On Varieties Of Doubly Robust Estimators Under Missingness Not At Random With A Shadow Variable

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An Introduction to Difference and System GMM in Stata

by D Roodman 2009 Cited by 8272 lano and Bover 1995; Blundell and Bond 1998) dynamic panel estimators are where β is a column vector of coefficients, y and ε are random variables, vector would double the weight of poverty at the expense of other instruments. In mates would not be robust to heteroskedasticity or serial correlation in the errors.

Missing-Values Adjustment for Mixed-Type Data - CORE

by A Tarsitano 2011 Cited by 9 a variety of settings in coordination with imputation of the least power mean of the of missing values in the target variable even when some data are absent from incomplete records, they introduce bias into the estimation/prediction process. each of them is linked to a specific type of variable and not to the globality of 

Semiparametric response model with nonignorable

by M Uehara 2018 Cited by 1 Two types of efficient estimators, ness for the study variable is conditionally independent of that the response mechanism is called missing not at random or different from other doubly robust form estimators in the nonignorable estimators under missingness not at random with a shadow variable.

CURRICULUM VITAE Wang Miao

17 Mar 2021 Miao W. and Tchetgen Tchetgen E. (2016). On varieties of doubly robust estimators under missingness not at random with a shadow variable.

Statistica Sinica Preprint No: SS-2018-0466

by NM Confounders Key words and phrases: Causal inference, doubly robustness, outcome-​independent missingness, nonignorable missing, shadow variable. 1. Introduction There are two types of missing-data as missing at random. When data are In this study, we focus on a semiparametric estimation of the causal effect when the 

Policy evaluation, high-dimension and machine learning

16 Jan 2020 combien je suis fier d'avoir effectué ma th`ese au CREST, dont le Covariate Balancing Weights and Double Robustness The most sparse true model is coded by M0, a random variable taking value R orthogonal estimators in a general framework or double machine As a consequence, the missing.

Identification, Doubly Robust Estimation, and - arXiv

by W Miao 2015 Cited by 5 cation, missing not at random, shadow variable. 1 doubly robust estimators under more stringent identifying conditions, which are inspired by study the performance of a variety of estimators in Section 5 via both a series of 

Research Methods - Johns Hopkins Bloomberg School of

With appropriate and careful design, more robust conclusions can be made. researchers to address issues related to variations in outcomes stems in in intervention response that is governed by post-randomization variables (Jo, 2002a-c). Informative dropout (or missing not at random; MNAR) occurs when MAR fails.

semiparametric estimation with data missing not at random

by BL Sun 2018 Cited by 16 a novel doubly robust estimator of the mean of an outcome subject to MNAR. and Zhao and Shao (2015), under the assumption that a shadow variable is fully In this section, we consider estimation and inference under a variety of semi-.

Breaking the MAR Paradigm: Estimation, Bounding, and

by A Ocampo 2020 Estimation,. Bounding, and Sensitivity When Data Are Missing Not at Random. A shadow variable approach can identify causal effects under MNAR (Miao and Tch- On varieties of doubly robust estimators under missingness not at.

Linear Models and Extensions - Routledge

Estimation and Prediction: Classical Approach (Chapter 5) from Linear Models and the Relevant is not only the mean of that random variable, but much more importantly, example of this, though, is working with missing data, in which we wish to were doing a better job than others, while correcting for variations in age.

Improving estimation efficiency for regression with MNAR

by M Che 2020 Cited by 1 and Tchetgen Tchetgen, E.J. (2016) On varieties of doubly robust estimators under missingness not at random with a shadow variable. Biometrika 

Imputation of the 1989 Survey of Consumer Finances

by AB Kennickell 1991 Cited by 171 Assets include the principal residence, all types of loans made to others, real the imputation of the missing values cannot be addressed routinely using Every variable in the survey has a shadow variable that reflects the most desirable approach would be to use robust estimation techniques for the covariance matrix.

On varieties of doubly robust estimators under missingness

by W Miao 2016 Cited by 33 Some key words: Doubly robust estimation; Missingness not at random; Shadow variable. 1. INTRODUCTION. Doubly robust methods are 

Handbook of Causal Analysis for Social Research

Fixed Effects, Random Effects, and Hybrid Models for Causal Analysis 3, ​Types of Causes, Jeremy Freese and J. Alex Kevern lay out the variety of causal under reasonable assumptions that causal effects are not constant and additive, but the Because some of the causal variables are missing, the regression.

Conference Program - University of Waterloo

19 Oct 2019 The Waterloo Student Conference in Statistics, Actuarial Science and. Finance is an YILIN CHEN Doubly Robust Inference with Non-probability Survey General Estimating Equations under Density Ratio Models For regression with covariates missing not at random (MNAR) where the missingness.

The Internal Ratings-Based Approach - Bank for International

(iii) Treatment of maturity under the advanced IRB approach robustness of the banks' internal rating system and overall credit risk the substitution approach will not give recognition to the so-called double default effect of variable (i.e. the probability that a normal random variable with mean zero and variance of.

Working Paper Number 103 December 2006 - RePEc

by D Roodman 2006 Cited by 8314 individuals; with independent variables that are not strictly exogenous, where β is a column of coefficients, y and ε are random variables, x = [x1 xk] is a would be minimal, yet including it in the moment vector would essentially double the weight of from Stata estimation commands with the robust and cluster options.

Regression modelling of correlated data in ecology: subject

by J Fieberg 2009 Cited by 69 particular, a SS interpretation is not natural for covariates that do not vary within individuals (e.g. models, marginal model, mixed effects, random effects, sandwich estimators clutch size in two types of nest structures (single- and double-cylin- Bang, H. & Robins, J.M. (2005) Doubly robust estimation in missing data and.

Statistica Sinica Preprint No: SS-2016-0324.R2

by B Sun Specifically, we propose a novel doubly robust estimator of the mean of an out- come subject to Key words and phrases: Instrumental variable, Missing not at random, Inverse and Shao (2015), under the assumption that a shadow variable is fully In this section, we consider estimation and inference under a variety.

Stable Weights that Balance Covariates for Estimation with

by JR Zubizarreta Cited by 198 outcomes, or a variety of population means for several variables. are missing at random (MAR), weighting methods are used in observational studies weighting method, the researcher does not have to specify a probability model, but Performance of Double-Robust Estimators When Inverse Probability Weights.

Understanding instrumental Variables in Models with

by JJ Heckman 2006 Cited by 939 Missing instruments play no role in identifying the mean responses but may affect the efficiency of the IV estimation. We establish that in a model where.

Public consultation on the ECB guide to internal models risk

European Banking Authority (EBA) Guidelines on PD estimation, LGD estimation and the the credit bureau score continue to be appropriate input variables in their this does not lead to any bias or double-counting effect in the risk parameter The ECB understands a shadow rating model (SRM) to be an internal rating.

Randomized Impact Evaluation in Practice - World Bank

1 Apr 2009 Double-Difference Method FONCODES was not allocated randomly, for example, and There are two types of quantitative impact evaluations: ex post and ex ante. An ex An impact evaluation is essentially a problem of missing data, term in the estimating equation will contain variables that are also 

The Long Shadow of the Chinese Cultural Revolution - EconStor

by X Meng 2016 Cited by 11 Any opinions expressed in this paper are those of the author(s) and not those of IZA. In addition to cross-cohort variations on schooling interruptions, for the same cohort child has missing values for the education variable, and where the age using the few-cluster cluster robust variance estimate (CR2VE, the SEs are​ 

Nonignorable missing data, single index propensity score and

by X CHEN 2021 Abstract: In missing data problems, missing not at random is difficult to handle since the response used instrumental variables for identifiability, we use a semi​- lihood estimators for drawing inferences and Shao and Wang There are a variety of Doubly Robust Estimation, and Semiparametric Efficiency Theory of.

Shadow Economy - IMF eLibrary - International Monetary Fund

GDP, therefore the definition of the shadow economy in our study tries to avoid indicator variables such as the light intensity approach, as well as to present We also run a variety of robustness The problem of double counting (​MCAR), missing at random (MAR) or missing not at random (MNAR), (Little and Rubin,.

Corporate foreign bond issuance and interfirm loans in China

by Y Huang 2021 Cited by 17 to do more inter-firm lending than firms in non-risky sectors. Moreover, this the shadow banking characteristics of entrusted loans. have missing observations)​. by exports over revenues), and a dummy variable that takes a value of one for firms that belong We use robust standard errors double clustered at the.

QUANTILE REGRESSION 40 YEARS ON 1. Introduction

by R KOENKER Cited by 91 Some recent developments in quantile regression methods are surveyed below. As long as treatment is randomly assigned estimation of the QTE is easily impl The extensive recent work on so-called doubly-robust methods that combine these Errors in Variables, Missing Data and Sample Selection.

LABOR SUPPLY MODELS - UCL

by R BLUNDELL 2007 Cited by 122 Maximum likelihood: Accounting for participation and missing wages. 4721. 4.8.1​. qt and ht do not depend on each other then we say that the utility function is additively behavior, while retaining a three-parameter linear-in-variables form, is that used in random denominators from the kernel regression estimators.

Correcting for self-selection based endogeneity in - econstor

by J Clougherty 2015 Cited by 15 missing when standard regression techniques are employed. variable by managers (i.e., not randomly assigned by the researcher), thus it is quite possible that new market niche as adequate counterfactuals in order to estimate the endogenous switching model resembles a 'double' sample-selection process and can 

SAFE Working Paper No. 79 - Leibniz Institute for Financial

by ÒJAM Taylor 2014 Cited by 367 the application of an estimator from the family of doubly robust augmented of this section, Dt is a continuous random variable although later in the paper, we will nonlinearity and do a stratification of the results; here we found significant variations in Inverse Probability Weighted M-Estimation for General Missing Data.

A Nuisance-Free Inference Procedure Accounting for - MDPI

by J Zhao 2020 the true missingness mechanism tends to be missing not at random, e.g., patient the covariate X. In general, a shadow variable with a smaller E.J. On varieties of doubly robust estimators under missingness not at random.

Slack Resources and Technology-Mediated Human Capital

by J Jabbari 2020 a variety of robustness and mechanism checks, we find that variable for gig employment was developed from the question In the past year, did you earn any As the decision to participate in online learning and the gig economy is not random and may be Doubly robust estimation in missing data and causal inference.

Estimating the Average Treatment Effect of Fiscal Policy

by Ò Jordà 2013 Cited by 367 one half followed the prescriptions of the doctors of the variety of those The boom, not the slump, is the right time for austerity at the Treasury. into the broad class of doubly robust estimators of which Robins, Rotnitzky and section, Dt is a continuous random variable although later in the paper, we will treat Dt as a.

CURRICULUM VITAE Wang Miao

On varieties of doubly robust estimators under missingness not at random with a shadow variable. Biometrika, 103:475 482. 3. Miao W., Geng Z., and Tchetgen 

PDF download - Institute for Economic Studies, Keio

by T Hoshino 2019 moments in random sampling data in order to correct the bias. doubly robust-​type estimator for biased sampling data. which there is no missingness in variables of interest y. We conduct two types of simulation studies when auxiliary information is Shadow prices, market wages, and labor supply.

A Penalized Likelihood Approach for Statistical Inference in a

by J Zhao 2020 We adopt the shadow variable approach in this paper primarily by virtue of missing-not-at-random (MNAR). We term Z the Miao, W. and Tchetgen Tchetgen, E. J. On varieties of doubly robust estimators under missingness not at random.

Estimating the Average Treatment Effect of Fiscal Policy

by Ò Jordà 2013 Cited by 367 one half followed the prescriptions of the doctors of the variety of those The boom, not the slump, is the right time for austerity at the Treasury. into the broad class of doubly robust estimators of which Robins, Rotnitzky and Zhao (​1994) section, Dt is a continuous random variable although later in the 

D epartm ent of E conom ics Working Paper Series

by D Qin Cited by 2 rates of non- working wives under the missing-at-random (MAR) condition. from the tobit estimator which deals with truncated variables. Under this approach, SB amounts to asserting that the missing wage rates are missing not at random Two 'doubly robust' methods are thus adopted here: (a) inverse probability.

Double robustness - WordPress.com

of some covariates, not of intrinsic interest, in order to estimate the values of incomplete variables and the propensity to have them observed, even if these auxiliary 2.1 Estimating a population mean from incomplete data under missing at random E.J. (2016) On varieties of doubly robust estimators under missingness.

calibration and multiple robustness when data are missing not

by P Han 2018 Cited by 7 calibration to construct a multiply robust estimator when data are missing not at random over the well-known double robustness (e.g. Scharfstein, Rotnitzky and Robins. (1999) Calibration in missing data literature has two major variations. an instrumental or shadow variable, that, conditional on the rest of X, is.

A National Analysis of Music Coursetaking, Social-Emotional

by BP Shaw 2020 I addressed missing data through multiple imputation using chained equations. The analysis involved a doubly robust approach combining propensity that, without Julia Shaw's success and support, there is no scenario in which I would be finishing Variables Used for Propensity Score Estimation: Demographics

Improving estimation efficiency for regression with - Deep Blue

by M Che 2020 Cited by 1 Summary: For regression with covariates missing not at random (MNAR) where the missingness depends on the missing probability conditional on all fully observed variables in the data set. With this model On varieties of doubly robust estimators under missingness not at random with a shadow variable. Biometrika 

Final thesis file - Minerva Access - University of Melbourne

by X Wang 2020 propose a doubly robust estimator to unbiasedly measure the error of 4.2 The types of intent hierarchies in TREC Web Track 2009-2013 test collec- a critical problem is that the missing ratings are often missing not at random dom variable in a label (output) space Y. Let prpy xq be the real data Shadow Man (​‹).

Identification of Causal Effects Using Instrumental Variables

Economists are typically interested in estimating causal interact and the variety of possible treatments are required. if not completely random (Rubin 1978), we assume random ing, double blinding, and using placebos in clinical trials. Missing Data, Journal of the American Statistical Association, 90, 106-. 121.

Shadow Economy: Estimation Methods, Problems, Results

by F Schneider 2018 Cited by 109 estimating the size and development of the shadow economy and results Table 1: A taxonomy of types of underground economic activities Causal variable/No. In general, the selection of taxpayers for tax audits is not random but the danger of a double counting problem due to the inclusion of the 

Package 'MASS'

by B Ripley 2020 Cited by 471 form a nested sequence of models, but need not be specified in any particular Integrate a function of one variable over a finite range using a recursive adaptive method. This Dodge Shadow and Plymouth Sundance) were listed at most once. R. G. Staudte and S. J. Sheather (1990) Robust Estimation and Testing.

AAAI-21 Accepted Paper List.1.29.21 - Association for the

416: Robust Reinforcement Learning: A Case Study in Linear Quadratic Estimation in Monocular Videos 1409: Action Candidate Based Clipped Double Q-‐Learning for Discrete and 1726: Instrumental Variable-‐Based Identification for Causal Dffects Using Covariate Estimation on Missing-‐Not-‐​at-‐Random Data.

Policy Evaluation with Latent Confounders via Optimal Balance

by A Bennett 2019 Cited by 9 assumption of no unobserved confounders, which is bound to fail in practice. actually fit outcome prediction models and doubly robust estimators that work represents the latent variable model underlying the observables. In particular, there is a variety of work that studies sufficient Rademacher random variables.