2021

Volume 24, n° 2, May 2021

  • Chen L, Huo Y. A simple estimator for quantile panel data models using smoothed quantile regressions, pp. 247-263. doi:10.1093/ectj/utaa023
  • Lee S, Shin Y. Complete subset averaging with many instruments, pp. 290-314. doi:10.1093/ectj/utaa033
  • Semenova V, Chernozhukov V. Debiased machine learning of conditional average treatment effects and other causal functions, pp. 264-289. doi:10.1093/ectj/utaa027
  • Lee S, Gørgens T. Estimation of dynamic models of recurrent events with censored data, pp. 199-224. doi:10.1093/ectj/utaa028
  • Karabiyik H, Westerlund J. Forecasting using cross-section average–augmented time series regressions, pp. 315-333. doi:10.1093/ectj/utaa031
  • Honoré BE, de Paula Á. Identification in simple binary outcome panel data models, pp. C78-C93. doi:10.1093/ectj/utab010
  • Fernández-Val I, Freeman H, Weidner M. Low-rank approximations of nonseparable panel models, pp. C40-C77. doi:10.1093/ectj/utab007
  • Tuğan M. Panel VAR models with interactive fixed effects, pp. 225-246. doi:10.1093/ectj/utaa021
  • Aihounton GBD, Henningsen A. Units of measurement and the inverse hyperbolic sine transformation, pp. 334-351. doi:10.1093/ectj/utaa032
  • Heckman JJ, Karapakula G. Using a satisficing model of experimenter decision-making to guide finite-sample inference for compromised experiments, pp. C1-C39. doi:10.1093/ectj/utab009

(résumés du n° 2/2021)

Volume 24, n° 1, January 2021

  • Chen L-Y, Lee S. Binary classification with covariate selection through ℓ0-penalised empirical risk minimisation, pp. 103-120. doi:10.1093/ectj/utaa017
  • Erratum to: Testing Identification via Heteroskedasticity in Structural Vector Autoregressive Models, pp. 198-198. doi:10.1093/ectj/utaa015
  • Kejriwal M, Yu X. Generalized Forecast Averaging in Autoregressions with a Near Unit Root, pp. 83-102. doi:10.1093/ectj/utaa006
  • Fosgerau M, Kristensen D. Identification of a class of index models: A topological approach, pp. 121-133. doi:10.1093/ectj/utaa016
  • Rossi B. Identifying and estimating the effects of unconventional monetary policy: How to do it and what have we learned? The Econometrics Journal. 2021;24(1):C1-C32. doi:10.1093/ectj/utaa020
  • Ginker T, Lieberman O. LSTUR regression theory and the instability of the sample correlation coefficient between financial return indices, pp. 58-82. doi:10.1093/ectj/utaa011
  • Knaus MC, Lechner M, Strittmatter A. Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence, pp. 134-161. doi:10.1093/ectj/utaa014
  • Zhu R, Zhang X, Ma Y, Zou G. Model averaging estimation for high-dimensional covariance matrices with a network structure, pp. 177-197. doi:10.1093/ectj/utaa030
  • Cai M, Del Negro M, Herbst E, Matlin E, Sarfati R, Schorfheide F. Online estimation of DSGE models, pp. C33-C58. doi:10.1093/ectj/utaa0290.
  • Xu R. Potential outcomes and finite-population inference for M-estimators, pp. 162-176. doi:10.1093/ectj/utaa022
  • Royal Economic Society Annual Conference 2018 Special Issue on Structural Macroeconometrics, pp. Ci-Ciii. doi:10.1093/ectj/utaa034
  • Fu J-YM, Horowitz JL, Parey M. Testing exogeneity in nonparametric instrumental variables models identified by conditional quantile restrictions, pp. 23-40. doi:10.1093/ectj/utaa007
  • Lütkepohl H, Meitz M, Netšunajev A, Saikkonen P. Testing identification via heteroskedasticity in structural vector autoregressive models, pp. 1-22. doi:10.1093/ectj/utaa008
  • Bera A, Montes-Rojas G, Sosa-Escudero W, Alejo J. Tests for nonlinear restrictions under misspecified alternatives with an application to testing rational expectation hypotheses, pp. 41-57. doi:10.1093/ectj/utaa010

(résumés n° 1/2021)

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