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ℓ2 松弛：预测组合与投资组合分析的应用AI 解读

Abstract We propose ℓ2-relaxation, which is a novel convex optimization problem, to tackle a forecast combination with many forecasts or a minimum variance portfolio with many assets. ℓ2-relaxation minimizes the squared Euclidean norm of the weight vector subject to a set of relaxed linear inequalities to balance the bias and variance. It delivers optimality with approximately equal within-group weights when latent block equicorrelation patterns dominate the high-dimensional…

This Special Issue celebrates the seminal contributions of Philip G. Wright to causal inference in economics and puts them in a modern perspective. At its core is a reproduction of Online Appendix B in Wright’s 1928 book The Tariff on Animal and Vegetable Oils.1 This book deals with demand for and supply of oils and butter. In its Online Appendix B, Wright made several fundamental contributions to causal inference. He introduced a structural equation model of demand and…

关税对价格与产出的影响：以黄油和亚麻籽为重点AI 解读

Editorial statement: This is a reproduction of Appendix B in Wright, P. G. (1928). The Tariff on Animal and Vegetable Oils, 286–319. New York: The Macmillan Company. We have corrected four minor errors in its mathematical expressions: We have made further typographical changes to improve readability: We (i) have placed all footnotes, some of which are long and contain graphics, in a new section at the end; (ii) have reconstructed all graphics using TikZ, with some added…

拓展工具变量方法的适用范围AI 解读

Summary Since Philip Wright’s proposal of the linear instrumental variables (IV) estimator in 1928, the scope of application of IV models has greatly expanded. There are now nonlinear IV models in use with both additive and nonadditive latent variables, employing both semiparametric and nonparametric specifications. A notable feature of Wright’s linear IV estimator is that it is applicable to incomplete models that leave the genesis of endogenous explanatory variables…

公式型工具变量的设计型识别：综述AI 解读

Summary Many studies in economics use instruments or treatments that combine a set of exogenous shocks with other predetermined variables via a known formula. Examples include shift-share instruments and measures of social or spatial spillovers. We review recent econometric tools for this setting, which leverage the assignment process of the exogenous shocks and the structure of the formula for identification. We compare this design-based approach with conventional…

计量经济学中的因果推断与数据融合AI 解读

Summary Learning about cause and effect is arguably the main goal in applied econometrics. In practice, the validity of these causal inferences is contingent on a number of critical assumptions regarding the type of data that has been collected, and the substantive knowledge that is available about the phenomenon under investigation. For instance, unobserved confounding factors threaten the internal validity of estimates; data availability is often limited to nonrandom,…

动态离散博弈中高效且收敛的序贯伪似然估计AI 解读

Abstract We propose a new sequential Efficient Pseudo-Likelihood (k-EPL) estimator for dynamic discrete choice games of incomplete information. k-EPL considers the joint behaviour of multiple players simultaneously, as opposed to individual responses to other agents’ equilibrium play. This, in addition to reframing the problem from conditional choice probability (CCP) space to value function space, yields a computationally tractable, stable, and efficient estimator. We show…

网络干扰下的政策定向配置AI 解读

Abstract This article studies the problem of optimally allocating treatments in the presence of spillover effects, using information from a (quasi-)experiment. I introduce a method that maximizes the sample analogue of average social welfare when spillovers occur. I construct semi-parametric welfare estimators with known and unknown propensity scores and cast the optimization problem into a mixed-integer linear program, which can be solved using off-the-shelf algorithms. I…

通胀知识、通胀预期与央行信任：一项调查实验AI 解读

Abstract This paper explores the causal effects of inflation literacy using a two-step randomized controlled trial. The first step shows that qualitative information about inflation and monetary policy (Literacy treatment) enhances knowledge about these issues and raises both the likelihood of providing inflation predictions and trust in the central bank. The second step tests the joint impact of the Literacy treatment and further quantitative treatments. While the level of…

重印：有限欠识别AI 解读

I adapt the Generalised Method of Moments to deal with nonlinear models in which a finite number of isolated parameter values satisfy the moment conditions. I also study the closely related class of first-order underidentified models, whose expected Jacobian is rank deficient but not necessarily zero. In both cases, my proposed procedures exploit the underidentification structure to yield parameter estimators and underidentification tests within a standard asymptotically…

通过杠杆效应识别波动率风险价格AI 解读

In asset pricing models with stochastic volatility, uncertainty about volatility affects risk premia through two channels: aversion to decreasing returns and aversion to increasing volatility. We analyze the identification of and robust inference for structural parameters measuring investors’ aversions to these risks: the return risk price and the volatility risk price. In the presence of a leverage effect (instantaneous causality between the asset return and its…

函数型生态推断AI 解读

In this paper, we consider the problem of ecological inference when one observes the conditional distributions of Y|W and Z|W from aggregate data and attempts to infer the conditional distribution of Y|Z without observing Y and Z in the same sample. First, we show that this problem can be transformed into a linear equation involving operators for which, under suitable regularity assumptions, least squares solutions are available. We then propose the use of the least squares…

多因子资产定价模型中识别稳健的同时推断AI 解读

This paper proposes exact identification-robust confidence sets for the zero-beta rate and ex-post factor prices in asset pricing models. Exploiting the information from the cross-sectional intercept allows us to impose or formally test model-consistent restrictions, including those resulting from traded factors in excess of the zero beta-rate or from return spreads. Analytical projection-based solutions for confidence set outcomes are developed. The proposed procedures are…

平稳向量自回归模型中的长期风险AI 解读

This paper introduces a local-to-unity/small sigma model for stationary processes with long-range persistence and non-negligible long-run prediction and estimation risks. The model represents a process containing unobserved short and long-run components measured on different time scales. The short-run component is defined in calendar time, while the long-run component evolves in rescaled time with ultra-long units. We develop estimation and long-run prediction methods for…

条件谱方法AI 解读

We model predictive scale-specific cycles. By employing suitable matrix representations, we express the forecast errors of covariance-stationary multivariate time series in terms of conditionally orthonormal scale-specific bases. The representations yield conditionally orthogonal decompositions of these forecast errors. They also provide decompositions of their variances and betas in terms of scale-specific variances and betas capturing predictive variability and…

离散选择模型中的弱识别AI 解读

We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that when the null hypothesis of weak identification is rejected, Wald-based inference can be carried…

IV 回归中的外生性检验与弱识别：渐近理论及点估计AI 解读

This paper provides new insights on exogeneity tests in linear IV models and their use for estimation, when identification fails or may not be strong. We make two main contributions. First, we show that Durbin–Wu–Hausman (DWH) and Revankar–Hartley (RH) exogeneity tests have correct level asymptotically, even when the first-stage coefficient matrix (which controls identification) is rank-deficient. We provide necessary and sufficient conditions under which these tests are…

利用大量辅助统计量进行模拟估计与长期动态分析AI 解读

The existing asymptotic theory for estimators obtained by simulated minimum distance does not cover situations in which the number of components of the auxiliary statistics (or number of matched “moments”) is large — typically larger than the sample size. We establish the consistency of the simulated minimum distance estimator in this situation and derive its asymptotic distribution. Our estimator is easy to implement and allows us to exploit all the informational content of…

链式双重差分AI 解读

This paper studies the identification, estimation, and inference of long-term (binary) treatment effect parameters when balanced panel data is not available, or consists of only a subset of the available data. We develop a new estimator: the chained difference-in-differences, which leverages the overlapping structure of many unbalanced panel data sets. This approach consists in aggregating a collection of short-term treatment effects estimated on multiple incomplete panels.…

潜在因子与可观测因子的空间张成检验AI 解读

Factor analysis is a widely used tool to summarize high dimensional panel data via a small dimensional set of latent factors. Many applications in finance and macroeconomics, are often focused on observable factors with an economic interpretation. The objective of this paper is to provide a test to answer a question which naturally comes up in discussions regarding latent versus observable factors: do latent and observable factors span the same space? We derive asymptotic…

通过相关性的多重检验正则化股票收益协方差矩阵AI 解读

This paper develops a large-scale inference approach for the regularization of stock return covariance matrices. The framework allows for the presence of heavy tails and multivariate GARCH-type effects of unknown form among the stock returns. The approach involves simultaneous testing of all pairwise correlations, followed by setting non-statistically significant elements to zero. This adaptive thresholding is achieved through sign-based Monte Carlo resampling within…

期限结构模型中风险溢价的识别稳健推断AI 解读

We propose identification robust statistics for testing hypotheses on the risk premia in dynamic affine term structure models. We do so using the moment equation specification proposed in Adrian et al. (2013). Statistical inference based on their three-stage estimator requires knowledge of the risk factors’ quality and can be misleading when the β’s are weak, which results when sampling errors are of comparable order of magnitude as the risk factor loadings. We extend the…

混合识别强度矩条件模型的效率界AI 解读

Moment condition models with mixed identification strength are models that are point identified but with estimating moment functions that are allowed to drift to 0 uniformly over the parameter space. Even though identification fails in the limit, depending on how slow the moment functions vanish, consistent estimation is possible. Existing estimators such as the generalized method of moment (GMM) estimator exhibit a pattern of nonstandard or even heterogeneous rate of…

针对正态混合备择的得分型检验AI 解读

Testing normality against discrete normal mixtures is complex because some parameters turn increasingly underidentified along alternative ways of approaching the null, others are inequality constrained, and several higher-order derivatives become identically 0. These problems make the maximum of the alternative model log-likelihood function numerically unreliable. We propose score-type tests asymptotically equivalent to the likelihood ratio as the largest of two simple…