groups and over time. To account for unobserved heterogeneity, we use large (unbalanced) panels to estimate fixed effects models at the mean and at different quantiles of the wage distribution. We find that unobserved heterogeneity explains a large part of the (conditional) wage gap. The remaining informal sector wage penalty is large in the lower part of the distribution but almost disappears at the top. The penalty primarily concerns young workers and is found to be procyclical. We carefully investigate the robustness of these results and discuss their policy implications as well as regularities across countries. JEL Classification:J21, J23, J24, J31, C14, O17

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Section: Concluding Discussionmentioning

confidence: 99%

The Informal Sector Wage Gap: New Evidence Using Quantile Estimations on Panel Data

Bargain

Kwenda

2014

Economic Development and Cultural Change

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“…In addition, most quantile panel data estimators include additive fixed effects which separate the disturbance term and assumes the parameters vary based only on the time-varying components of the disturbance term (Canay, 2011;Galvao, 2011;Harding and Lamarche, 2009;Koenker, 2004;Lamarche, 2010;Ponomareva, 2011;Rosen, 2012). With additive fixed effects, the model is ,…”

Section: Methodsmentioning

confidence: 99%

Quantile Parameter Heterogeneity in the Finance-Growth Relation: The Case of OECD Countries

Uyar¹,

Uyar²

2018

Prague Economic Papers

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This paper seeks to investigate the effect of financial development on growth in OECD countries during 1999-2014. The aim of the analysis is to study the dependence of growth on given financial development indicators along quantiles of the conditional growth distribution, taking into account the effect played by each country over time. For the purpose of the empirical analysis, it performed the instrumental variable quantile regression panel data (IV-QRPD) model suggested by Powell (2016). The findings of IV-QRPD model indicated that the effect of finance on growth is changing along quantiles of the conditional growth distribution. That is to say, we provide some evidence that high-growth OECD countries react to the changes in financial development less than low-growth countries.

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“…2 The theory of the CCE estimator was further developed in, e.g., Harding and Lamarche (2009;, Kapetanios, Pesaran, and Yamagata (2011), Chudik, Pesaran, and Tosetti (2011), and Chudik and Pesaran (2015. 3 The LS estimator is sometimes called "concentrated" least squares estimator in the literature, and in an earlier version of the paper, we referred to it as the "Gaussian Quasi Maximum Likelihood Estimator", because LS estimation is equivalent to maximizing a conditional Gaussian likelihood function.…”

Section: Notesmentioning

confidence: 99%

Dynamic linear panel regression models with interactive fixed effects

Moon¹,

Weidner²

2014

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We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables. The first-order asymptotic theory of the least squares (LS) estimator of the regression coefficients is worked out in the limit where both the cross-sectional dimension and the number of time periods become large. We find two sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the idiosyncratic error term, and bias due to predetermined (as opposed to strictly exogenous) regressors. We provide a bias-corrected LS estimator. We also present bias-corrected versions of the three classical test statistics (Wald, LR, and LM test) and show their asymptotic distribution is a χ 2 -distribution. Monte Carlo simulations show the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes.

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A quantile regression approach for estimating panel data models using instrumental variables

Cited by 128 publications

References 5 publications

The Informal Sector Wage Gap: New Evidence Using Quantile Estimations on Panel Data

The Informal Sector Wage Gap: New Evidence Using Quantile Estimations on Panel Data

Quantile Parameter Heterogeneity in the Finance-Growth Relation: The Case of OECD Countries

Dynamic linear panel regression models with interactive fixed effects

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