论文检索

由后验揭示先验：英国通胀预测的应用AI 解读

Summary A Bayesian typically uses data and a prior to produce a posterior. We shall follow the opposite route, using data and the posterior information to reveal the prior. We then apply this theory to inflation forecasts by the Bank of England and the National Institute of Economic and Social Research in an attempt to get some insight into the prior beliefs of the policy makers in these two institutions, especially under the uncertainties about the Brexit referendum, the…

参数位于参数空间边界时的惩罚准似然估计与模型选择AI 解读

Summary We consider here penalized likelihood-based estimation and model selection applied to econometric time series models, which allow for nonnegativity (boundary) constraints on some or all of the parameters. We establish that joint model selection and estimation result in standard asymptotic Gaussian distributed estimators. The results contrast with nonpenalized estimation, which, as is well-known, leads to nonstandard asymptotic distributions that depend on the unknown…

有偏技术进步下替代弹性的识别：结构面板 GMM 估计AI 解读

Summary: This paper provides a structural panel GMM (P-GMM) estimator of the elasticity of substitution between capital and labour that does not depend on external instruments, and which can be applied in the presence of biased technical change. We identify the conditions under which P-GMM is a consistent estimator and compare it to a fixed effects estimator. Using a Monte Carlo study, we find that the P-GMM estimator is nearly unbiased provided the number of time periods…

混合因子结构下的大型协方差矩阵估计AI 解读

Summary We extend the principal orthogonal complement thresholding (POET) framework by J. Fan, Y. Liao, and M. Mincheva (2013) to estimate large covariance matrices with a ‘mixed’ structure of observable and unobservable strong/weak factors, and we call this method the extended POET (ePOET). Especially, the weak factor structure allows the existence of slowly divergent eigenvalues of the covariance matrix that are frequently observed in real data. Under some mild conditions,…

随机右删失下的工具变量分位数回归AI 解读

Summary This paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It is assumed that the structural quantile of the logarithm of the outcome variable is linear in the covariates and censoring is independent. The regressors and instruments can be either continuous or discrete. The specification generates a continuum of equations of which the quantile…

含干扰泛函的复杂调查数据增广两步估计方程AI 解读

Summary Statistical inference in the presence of nuisance functionals with complex survey data is an important topic in social and economic studies. The Gini index, Lorenz curves, and quantile shares are among the commonly encountered examples. The nuisance functionals are usually handled by a plug-in nonparametric estimator and the main inferential procedure can be carried out through a two-step generalized empirical likelihood method. Unfortunately, the resulting inference…

面板数据非线性双重差分的简便方法AI 解读

Summary I derive simple, flexible strategies for difference-in-differences settings where the nature of the response variable may warrant a nonlinear model. I allow for general staggered interventions, with and without covariates. Under an index version of parallel trends, I show that average treatment effects on the treated (ATTs) are identified for each cohort and calendar time period in which a cohort was subjected to the intervention. The pooled quasi-maximum likelihood…

LATE为时不晚：允许或不允许反向服从者的局部平均处理效应新解AI 解读

Summary In heterogeneous treatment effect models with endogeneity, identification of the local average treatment effect (LATE) typically relies on the availability of an exogenous instrument monotonically related to treatment participation. First, we demonstrate that a strictly weaker local monotonicity condition—invoked for specific potential outcome values rather than globally—identifies the LATEs on compliers and defiers. Second, we show that our identification results…

具有乘性不可观测效应的三维引力模型AI 解读

Summary This paper investigates three-way gravity models with multiplicative unobserved effects, which are popular in bilateral trade analysis and many other contexts. Such models are usually estimated by the fixed effects Poisson pseudo maximum likelihood method. As an alternative, we extend the estimation strategy proposed by Jochmans (2017) to our new settings by constructing moment conditions that are independent of the unobserved effects. Our method entails estimation…

工具变量分位数回归的一阶段表示AI 解读

Summary This paper develops a first-stage linear regression representation for an instrumental variables (IV) quantile regression (QR) model. The quantile first stage is analogous to the least-squares case, i.e., a linear projection of the endogenous variables on the instruments and other exogenous covariates, with the difference that the QR case is a weighted projection. The weights are given by the conditional density function of the innovation term in the QR structural…

变系数非参数变换模型的模型选择AI 解读

Summary Based on the smoothed partial rank (SPR) loss function, we propose a group LASSO penalized SPR estimator for the varying coefficient nonparametric transformation models, and derive its estimation and model selection consistencies. It not only selects important variables, but is also able to select between varying and constant coefficients. To deal with the computational challenges in the rank loss function, we develop a group forward and backward stagewise algorithm…

GARCH 时间序列中参数变化区间的检验AI 解读

Summary We develop a uniform test for detecting and dating the integrated or mildly explosive behaviour of a strictly stationary generalized autoregressive conditional heteroskedasticity (GARCH) process. Namely, we test the null hypothesis of a globally stable GARCH process with constant parameters against the alternative that there is an ‘abnormal’ period with changed parameter values. During this period, the parameter-value change may lead to an integrated or mildly…

异质处理效应下的双向固定效应与双重差分：综述AI 解读

Summary Linear regressions with period and group fixed effects are widely used to estimate policie’s effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions. It has recently been shown that those regressions may produce misleading estimates if the policy’s effect is heterogeneous between groups or over time, as is often the case. This survey reviews a fast-growing literature that documents this issue and…

2022年 Denis Sargan 计量经济学奖AI 解读

The editors of The Econometrics Journal decided to award the 2022 Denis Sargan Econometrics Prize to Chiara Casoli (Fondazione Eni Enrico Mattei, Milan) for her article “Permanent-Transitory decomposition of cointegrated time series via dynamic factor models, with an application to commodity prices” with Riccardo (Jack) Lucchetti in the May 2022 issue of The Econometrics Journal.1 Chiara and Jack’s prize winning article develops a cointegration-based Permanent-Transitory…

通过稀疏精度矩阵估计高维向量自回归AI 解读

Summary We consider the problem of estimating sparse vector autoregression (VAR) via penalized precision matrices. This matrix is the output of the underlying directed acyclic graph of the VAR process, whose zero components correspond to the zero coefficients of the graphical representation of the VAR. The sparsity-based precision matrix estimator is deduced from the D-trace loss with convex and nonconvex penalty functions. We establish the consistency of the penalized…

离散博弈中合作行为的非参数检验AI 解读

Summary We propose a nonparametric test for cooperative behaviour among players in discrete, static games. Assuming that certain exchangeability conditions hold if we match observable characteristics across all players, we obtain testable implications for cooperative (coalitional) behaviour, which we define as occurring when players maximise an unknown joint objective function that is symmetric conditional on the matching. Cooperation implies the existence of a class of…

差异化产品总量需求模型中随机系数的非参数识别AI 解读

Summary This paper studies nonparametric identification in market-level demand models for differentiated products with heterogeneous consumers. We consider a general class of models that allows for the individual-specific coefficients to vary continuously across the population and give conditions under which the density of these coefficients, and hence also functionals such as the fractions of individuals who benefit from a counterfactual intervention, is identified.

含高维协变量的断点回归设计推断AI 解读

Summary We study regression discontinuity designs in which many predetermined covariates, possibly much more than the number of observations, can be used to increase the precision of treatment effect estimates. We consider a two-step estimator which first selects a small number of ‘important’ covariates through a localised lasso-type procedure, and then, in a second step, estimates the treatment effect by including the selected covariates linearly into the usual local linear…

Summary We propose new ways to compare two latent distributions when only ordinal data are available, and without imposing parametric assumptions on the underlying continuous distributions. First, we contribute identification results. We show how certain ordinal conditions provide evidence of between-group inequality, quantified by particular quantiles being higher in one latent distribution than in the other. We also show how other ordinal conditions provide evidence of…

无排除工具变量时的可行工具变量回归AI 解读

Summary The relevance condition of integrated conditional moment (ICM) estimators is significantly weaker than the conventional instrumental variable's in at least two respects: (1) consistent estimation without excluded instruments is possible, provided endogenous covariates are nonlinearly mean-dependent on exogenous covariates, and (2) endogenous covariates may be uncorrelated with but mean-dependent on instruments. These remarkable properties notwithstanding,…

半参数因子模型的可行加权投影主成分分析AI 解读

Summary Various factor estimation procedures have been developed, based on the latent factor model. They often consider general conditions that allow for correlations and heteroscedasticity. However, the conventional principal components method does not efficiently estimate the parameters. It also does not accommodate additional covariates, which explain the unknown factors, even if they are available. In particular, a few aggregated macroeconomic variables can be used as…

密度比模型下两个半连续总体基尼指数的半参数推断AI 解读

Summary The Gini index is a popular inequality measure with many applications in social and economic studies. This paper studies semi-parametric inference on the Gini indices of two semi-continuous populations. We characterise the distribution of each semi-continuous population by a mixture of a discrete point mass at zero and a continuous skewed positive component. A semi-parametric density ratio model is then employed to link the positive components of the two…

含交互效应的空间动态面板IV估计：大样本理论与银行风险态度应用AI 解读

Summary This paper develops a new instrumental variables estimator for spatial, dynamic panels with interactive effects under large N and T asymptotics. For this class of models, most approaches available in the literature are based on quasi-maximum likelihood estimation. The approach put forward here is appealing from both a theoretical and a practical point of view for a number of reasons. First, it is linear in the parameters of interest and computationally inexpensive.…

条件分位数中的样本选择检验AI 解读

Summary This paper provides distribution free tests for detecting sample selection in conditional quantile functions. The first test is an omitted predictor test with the propensity score as the omitted variable. In the case of rejection we cannot distinguish between rejection due to genuine selection or to misspecification. Thus, we suggest a second test using only individuals with propensity score close to one. The latter relies on an ‘identification at infinity’ argument,…