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高频委托报价中的跳跃检测AI 解读

We propose methods to infer jumps of a semi-martingale, which describes long-term price dynamics, based on discrete, noisy, high-frequency observations. Different to the classical model of additive, centered market microstructure noise, we consider one-sided microstructure noise for order prices in a limit order book. We develop methods to estimate, locate and test for jumps using local minima of best ask quotes. We provide a local jump test and show that we can consistently…

含交互固定效应的高维离散选择模型及其因果推断应用AI 解读

ABSTRACT We propose a two‐step procedure to estimate a high dimensional discrete choice panel with interactive fixed effects where the initial and final estimators are obtained via a nuclear‐norm regularized (NNR) maximum likelihood estimation and post‐NNR iterated estimation, respectively. We apply the method to make counterfactual predictions of choice probabilities. Simulations demonstrate nice finite sample performance in estimation and tests. An illustrative application…

检验预测效率的工具变量方法AI 解读

ABSTRACT We study a specific form of forecast efficiency that requires forecast errors to be unpredictable from forecast revisions. One approach aggregates forecasts and estimates an aggregated efficiency regression, while another estimates the relationship by running separate regressions for each individual and then aggregating. We demonstrate that both estimators can be asymptotically biased in the presence of public noise. To address these biases, we propose instrumenting…

欧洲总体层面主观幸福感随收入饱和的计量证据AI 解读

ABSTRACT The Easterlin paradox explores the link between subjective well‐being (SWB) and income, questioning if a saturation point exists where more income no longer boosts SWB. In the literature, the studies dealing with a saturation point of SWB at the cross‐sectional level are ambiguous. The results of this study provide evidence for its existence. The EU‐SILC data are used to test for satiation by means of kink regressions and generalized additive models. The unique…

收入增长中不存在的矩AI 解读

ABSTRACT This study addresses the limitations of traditional earnings risk measures, which often rely on moments such as variance, skewness, and kurtosis. For heavy‐tailed distributions, these moments may not exist, challenging such analyses. We propose robust conditional Pareto exponents as novel measures of earnings risk, with accompanying estimation and inference methods. Using UK NESPD and US PSID data, we find (1) moments often fail to exist; (2) tail risk rises over…

检验两个参数的符号一致性AI 解读

ABSTRACT We test the null hypothesis that two parameters have the same sign, assuming that (asymptotically) normal estimators are available. Examples of this problem include the analysis of heterogeneous treatment effects, causal interpretation of reduced‐form estimands, meta‐studies, and mediation analysis. A number of tests were recently proposed. We recommend a test that is simple and rejects more often than many of these recent proposals. Like all other tests in the…

局部稳健且半参数有效的贝叶斯推断AI 解读

We propose a framework for making Bayesian parametric models robust to local misspecification. Suppose in a baseline parametric model, a parameter of interest has an interpretation in an encompassing semiparametric model. Bayesian and maximum likelihood estimators are generally biased under local misspecification. We propose to augment the baseline likelihood by a multiplicative factor that involves scores for the baseline model, the efficient scores for the encompassing…

空间处理的因果推断AI 解读

Many events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units. I approach the spatial treatment setting from an experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between units near realized treatment locations and units near counterfactual (unrealized) candidate locations, which differs from…

含噪协变量的因果推断：异方差测量误差与差分隐私AI 解读

We study causal inference with high‐dimensional covariates, observed with various types of heteroscedastic noise. Our main assumption is that the true covariates are low rank, which we empirically evaluate with plots and interpret as approximate repeated measurements. This assumption delivers semiparametric inference on the causal parameter, as precisely as if the true covariates were available. A methodological contribution is to introduce an error‐in‐variable balancing…

局部投影的双重稳健性与 VAR 推断的隐患AI 解读

We consider impulse response inference in a locally misspecified vector autoregression (VAR) model. The conventional local projection (LP) confidence interval has correct coverage even when the misspecification is so large that it can be detected with probability approaching 1. This result follows from a “double robustness” property analogous to that of popular partially linear regression estimators. By contrast, the conventional VAR confidence interval with…

经济增长与大型企业的崛起AI 解读

I document that the right tail of the firm size distribution systematically thickens with economic development. To rationalize this fact, I develop a parsimonious idea search model in which both aggregate growth and the firm size distribution are endogenously determined. The model features an asymptotic balanced growth path along which Gibrat's law holds at each date, and the right tail of the firm size distribution thickens monotonically toward Zipf's law. The model also…

信念更新中的推断与预测差距AI 解读

Evidence from the laboratory and the field has uncovered both underreaction and overreaction to new information. We provide new experimental evidence on the underlying mechanisms of under‐ and overreaction by comparing how people make inferences and revise forecasts in the same information environment. Participants underreact to signals when inferring about underlying states, but overreact to the same signals when revising forecasts about future outcomes—a phenomenon we term…

工具变量模型中的识别：Abadie 的 Kappa 的核心作用AI 解读

We study instrumental variables models characterized by: (i) Unobserved heterogeneity consisting of potential outcomes and response types that describe how the instrument determines treatment choice; (ii) Conditional independence of the instrument and the unobserved heterogeneity; and (iii) Convex restrictions on the distribution of unobserved heterogeneity. We show certain causal parameters are identified in these models if and only if a version of the kappa of Abadie…

高斯过程最大值位置分布函数的连续性AI 解读

Certain extremum estimators have asymptotic distributions that are non‐Gaussian, yet characterizable as the distribution of the arg max of a Gaussian process. This paper presents high‐level sufficient conditions under which such asymptotic distributions admit a continuous distribution function. The plausibility of the sufficient conditions is demonstrated by verifying them in three examples, namely, maximum score estimation, empirical risk minimization, and threshold…

通过近似后验传达科学不确定性AI 解读

We cast the problem of communicating scientific uncertainty as one of reporting a posterior distribution on an unknown parameter to an audience of Bayesian decision‐makers. We establish novel bounds on the audience's regret when the analyst reports an approximation to a posterior that the audience treats as exact. Under a palatable restriction on the audience's decision problems, the bounds take an especially convenient form. Under a further restriction on the audience's…

估计精度能够预测参数时的经验贝叶斯方法AI 解读

Gaussian empirical Bayes methods usually maintain a precision independence assumption: The unknown parameters of interest are independent from the known standard errors of the estimates. This assumption is often theoretically questionable and empirically rejected. This paper proposes to model the conditional distribution of the parameter given the standard errors as a flexibly parameterized location‐scale family of distributions, leading to a family of methods that we call…

基于最优传输的广义矩估计AI 解读

We propose a novel optimal transport‐based version of the Generalized Method of Moment (GMM). Instead of handling overidentification by reweighting the data to satisfy the moment conditions (as in Generalized Empirical Likelihood methods), this method proceeds by allowing for errors in the variables of the least mean‐square magnitude necessary to simultaneously satisfy all moment conditions. This approach, based on the notions of optimal transport and Wasserstein metric,…

满意化行为、加总与拟线性效用AI 解读

This paper studies approximation error in the quasilinear utility model. Error arises because individuals do not perfectly optimize, and instead satisfice. We investigate the consequences of individual satisficing for modeling aggregate demand, providing an approximate aggregation theorem. We present a simple method for statistical inference on the minimal level of satisficing needed to explain aggregate data. In an illustrative application to scanner panel data, we find…

网络干扰下因果推断的局部方法AI 解读

We propose a new nonparametric modeling framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, disease and financial contagion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other…

迈向轻度爆炸自回归的统一渐近理论AI 解读

Abstract This article provides a general asymptotic theory for mildly explosive autoregression. We confirm that Cauchy limit theory remains invariant across a broad range of error processes, including general linear processes with martingale difference innovations, stationary causal processes, and nonlinear autoregressive time series, such as threshold autoregressive and bilinear models. Our results unify the Cauchy limit theory for long memory, short memory, and…

样本选择下的分布回归与英国工资分解AI 解读

We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model, which accommodates much richer patterns of heterogeneity in the effect of covariates and selection process and allows for departures from Gaussian errors while maintaining the same level of tractability. The model applies to continuous, discrete, and mixed outcomes. We provide identification, estimation, and inference…

动态网络的双重异质性模型AI 解读

Analysis of networks that evolve dynamically requires the joint modelling of individual snapshots and time dynamics. This paper proposes a new flexible two-way heterogeneity model towards this goal. The new model equips each node of the network with two heterogeneity parameters, one to characterize the propensity to form ties with other nodes statically and the other to differentiate the tendency to retain existing ties over time. With n observed networks each having p…

核岭回归的几何分析及其应用AI 解读

We obtain upper bounds for the estimation error of Kernel Ridge Regression (KRR) for all nonnegative regularization parameters, offering a geometric perspective on various phenomena in KRR. As applications: 1. We address the Multiple Descents problem, unifying the proofs of (In Proceedings of Thirty Third Conference on Learning Theory (2020) 2683–2711 PMLR) and (Ann. Statist. 49 (2021) 1029–1054) for polynomial kernels in nonasymptotic regime and we establish Multiple…

灵活应对赢家诅咒的推断方法AI 解读

Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives. For instance, researchers may seek to infer the effectiveness of the most successful treatment or determine which demographic group benefits most from a specific treatment. Similarly, in machine learning, practitioners are often interested in the population performance of the model that performs best empirically. However, cherry-picking the best…