Lynda Khalaf

Biographic Data

ID6788507
NAMELynda Khalaf
GIVEN NAMESLynda
FAMILY NAMEKhalaf
SIGNATUREKHALAF L
AFFILIATIONSCarleton University
ORCID0000-0001-8833-5232
VERIFIEDYes
TOTAL WORKS7
TOTAL CITATIONS0
AUTHOR COUNT6
EDITOR COUNT1
FIRST PUBLICATION YEAR2007
LATEST PUBLICATION YEAR2025
H-INDEX0

  • Finite‐Sample Identification‐Robust Inference for Nonlinear DSGE Models

    Open AccessLynda Khalaf, Zhenjiang Lin et al.ARTICLEJournal of Applied Econometrics2025

    We develop identification‐robust likelihood‐free simultaneous confidence sets for dynamic stochastic general equilibrium models, without relying on linear approximations. Our methodology integrates simulation estimation methods using auxiliary statistics with Monte Carlo test principles. Results cover deep parameters and impulse responses. Auxiliary statistics include coefficients of linear and nonlinear vector autoregressions and local projectio…

  • Identification-robust methods for comparing inequality with an application to regional disparities

    Open AccessJean‐Marie Dufour, Emmanuel Flachaire et al.ARTICLEThe Journal of Economic Inequality2024References: 4

    We propose Fieller-type methods for inference on generalized entropy inequality indices in the context of the two-sample problem which covers testing the statistical significance of the difference in indices, and the construction of a confidence set for this difference. In addition to irregularities arising from thick distributional tails, standard inference procedures are prone to identification problems because of the ratio transformation that …

  • Identification-Robust Inference With Simulation-Based Pseudo-Matching

    Bertille Antoine, Lynda Khalaf et al.ARTICLEJournal of Business and Economic…2023

    We develop a general simulation-based inference procedure for partially specified models. Our procedure is based on matching auxiliary statistics to simulated counterparts where nuisance parameters are calibrated neither assuming identification of parameters of interest nor a one-to-one binding function. The conditions underlying the asymptotic validity of our (pseudo-)simulators in conjunction with appropriate bootstraps are characterized beyond…

  • Confidence Sets for Inequality Measures

    Open AccessJean‐Marie Dufour, Jean-Marie Dufour et al.CHAPTERProductivity and Inequality2018

  • Productivity and Inequality

    Open AccessWilliam H-undifferentiated Greene, Michael R Veall et al.BOOKProductivity and Inequality2018

  • Permutation Tests for Comparing Inequality Measures

    Jean‐Marie Dufour, Jean-Marie Dufour et al.ARTICLEJournal of Business and Economic…2017

    Asymptotic and bootstrap tests for inequality measures are known to perform poorly in finite samples when the underlying distribution is heavy-tailed. We propose Monte Carlo permutation and bootstrap methods for the problem of testing the equality of inequality measures between two samples. Results cover the Generalized Entropy class, which includes Theil’s index, the Atkinson class of indices, and the Gini index. We analyze finite-sample and asy…

  • Multivariate Tests of Mean–Variance Efficiency With Possibly Non-Gaussian Errors

    Marie‐Claude Beaulieu, Marie-Claude Beaulieu et al.ARTICLEJournal of Business and Economic…2007

    We develop exact mean–variance efficiency tests of the market portfolio in the context of (conditional and unconditional) capital asset pricing models (CAPM), allowing for a wide class of possibly non-Gaussian error distributions. The proposed procedures are applicable in a general multivariate linear regression framework, and exactness is achieved through Monte Carlo test techniques. We also perform exact multivariate diagnostic checks. Empirica…