The Relationship Between Wages and Income and the Timing and Spacing of Births: Evidence from Swedish Longitudinal Data
工资收入与生育时点及间隔:瑞典纵向数据证据AI 解读 ↓
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工资收入与生育时点及间隔:瑞典纵向数据证据AI 解读 ↓
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This paper considers the formulation, estimation, and evaluation of semiparametric multistates duration models with time-varying regressors and unobservables correlated over spells. The authors' models are applied to test a dynamic version of the neoclassical theory of fertility. Using Swedish data, they find that rising female wages over the lifecycle delay times to all conceptions, but barely affect childlessness. The authors estimate a model that predicts fertility attained at different ages as well as the aggregate time series of birth rates. Estimated cohort drift in wage and income parameters is consistent with the expansion of pronatal social programs over the sample period. Copyright 1990 by The Econometric Society.
含固定效应与时变系数的半参数空间自回归面板模型
This article considers a semiparametric spatial autoregressive (SAR) panel data model with fixed effects and time-varying coefficients. The time-varying coefficients are allowed to follow unknown functions of time, while the other parameters are assumed to be unknown constants. We propose a local linear quasi-maximum likelihood estimation method to obtain consistent estimators for the SAR coefficient, the variance of the error term, and the nonparametric time-varying…
稳健预测
We use a decision-theoretic framework to study the problem of forecasting discrete outcomes when the forecaster is unable to discriminate among a set of plausible forecast distributions because of partial identification or concerns about model misspecification or structural breaks. We derive "robust" forecasts which minimize maximum risk or regret over the set of forecast distributions. We show that for a large class of models including semiparametric panel data models for…
含固定效应的函数系数空间自回归面板模型:估计与推断
This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors with a large number of cross-sectional units and a finite time periods. Our proposed methodology removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, however avoids using backfitting technique. We derive the…
事件研究中的时变异质性处理效应
This paper examines the identification and estimation of heterogeneous treatment effects in event studies, emphasizing the importance of both lagged dependent variables and treatment effect heterogeneity. We show that omitting lagged dependent variables can induce omitted variable bias in the estimated time-varying treatment effects. We develop a novel semiparametric approach based on a short-T dynamic linear panel model with correlated random coefficients, where the…
面板数据断点检测的加权回归方法
New procedures for detecting a change in the cross-sectional mean of panel data are proposed. The procedures rely on estimating nuisance parameters using certain cross-sectional means across panels using a weighted least squares regression. In the case of weak cross-sectional dependence between panels, we show how test statistics can be constructed to have a limit null distribution not depending on any choice of bandwidths typically needed to estimate the long-run variances…
通过张量补全推断序贯处理的因果效应
Marginal Structural Models (MSMs) are popular for causal inference of sequential treatments in longitudinal observational studies, which however are sensitive to model misspecification. To achieve flexible modeling, we envision the potential outcomes to form a three-dimensional tensor indexed by subject, time, and treatment regime and propose a tensorized history-restricted MSM (HRMSM). The semi-parametric tensor factor model allows us to leverage the underlying low-rank…