Generalized Method of Moments Estimation When a Parameter Is on a Boundary

By Donald W. K. Andrews. 2002

151 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5953a8e3f45972f6290546096004e22a074edb71

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Citations in the corpus

Determinants of Structural Change (2013, background): “Andrews, D. (1999): “Estimation When a Parameter is on a Boundary,” Econometrica, 67, pp. 1341–1383. ——— (2000): “Inconsistency of the Bootstrap when a Parameter Is on the Boundary of the Parameter Space,” Econometrica, 68, 399–406. ——— (2002): “Generalized Method of Moments Estimation When a Parame”
Inferring Welfare Maximizing Treatment Assignment Under Budget Constraints (2012, unknown): “[2] Andrews, Donald.”
Generic results for establishing the asymptotic size of confidence sets and tests (2020, background): “…Cox (2016), and Han and McCloskey (2019); for parameters near a 30 boundary, Chernoff (1954), Self and Liang (1987), Shapiro (1989), Geyer (1994), Andrews (1999, 2001, 2002), Andrews and 31 Guggenberger (2010b), and McCloskey (2017); and for post-model selection inference, Kabaila (1995), Leeb…”
Poisson QMLE of Count Time Series Models (2016, methodology): “Estimators with similar asymptotic distributions have been studied by, for example, Andrews (1999), Francq and Zakoian (2009) and the references therein.”
On the second-order properties of empirical likelihood with moment restrictions (2007, methodology): “Here we only cite the latest reviews of Andrews (2002), Brown and Newey (2002), Imbens (2002) and Hansen and West (2002).”
Testing Negative Error Variances (2012, background): “If estimates are at the boundary (e.g., the variance of an error term is equal to zero), the estimates and statistical tests behave in unusual ways (Chernoff 1954, Andrews 1999, Andrews 2001).”
Bootstrap Unit Root Tests in Panels with Cross-Sectional Dependency (2004, background): “The framework within which we may e ectively deal with the aforementioned problem has been recently developed by Andrews (1999).8 To deal with the problem, we may replace zeros for the members of ̂GT and ̂OT which have positive values.”
DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS (2017, methodology): “Combining consistency of the LS estimator and the expansion of the profile objective function in Theorem 4.1, one obtains √ N T WN T ( β̂ − β0) = CN T + op(1); see, for example, Andrews (1999).”
Statistical Inference for Average Treatment Effects Estimated by Synthetic Control Methods (2019, methodology): “We also conducted simulations of Andrews’ (2003) test under DGP1 using T1 = 90 and T2 = 20 (the same T1 and T2 as in our empirical data).”
Weak Instrument Robust Tests in GMM and the New Keynesian Phillips Curve (2009, methodology): “For example, they are almost the same if we use the quadratic spectral kernel estimator proposed by Andrews and Monahan (1992) or the MA-l estimator proposed by West, K.”
Select the Valid and Relevant Moments: An Information-Based LASSO for GMM with Many Moments (2013, methodology): “Similar stochastic equicontinuity conditions are employed in Pakes and Pollard (1989), Andrews (2002), and Chen, Linton, van Keilegom (2003), among others.”
Testing the Number of Components in Normal Mixture Regression Models (2015, methodology): “Under this reparameterization, the log-likelihood function is locally approximated by a quadratic function of polynomials of parameters, and a standard analysis goes through with some adjustment using the results of Andrews (1999) and Zhu and Zhang (2006), who generalize Andrews (1999). We propose a”
Related and unrelated variety as regional drivers of enterprise productivity and innovation: A multilevel study (2016, background): “Zero random effects have been discussed in the literature (Andrews, 1999; Self and Liang, 1987), and in our context it implies that the variation within regions (with reference to enterprise productivity) is not lower than the variation across the whole sample.”
QML INFERENCE FOR VOLATILITY MODELS WITH COVARIATES (2015, methodology): “We thus retrieve (13) under the conditions given by Francq and Zakoïan (2007) in the particular case of GARCH models with (ηt) iid and r = 0 (see also Andrews (1999) and the references therein for the boundary problem in a more general estimation framework).”