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潜在序贯可忽略性假设与处理不依从下的数字沟通策略因果效应推断AI 解读

Organizations are increasingly relying on digital communications, such as targeted e-mails and mobile notifications, to engage with their audiences. Despite the evident advantages like cost-effectiveness and customization, assessing the effectiveness of such communications from observational data poses various statistical challenges. An immediate challenge is to adjust for targeting rules used in these communications. When digital communications involve a sequence of e-mails…

监督式动态主成分分析：多预测变量下的线性动态预测AI 解读

This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an effective way to bridge the gap between predictors and the target variable of interest by scaling and combining the predictors and their lagged values, resulting in an effective dynamic forecasting. Unlike the traditional diffusion-index approach, which does not learn the…

利用参数扩展 SEM 方法估计潜变量面板数据模型AI 解读

This article presents new estimation algorithms for three types of dynamic panel data models with latent variables: factor models, discrete choice models, and persistent-transitory quantile processes. The new methods combine the parameter expansion (PX) ideas of Liu, Rubin, and Wu with the stochastic expectation-maximization (SEM) algorithm in likelihood and moment-based contexts. The goal is to facilitate convergence in models with a large space of latent variables by…

具有异质性的非参数时变面板模型AI 解读

Since Bai (2009, Econometrica 77, 1229–1279), considerable extensions have been made to panel data models with interactive fixed effects (IFEs). However, little work has been conducted to understand the associated iterative algorithm, which, to the best of our knowledge, is the most commonly adopted approach in this line of research. In this paper, we refine the algorithm of panel data models with IFEs using the nuclear-norm penalization method and duple least-squares (DLS)…

具有一般因子与解释变量的交互效应面板模型AI 解读

This paper considers a model with general regressors and unobservable common factors. An estimator based on iterated principal component analysis is proposed, which is shown to be not only asymptotically normal, but under certain conditions also free of the otherwise so common asymptotic incidental parameters bias. Interestingly, the conditions required to achieve unbiasedness become weaker the stronger the trends in the factors, and if the trending is strong enough,…

利用替代性高频数据追踪经济活动AI 解读

ABSTRACT Monthly macroeconomic series captured the sharp fluctuations during the COVID‐19 pandemic only with a lag. The use of alternative high‐frequency data is promising for crisis periods, but it is difficult to extract relevant business cycle information from them. We present a Bayesian mixed‐frequency dynamic factor model with stochastic volatility for measuring GDP growth at high‐frequency intervals. Its novelty is an additional state‐space block, in which the sparse…

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…

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…

重新评估稀疏网格积分的似然近似AI 解读

ABSTRACT This paper revisits sparse grid integration proposed in the literature for approximating integrals that occur as choice probabilities in random coefficient discrete choice models. First, we successfully replicate their main findings for the panel mixed logit. Second, for higher variances and for a different structure of the variances of the random coefficients, in certain cases, we fail to replicate the original results. Third, for the important special case of…

菲利普斯曲线的断点：来自面板数据的证据AI 解读

ABSTRACT We revisit the Phillips curve, applying new Bayesian panel methods with structural breaks to US and EU disaggregate data. Our approach lets us estimate both the number and timing of breaks and to determine the existence of clusters of industries, cities, or countries whose Phillips curves display similar patterns. We find evidence of a flattening for US sectoral data and among EU countries, particularly poorer ones. Evidence of flattening is weaker for MSA‐level…

格点上高维随机场的一般时空因子模型AI 解读

Motivated by the need for analysing large spatio-temporal panel data, we introduce a novel nonparametric methodology for n-dimensional random fields observed across S spatial locations and T time periods. We call it general spatio-temporal factor model (GSTFM). First, we provide the probabilistic and mathematical underpinning needed for the representation of a random field as the sum of two components: the common component (driven by a small number q of latent factors) and…

时变系数面板模型的设定检验AI 解读

This paper provides nonparametric specification tests for the commonly used homogeneous and stable coefficients structures in panel data models. We first obtain the augmented residuals by estimating the model under the null hypothesis and then run auxiliary time series regressions of augmented residuals on covariates with time-varying coefficients (TVCs) via sieve methods. The test statistic is then constructed by averaging the squared fitted values, which are close to zero…

事件研究中的有限类型异质性处理效应AI 解读

Abstract A key assumption of the differences-in-differences designs is that the average evolution of untreated potential outcomes is the same across different treatment cohorts: parallel trend assumption. In this paper, we relax the parallel trend assumption by assuming a latent type variable and developing a type-specific parallel trend. With a finite support assumption on the latent type and long pretreatment time periods, an extremum classifier consistently estimates the…

使用广义Bierens最大统计量推断条件矩约束识别的参数AI 解读

Abstract Many economic panel and dynamic models, such as rational behavior and Euler equations, imply that the parameters of interest are identified by conditional moment restrictions. We introduce a novel inference method without any prior information about which conditioning instruments are weak or irrelevant. Building on Bierens (1990), we propose penalized maximum statistics and combine bootstrap inference with model selection. Our method optimizes asymptotic power by…

信息性观测时点下的函数主成分分析AI 解读

Summary Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serve as important building blocks for forecasting and model building. Decades of research have advanced methods for functional principal component analysis, often assuming independence between the observation times and longitudinal outcomes. Yet such assumptions are fragile in real-world settings where observation times may be driven…

纵向研究中序贯未测混杂下的敏感性模型与界限AI 解读

Abstract We consider sensitivity analysis for causal inference in a longitudinal study with time-varying treatments and covariates. It is of interest to assess the worst-case possible values of counterfactual outcome means and average treatment effects under sequential unmeasured confounding. We formulate several multi-period sensitivity models to relax the corresponding versions of the assumption of sequential non-confounding. The primary sensitivity model involves only…

战争的知识战利品？国防研发、生产率与国际溢出AI 解读

Abstract We examine the impact of government funding for R&D—and defense-related R&D in particular—on privately conducted R&D and its ultimate effect on productivity growth. We estimate longitudinal models that relate privately funded R&D to lagged government-funded R&D using industry-country level data from OECD countries and firm level data from France. To deal with the potentially endogenous allocation of government R&D funds, we use changes in predicted defense R&D as an…

具有趋势性时变系数的空间面板数据模型AI 解读

This article investigates the estimation and inference of spatial panel data models in which the regression coefficient vector is a trending function. We use time differences to eliminate the individual effects and employ various GMM estimations for regression coefficients with both linear and quadratic moments. Time trend estimator based on these GMM estimations is also proposed. Monte Carlo experiments show that the finite sample performance of the estimators is…

含内生主导单元的非线性空间动态面板模型：份额数据应用AI 解读

This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed…

结构不稳定下的面板数据协整检验AI 解读

Spurious regression analysis in panel data when the time series are cross-section dependent is analyzed in the article.The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components.We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran's common correlated effects approach.This…

降秩时空模型AI 解读

To simultaneously model the cross-sectional dependency and dynamic time dependency among n units, most research in spatial econometrics parameterizes the coefficient matrices among the n units as functions of known weights matrices. This modeling framework is over-simplified and faces the risk of misspecification when constructing the weights matrices. In this article, we propose a novel reduced-rank spatio-temporal model by assuming the coefficient matrices follow a…

异质误差方差下线性面板模型的分组异质性AI 解读

We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introduce an objective function that avoids a singularity that arises in a pseudolikelihood approach. We provide theoretical and numerical evidence showing when allowing for variance groups improves classification. The developed procedure provides new evidence on the relation between…

允许网络随时间变化的面板空间与社会网络依赖检验AI 解读

The paper introduces robust generalized Moran I tests for network-generated cross-sectional dependence in a panel data setting where unit-specific effects can be random or fixed. Network dependence may originate from endogenous variables, exogenous variables, and/or disturbances, and the network dependence is allowed to vary over time. The formulation of the test statistics also aims at accommodating situations where the researcher is unsure about the exact nature of the…