1,721,009 research outputs found
QRPROCESS: Stata module for quantile regression: fast algorithm, pointwise and uniform inference
No full textThis package offers fast estimation and inference procedures for the linear quantile regression model. First, qrprocess implements new algorithms that are much quicker than the built-in Stata commands, especially when a large number of quantile regressions or bootstrap replications must be estimated. Second, the commands provide analytical estimates of the variance-covariance matrix of the coefficients for several quantile regressions allowing for weights, clustering and stratification. Third, in addition to traditional pointwise confidence intervals, this command also provides functional confidence bands and tests of functional hypotheses. Fourth, predict called after qrprocess can generate monotone estimates of the conditional quantile and distribution functions obtained by rearrangement. Fifth, the new command plotprocess conveniently plots the estimated coefficients with their confidence intervals and uniform bands
Fast algorithms for the quantile regression process
Get PDFThe widespread use of quantile regression methods depends crucially
on the existence of fast algorithms. Despite numerous algorithmic improvements,
the computation time is still non-negligible because researchers
often estimate many quantile regressions and use the bootstrap for inference.
We suggest two new fast algorithms for the estimation of a sequence of quantile
regressions at many quantile indexes. The first algorithm applies the preprocessing
idea of Portnoy and Koenker (1997) but exploits a previously estimated
quantile regression to guess the sign of the residuals. This step allows
for a reduction of the effective sample size. The second algorithm starts from
a previously estimated quantile regression at a similar quantile index and updates
it using a single Newton-Raphson iteration. The first algorithm is exact,
while the second is only asymptotically equivalent to the traditional quantile
regression estimator. We also apply the preprocessing idea to the bootstrap by
using the sample estimates to guess the sign of the residuals in the bootstrap
sample. Simulations show that our new algorithms provide very large improvements
in computation time without significant (if any) cost in the quality of
the estimates. For instance, we divide by 100 the time required to estimate 99
quantile regressions with 20 regressors and 50,000 observations.https://arxiv.org/abs/1901.03821First author draf
Local quantile treatment effects
Get PDFThis chapter reviews instrumental variable models of quantile treatment effects. We focus on models that achieve identification through a monotonicity assumption in the treatment choice equation. We discuss the key conditions, the role of control variables as well as the estimands in detail and review the literature on estimation and inference. Then we consider extensions to multiple and continuous instruments, to the regression discontinuity design, and discuss the testability of the assumptions. Finally, we compare this approach to the alternative instrumental variable approach reviewed by Chernozhukov et al. (2016). Two open research problems are highlighted in the conclusio
Local Quantile Treatment Effects
No full textThis chapter reviews instrumental variable models of quantile treatment effects. We focus on models that achieve identification through a monotonicity assumption in the treatment choice equation. We discuss the key conditions, the role of control variables as well as the estimands in detail and review the literature on estimation and inference. Then we consider extensions to multiple and continuous instruments, to the regression discontinuity design, and discuss the testability of the assumptions. Finally, we compare this approach to the alternative instrumental variable approach reviewed in Chapter 9 of this handbook. Two open research problems are highlighted in the conclusion
Do Public Ownership and Lack of Competition Matter for Wages and Employment? Evidence from Personnel Records of a Privatized Firm
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Counterfactual : an R package for counterfactual analysis
Get PDFThe Counterfactual package implements the estimation and inference methods of Cher nozhukov et al. (2013) for counterfactual analysis. The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions. This paper serves as an introduction to the package and displays basic functionality of the commands contained within
Informal Pay Gaps in Good and Bad Times: Evidence from Russia
Get PDFInformal work is traditionally large in Russia and has further increased in the recent years. We explore the implications of this shift in terms of wage dynamics. Our characterization is based on the estimation of informal pay gaps at the mean and along the wage distribution, relying on the Russian Longitudinal Monitoring Survey for 2003–2017. Our approach comprises three original features: we rely on unconditional quantile effects of informality, we incorporate quantile-specific fixed effects using a tractable approach, and we suggest a treatment of the incidental parameter bias. Over the whole period, informal wage penalties are relatively small and do not suggest heavily segmented labor markets, even at low wage levels. Yet, in the past decade, a substantial negative selection into informal employment and self-employment has taken place, on average and especially at low earnings. Economic downturns and labor market policies have likely contributed to the shakeout of less productive workers in the formal sector, making the low-tier informal sector more of a last resort
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