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面板数据模型中的截面依赖：专题社论AI 解读

Journal of Applied EconometricsVolume 31, Issue 1 p. 1-3 Editorial Cross-Sectional Dependence in Panel Data Models: A Special Issue First published: 24 January 2016 https://doi.org/10.1002/jae.2507Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I…

大N面板数据模型中无截面相关的定向检验AI 解读

Summary The paper proposes tests for no cross‐correlation based on the information matrix equality. The tests rely on suitably weighted residual cross‐covariances or cross‐correlations, and are in this sense a generalization of Pesaran's (2004, CESifo working paper 1229) test for no cross‐sectional dependence. They follow chi‐squared distributions under joint N , T asymptotics without restrictive sphericity or distributional assumptions. When using the outcome of the…

横截面依赖下动态面板模型的估计：利用簇依赖提高效率AI 解读

Summary This paper considers the estimation of dynamic panel data models when data are suspected to exhibit cross‐sectional dependence. A new estimator is defined that uses cross‐sectional dependence for efficiency while being robust to the misspecification of the form of the cross‐sectional dependence. We show that using cross‐sectional dependence for estimation is important to obtain an estimator that is more efficient than existing estimators. This new estimator also uses…

强弱截面依赖下时空分析的两阶段方法AI 解读

Summary An understanding of the spatial dimension of economic and social activity requires methods that can separate out the relationship between spatial units that is due to the effect of common factors from that which is purely spatial even in an abstract sense. The same applies to the empirical analysis of networks in general. We use cross‐unit averages to extract common factors (viewed as a source of strong cross‐sectional dependence) and compare the results with the…

未知组别归属下具有分组因子结构的面板数据模型AI 解读

Summary This paper studies panel data models with unobserved group factor structures. The group membership of each unit and the number of groups are left unspecified. We estimate the model by minimizing the sum of least squared errors with a shrinkage penalty. The number of explanatory variables can be large. The regressions coefficients can be homogeneous or group specific. The consistency and asymptotic normality of the estimator are established. We also introduce new C p…

中国化工行业的企业生产率溢出：空间 Hausman–Taylor 方法AI 解读

Summary This paper assesses intra‐sectoral spillovers in total factor productivity (TFP) across Chinese producers in the chemical industry using panel data on 12,552 firms observed over 2004‐2006. We specify TFP as a composite of observable factors such as export market participation, foreign as well as public ownership, the extent of accumulated intangible assets, and unobservable components. We introduce spatial spillovers in TFP through contextual effects of observable…

交易碎片化对英国股票市场质量的影响AI 解读

Summary We investigate the effects of fragmentation in equity markets on the quality of trading outcomes in a panel of FTSE stocks over the period 2008–2011. This period coincided with a great deal of turbulence in the UK equity markets, which had multiple causes that need to be controlled for. To achieve this, we use the common correlated effects estimator for large heterogeneous panels. We extend this estimator to quantile regression to analyse the whole conditional…

金融网络动态与持久性的估计：英镑货币市场应用AI 解读

Summary We propose a novel methodology for dynamic econometric modelling of large financial networks subject to persistence, structural changes and sparsity. We estimate bivariate dynamic Tobit‐type models for each pair of banks, allowing for deterministic or stochastic time‐varying parameters, and then aggregate across all bank pairs. To tackle the high dimensionality of the model, we construct a few lagged variables that efficiently summarize the position of a bank pair in…

视角未知时的沟通AI 解读

Consider a group of individuals with unobservable perspectives (subjective prior beliefs) about a sequence of states. In each period, each individual receives private information about the current state and forms an opinion (a posterior belief). She also chooses a target individual and observes the target's opinion. This choice involves a trade-off between well-informed targets, whose signals are precise, and well-understood targets, whose perspectives are well known.…

随机选择与私人信息AI 解读

We consider an agent who chooses an option after receiving some private information. This information, however, is unobserved by an analyst, so from the latter's perspective, choice is probabilistic or random. We provide a theory in which information can be fully identified from random choice. In addition, the analyst can perform the following inferences even when information is unobservable: (1) directly compute ex ante valuations of menus from random choice and vice versa,…

面板数据中潜在分组结构的识别AI 解读

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM)…

含经济供给模型的差异化产品需求估计：大市场渐近分析AI 解读

IO economists often estimate demand for differentiated products using data sets with a small number of large markets. By modeling demand as depending on a small number of product characteristics, one might hope to obtain increasingly precise estimates of demand parameters as the number of products in a single market grows large. In this paper, I address the question of consistency and asymptotic distributions of IV estimates of demand in a small number of markets as the…

住房与社区需求的动态模型AI 解读

This paper develops a dynamic model of neighborhood choice along with a computationally light multi‐step estimator. The proposed empirical framework captures observed and unobserved preference heterogeneity across households and locations in a flexible way. We estimate the model using a newly assembled data set that matches demographic information from mortgage applications to the universe of housing transactions in the San Francisco Bay Area from 1994 to 2004. The results…

菜单依赖的随机可行性AI 解读

We examine the role of stochastic feasibility in consumer choice using a random conditional choice set rule (RCCSR) and uniquely characterize the model from conditions on stochastic choice data. Feasibility is modeled to permit correlation in availability of alternatives. This provides a natural way to examine substitutability/complementarity. We show that an RCCSR generalizes the random consideration set rule of [Manzini and Mariotti, 2014]. We then relate this model to…

基于估计倾向得分的匹配AI 解读

Propensity score matching estimators (Rosenbaum and Rubin, 1983) are widely used in evaluation research to estimate average treatment effects. In this article, we derive the large sample distribution of propensity score matching estimators. Our derivations take into account that the propensity score is itself estimated in a first step, prior to matching. We prove that first step estimation of the propensity score affects the large sample distribution of propensity score…

组层面处理的工具变量分位数回归：贸易的分布效应AI 解读

We present a methodology for estimating the distributional effects of an endogenous treatment that varies at the group level when there are group-level unobservables, a quantile extension of Hausman and Taylor, 1981. Because of the presence of group-level unobservables, standard quantile regression techniques are inconsistent in our setting even if the treatment is independent of unobservables. In contrast, our estimation technique is consistent as well as computationally…

评论《结构模型估计的约束优化方法》AI 解读

We revisit the comparison of mathematical programming with equilibrium constraints (MPEC) and nested fixed point (NFXP) algorithms for estimating structural dynamic models by Su and Judd (SJ, 2012). Their implementation of the nested fixed point algorithm used successive approximations to solve the inner fixed point problem (NFXP-SA). We re-do their comparison using the more efficient version of NFXP proposed by Rust (1987), which combines successive approximations and…

夜幕掩护之下：环境光照如何影响犯罪活动AI 解读

Abstract We exploit daylight saving time (DST) as an exogenous shock to daylight, using both the discontinuous nature of the policy and the 2007 extension of DST, to consider the impact of light on criminal activity. Regression discontinuity estimates show a 7% decrease in robberies following the shift to DST. As expected, effects are largest during the hours directly affected by the shift in daylight. We discuss our findings within the context of criminal decision making…

达到最优收敛率的 Graphon 估计AI 解读

Network analysis is becoming one of the most active research areas in statistics. Significant advances have been made recently on developing theories, methodologies and algorithms for analyzing networks. However, there has been little fundamental study on optimal estimation. In this paper, we establish optimal rate of convergence for graphon estimation. For the stochastic block model with $k$ clusters, we show that the optimal rate under the mean squared error is $n^{-1}\log k+k^{2}/n^{2}$…

网络计数特征的子抽样 BootstrapAI 解读

Analysis of stochastic models of networks is quite important in light of the huge influx of network data in social, information and bio sciences, but a proper statistical analysis of features of different stochastic models of networks is still underway. We propose bootstrap subsampling methods for finding empirical distribution of count features or “moments” (Bickel, Chen and Levina [Ann. Statist. 39 (2011) 2280–2301]) and smooth functions of these features for the networks.…

面板动态联立方程模型的统计推断AI 解读

We study the identification and estimation of panel dynamic simultaneous equations models. We show that the presence of time-persistent individual-specific effects does not lead to changes in the identification conditions of traditional Cowles Commission dynamic simultaneous equations models. However, the limiting properties of the estimators depend on the way the cross-section dimension, N, or the time series dimension, T, goes to infinity. We propose three limited…

面板空间自相关最大似然推断的高阶修正AI 解读

In a panel data model with fixed effects, possible cross-sectional dependence is investigated in a spatial autoregressive setting. An Edgeworth expansion is developed for the maximum likelihood estimate of the spatial correlation coefficient. The expansion is used to develop more accurate interval estimates for the coefficient, and tests for cross-sectional independence that have better size properties, than corresponding rules of statistical inference based on first order…

长记忆随机波动模型的样本分位数分析AI 解读

This study investigates asymptotic properties of sample quantile estimates in the context of long-memory stochastic volatility models in which the latent volatility component is an exponential transformation of a linear long-memory time series. We focus on the least absolute deviation quantile estimator and show that while the underlying process is a sequence of stationary martingale differences, the estimation errors are asymptotically normal with the convergence rate which…

债务与税收：利用州税变化估计企业杠杆的税收敏感性AI 解读

Using staggered corporate income tax changes across U.S. states, we show that taxes have a first-order effect on capital structure. Firms increase leverage by around 40 basis points for every percentage-point tax increase. Consistent with dynamic tradeoff theory, the effect is asymmetric: leverage does not respond to tax cuts. This is true even within-firm: tax increases that are later reversed nonetheless lead to permanent leverage increases. The treatment effects are…