WHAT DO QUANTILE REGRESSIONS IDENTIFY FOR GENERAL STRUCTURAL FUNCTIONS?

2014 ◽  
Vol 31 (5) ◽  
pp. 1102-1116 ◽  
Author(s):  
Yuya Sasaki
Keyword(s):  
Partial Derivative ◽  
General Class ◽  
Weighted Average ◽  
Causal Effects ◽  
Quantile Regressions ◽  
Conditional Quantile ◽  
Mild Conditions ◽  
Structural Functions ◽  
Partial Effects

This paper shows what quantile regressions identify for general structural functions. Under fairly mild conditions, the quantile partial derivative identifies a weighted average of heterogeneous structural partial effects among the subpopulation of individuals at the conditional quantile of interest. This result justifies the use of quantile regressions as means of measuring heterogeneous causal effects for a general class of structural functions with multiple unobservables.

scholarly journals ON USING LINEAR QUANTILE REGRESSIONS FOR CAUSAL INFERENCE

2016 ◽  
Vol 33 (3) ◽  
pp. 664-690 ◽  
Author(s):  
Ryutah Kato ◽  
Yuya Sasaki
Keyword(s):  
Causal Inference ◽  
Unobserved Heterogeneity ◽  
Weighted Average ◽  
Quantile Function ◽  
Structural Function ◽  
Slope Parameter ◽  
Quantile Regressions ◽  
Conditional Quantile ◽  
Conditional Quantile Function ◽  
Linear Regressions

We show that the slope parameter of the linear quantile regression measures a weighted average of the local slopes of the conditional quantile function. Extending this result, we also show that the slope parameter measures a weighted average of the partial effects for a general structural function. Our results support the use of linear quantile regressions for causal inference in the presence of nonlinearity and multivariate unobserved heterogeneity. The same conclusion applies to linear regressions.

scholarly journals Recentered influence functions (RIFs) in Stata: RIF regression and RIF decomposition

2020 ◽  
Vol 20 (1) ◽  
pp. 51-94 ◽  
Author(s):  
Fernando Rios-Avila
Keyword(s):  
Fixed Effects ◽  
Decomposition Analysis ◽  
High Dimensional ◽  
Large Set ◽  
Influence Functions ◽  
Analysis Framework ◽  
Quantile Regressions ◽  
Rif Regression ◽  
Linear Regressions ◽  
Partial Effects

Recentered influence functions (RIFs) are statistical tools popularized by Firpo, Fortin, and Lemieux (2009 , Econometrica 77: 953–973) for analyzing unconditional partial effects on quantiles in a regression analysis framework (unconditional quantile regressions). The flexibility and simplicity of these tools have opened the possibility to extend the analysis to other distributional statistics using linear regressions or decomposition approaches. In this article, I introduce one function and two commands to facilitate the use of RIFs in the analysis of outcome distributions: rifvar() is an egen extension used to create RIFs for a large set of distributional statistics, rifhdreg facilitates the estimation of RIF regressions enabling the use of high-dimensional fixed effects, and oaxaca_rif implements Oaxaca–Blinder decomposition analysis (RIF decompositions).

Convergence in Probability on a Big Class of Time-Variant Evolutionary Algorithms

2019 ◽  
Vol 33 (06) ◽  
pp. 1959018
Author(s):  
Ming Chen ◽  
Yunwen Lei ◽  
Lixin Ding ◽  
Zhao Tong
Keyword(s):  
Global Convergence ◽  
Evolutionary Algorithms ◽  
Markov Chains ◽  
General Class ◽  
State Of The Art ◽  
General Theorem ◽  
Convergence In Probability ◽  
Simulation Experiments ◽  
Concrete Case ◽  
Mild Conditions

Motivated by the growing popularity of time-variant evolutionary algorithms (EAs) in solving practical problems, this paper uses spectral analyses to study convergence in probability for a general class of time-variant EAs which can be asymptotically described by reducible Markov chains with multiple aperiodic recurrent classes, covering many existing concrete case studies as specific instantiations. We provide a universal yet easily checkable characteristic for time-variant EAs satisfying global convergence, by introducing the asymptotical elitism and asymptotical monotonicity. To illustrate the effectiveness of our result, we consider four specific EAs with distinct asymptotical behavior, and recover, under even mild conditions, the state-of-the-art result as simple applications of our general theorem. Besides, simulation experiments further verify these results.

scholarly journals NONPARAMETRIC WEIGHTED AVERAGE QUANTILE DERIVATIVE

2021 ◽  
pp. 1-39
Author(s):  
Ying-Ying Lee
Keyword(s):  
Linear Models ◽  
Weighted Average ◽  
Linear Representation ◽  
Quantile Function ◽  
Structural Effect ◽  
Compact Set ◽  
Conditional Quantile ◽  
Partially Linear ◽  
Conditional Quantile Function ◽  
Quantile Processes

The weighted average quantile derivative (AQD) is the expected value of the partial derivative of the conditional quantile function (CQF) weighted by a function of the covariates. We consider two weighting functions: a known function chosen by researchers and the density function of the covariates that is parallel to the average mean derivative in Powell, Stock, and Stoker (1989, Econometrica 57, 1403–1430). The AQD summarizes the marginal response of the covariates on the CQF and defines a nonparametric quantile regression coefficient. In semiparametric single-index and partially linear models, the AQD identifies the coefficients up to scale. In nonparametric nonseparable structural models, the AQD conveys an average structural effect under certain independence assumptions. Including a stochastic trimming function, the proposed two-step estimator is root-n-consistent for the AQD defined by the entire support of the covariates. To facilitate tractable asymptotic analysis, a key preliminary result is a new Bahadur-type linear representation of the generalized inverse kernel-based CQF estimator uniformly over the covariates in an expanding compact set and over the quantile levels. The weak convergence to Gaussian processes applies to the differentiable nonlinear functionals of the quantile processes.

scholarly journals Gender differences in wage expectations: the role of biased beliefs

2021 ◽  
Author(s):  
Stephanie Briel ◽  
Aderonke Osikominu ◽  
Gregor Pfeifer ◽  
Mirjam Reutter ◽  
Sascha Satlukal
Keyword(s):  
Gender Differences ◽  
University Students ◽  
Gender Gap ◽  
Quantile Regressions ◽  
Conditional Quantile ◽  
Lower Wage

AbstractWe analyze gender differences in expected starting salaries along the wage expectations distribution of prospective university students in Germany, using elicited beliefs about both own salaries and salaries for average other students in the same field. Unconditional and conditional quantile regressions show 5–15% lower wage expectations for females. At all percentiles considered, the gender gap is more pronounced in the distribution of expected own salary than in the distribution of wages expected for average other students. Decomposition results show that biased beliefs about the own earnings potential relative to others and about average salaries play a major role in explaining the gender gap in wage expectations for oneself.

scholarly journals COMPLETE SUBSET AVERAGING FOR QUANTILE REGRESSIONS

2021 ◽  
pp. 1-43
Author(s):  
Ji Hyung Lee ◽  
Youngki Shin
Keyword(s):  
Asymptotic Optimality ◽  
Model Averaging ◽  
Prediction Method ◽  
Equal Weight ◽  
Finite Sample ◽  
Quantile Regressions ◽  
Usual Model ◽  
Conditional Quantile ◽  
Proper Size ◽  
Leave One Out

We propose a novel conditional quantile prediction method based on complete subset averaging (CSA) for quantile regressions. All models under consideration are potentially misspecified, and the dimension of regressors goes to infinity as the sample size increases. Since we average over the complete subsets, the number of models is much larger than the usual model averaging method which adopts sophisticated weighting schemes. We propose to use an equal weight but select the proper size of the complete subset based on the leave-one-out cross-validation method. Building upon the theory of Lu and Su (2015, Journal of Econometrics 188, 40–58), we investigate the large sample properties of CSA and show the asymptotic optimality in the sense of Li (1987, Annals of Statistics 15, 958–975) We check the finite sample performance via Monte Carlo simulations and empirical applications.

Toward a Unified Theory of Causality

2008 ◽  
Vol 41 (4-5) ◽  
pp. 412-436 ◽  
Author(s):  
James Mahoney
Keyword(s):  
Comparative Research ◽  
Population Level ◽  
Causal Effects ◽  
Unified Theory ◽  
Large Populations ◽  
The Face ◽  
Research Analysts ◽  
Causal Language ◽  
Partial Effects ◽  
Oriented Research

In comparative research, analysts conceptualize causation in contrasting ways when they pursue explanation in particular cases (case-oriented research) versus large populations (population-oriented research). With case-oriented research, they understand causation in terms of necessary, sufficient, INUS, and SUIN causes. With population-oriented research, by contrast, they understand causation as mean causal effects. This article explores whether it is possible to translate the kind of causal language that is used in case-oriented research into the kind of causal language that is used in population-oriented research (and vice versa). The article suggests that such translation is possible, because certain types of INUS causes manifest themselves as variables that exhibit partial effects when studied in population-oriented research. The article concludes that the conception of causation adopted in case-oriented research is appropriate for the population level, whereas the conception of causation used in population-oriented research is valuable for making predictions in the face of uncertainty.

Summer phytoplankton of the northern Barents Sea (75–80º N)

2019 ◽  
pp. 8-19
Author(s):  
Larisa A. Pautova ◽  
Vladimir A. Silkin ◽  
Marina D. Kravchishina ◽  
Valeriy G. Yakubenko ◽  
Anna L. Chultsova
Keyword(s):  
Barents Sea ◽  
Weighted Average ◽  
Arctic Basin ◽  
High Arctic ◽  
Alexandrium Tamarense ◽  
Warm Water ◽  
The Arctic ◽  
Absolute Maximum ◽  
Deep Trench ◽  
Maximum Biomass

The structure of the summer planktonic communities of the Northern part of the Barents sea in the first half of August 2017 were studied. In the sea-ice melting area, the average phytoplankton biomass producing upper 50-meter layer of water reached values levels of eutrophic waters (up to 2.1 g/m3). Phytoplankton was presented by diatoms of the genera Thalassiosira and Eucampia. Maximum biomass recorded at depths of 22–52 m, the absolute maximum biomass community (5,0 g/m3) marked on the horizon of 45 m (station 5558), located at the outlet of the deep trench Franz Victoria near the West coast of the archipelago Franz Josef Land. In ice-free waters, phytoplankton abundance was low, and the weighted average biomass (8.0 mg/m3 – 123.1 mg/m3) corresponded to oligotrophic waters and lower mesotrophic waters. In the upper layers of the water population abundance was dominated by small flagellates and picoplankton from, biomass – Arctic dinoflagellates (Gymnodinium spp.) and cold Atlantic complexes (Gyrodinium lachryma, Alexandrium tamarense, Dinophysis norvegica). The proportion of Atlantic species in phytoplankton reached 75%. The representatives of warm-water Atlantic complex (Emiliania huxleyi, Rhizosolenia hebetata f. semispina, Ceratium horridum) were recorded up to 80º N, as indicators of the penetration of warm Atlantic waters into the Arctic basin. The presence of oceanic Atlantic species as warm-water and cold systems in the high Arctic indicates the strengthening of processes of “atlantificacion” in the region.

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