Estimating the Large-Scale Structure of the Universe Using Quasi-Stellar Object Carbon IV Absorbers
利用类星体碳 IV 吸收体估计宇宙大尺度结构AI 解读 ↓
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利用类星体碳 IV 吸收体估计宇宙大尺度结构AI 解读 ↓
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Galaxies have long been known to form large clusters, and cosmologists are interested in characterizing this clustering as a way of studying the large-scale structure of the universe. This work is motivated by a data catalog consisting of information on lines of sight from Earth to distant quasi-stellar objects (QSO's) and the carbon IV absorbers that lie on them. The absorbers are believed to be gas clouds near galaxies too far away to be easily observed. Thus, the absorber catalog provides a unique and interesting way to examine the large-scale structure of the universe. On large scales previous studies have mainly used pairs of absorbers on the same lines of sight to obtain estimates describing the clustering of absorbers. It is clear that absorbers on different lines of sight contain information about the degree of clustering. We develop an adaptation of the rigid motion corrected estimator of the reduced second-moment function for the absorber catalog taking into account the across-line-of-sight information. We show how to compute this estimator efficiently using the weights for an isotropic estimator proposed in a recent study. We also show how the modified versions of the rigid motion estimators can be obtained. Simulations suggest that using the modified rigid motion correction estimator may reduce standard errors by 5–20%% on scales from 50 to 250 h−1 Mpc for a set of 100 lines of sight.
We study inference for continuous linear functionals of solutions to ill-posed inverse problems, including Euclidean parameters arising in nonparametric instrumental variables, proximal causal inference, and shadow-variable models. Although pointwise root-$n$ inference is possible under standard conditions, we show that this need not translate into locally reliable inference. Under a model imposing only minimal regularity conditions, the target is Hellinger discontinuous…
观测因果推断中不完全识别下的相对偏差
To conduct causal inference in observational settings, researchers must rely on certain identifying assumptions. In practice, these assumptions are unlikely to hold exactly. This paper considers the bias of selection-on-observables, instrumental variables, and proximal inference estimates under violations of their identifying assumptions. We develop bias expressions for IV and proximal inference that show how violations of their respective assumptions are amplified by any…
Two frequent approaches for identifying structural VARs are external instruments, which carry economic content but are often weak, and non-Gaussianity of the shocks, which provides statistical identification but carries no economic meaning. We combine the two strategies in a single generalized method of moments framework. This hybrid approach point-identifies the target shocks while also identifying the non-target shocks up to sign and ordering. Under suitable rank…
有意过度识别的局部投影
Local projections (LPs) have become a standard tool for estimating dynamic effects in macroeconomics and are typically implemented using ordinary least squares (OLS) for a given lag order. We propose an alternative approach that exploits additional lags as excluded instrumental variables (IVs). Under a finite-order model with martingale-difference shocks, the resulting (optimally weighted) IV estimator is never asymptotically less efficient than OLS and, as we show, can…
IV-LASSO 的工具变量字典与惩罚选择
Estimating the first stage of an instrumental variables (IV) model with the least absolute shrinkage and selection operator (LASSO) requires choosing a dictionary of technical instruments and a penalty level. First-order asymptotic theory offers no guidance on these choices, as any consistent implementation yields a structural parameter estimator with the same limiting distribution. In finite samples, however, these choices can have a substantial impact on the resulting…
非线性矩模型的高效随机广义矩估计
We study the asymptotic properties of the nonlinear stochastic GMM algorithm proposed by Chen, Lee, Liao, Seo, Shin and Song for recursively estimating parameters defined by possibly nonlinear and overidentified moment restrictions $\E[g(X,β^*)]=0$. Each observation updates estimates of the moment Jacobian, inverse covariance matrix and Gauss--Newton preconditioner at a cost independent of sample size. For linear and nonlinear instrumental-variable moments, we derive an…