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参考经济周期转折点定时的新方法AI 解读

Abstract–This article proposes a new approach to the analysis of the reference cycle turning points, defined on the basis of the specific turning points of a broad set of coincident economic indicators. Each individual pair of specific peaks and troughs from these indicators is viewed as a realization of a mixture of an unspecified number of separate bivariate Gaussian distributions whose different means are the reference turning points. These dates break the sample into…

含内生回归变量的固定 T 因子增广面板线性估计量AI 解读

A novel method-of-moments approach is proposed for the estimation of factor-augmented panel data models with endogenous regressors when T is fixed. The underlying methodology involves approximating the unobserved common factors using observed factor proxies. The resulting moment conditions are linear in the parameters. The proposed approach addresses several issues which arise with existing nonlinear estimators that are available in fixed T panels, such as local…

异方差分数单整时间序列模型中的自适应推断AI 解读

We consider estimation and inference in fractionally integrated time series models driven by shocks which can display conditional and unconditional heteroscedasticity of unknown form. Although the standard conditional sum-of-squares (CSS) estimator remains consistent and asymptotically normal in such cases, unconditional heteroscedasticity inflates its variance matrix by a scalar quantity, λ>1, thereby inducing a loss in efficiency relative to the unconditionally…

资产收益的贝叶斯分位数时间序列模型AI 解读

We consider jointly modeling a finite collection of quantiles over time. Formal Bayesian inference on quantiles is challenging since we need access to both the quantile function and the likelihood. We propose a flexible Bayesian time-varying transformation model, which allows the likelihood and the quantile function to be directly calculated. We derive conditions for stationarity, discuss suitable priors, and describe a Markov chain Monte Carlo algorithm for inference. We…

具有空间依赖与缺失数据的自回归模型AI 解读

We study herein an autoregressive model with spatially correlated error terms and missing data. A logistic regression model with completely observed covariates is used to model the missingness mechanism. An autoregressive model is used to accommodate time series dependence, and a spatial error model is used to capture spatial dependence. To estimate the model, a weighted least squares estimator is developed for the temporal component, and a weighted maximum likelihood…

平均线性回归函数的半参数有效估计AI 解读

Let Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables. Here X may include (combinations of) continuous, discrete, or non-mutually exclusive “treatments”. Consider the linear regression of Y onto X in a subpopulation homogeneous in W=w (formally a conditional linear predictor). Let b0w be the coefficient vector on X in this regression. We introduce a semiparametrically efficient estimate of the average β0=Eb0W.…

动态面板数据模型的稳健似然估计AI 解读

We develop likelihood-based estimators for autoregressive panel data models that are consistent in the presence of time series heteroskedasticity. Bias-corrected conditional score estimators, random effects maximum likelihood in levels and first differences, and estimators that impose mean stationarity are considered for general autoregressive models with individual effects. We investigate identification under unit roots, and show that random effects estimation in levels may…

错位采用情形下双重差分的设计型分析AI 解读

In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the staggered adoption setting where units, e.g, individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this treatment at all times afterwards. We take a design perspective where we investigate the properties of estimators and procedures given assumptions on the assignment…

利用多来源数据估计与推断半参数模型AI 解读

This paper studies the estimation and inference of nonlinear econometric models when the economic variables are contained in different data sets. We construct a semiparametric minimum distance (SMD) estimator of the unknown structural parameter of interest when there are some common conditioning variables in different data sets. The SMD estimator is shown to be consistent and has an asymptotic normal distribution. We provide the explicit form of the optimal weight for the…

含治愈比例的删失生存分位数回归模型AI 解读

A new quantile regression model for survival data is proposed that permits a positive proportion of subjects to become unsusceptible to recurrence of disease following treatment or based on other observable characteristics. In contrast to prior proposals for quantile regression estimation of censored survival models, we propose a new “data augmentation” approach to estimation. Our approach has computational advantages over earlier approaches proposed by Wu and Yin (2013,…

股票适合长期持有吗？来自广泛发达市场样本的证据AI 解读

We characterize the distribution of long-term equity returns based on the historical record of stock market performance in a broad cross section of 39 developed countries over the period from 1841 to 2019. Our comprehensive sample mitigates concerns over survivor and easy data biases that plague other work in this area. A bootstrap simulation analysis implies substantial uncertainty about long-horizon stock market outcomes, and we estimate a 12% chance that a diversified…

状态空间模型的自助法推断与诊断：动态宏观模型应用AI 解读

Abstract This paper investigates the potentials of the bootstrap as a tool for inference on the parameters of macroeconometric models which admit a state space representation. We consider a bootstrap estimator of the parameters of state space models and show that the bootstrap realizations of this estimator, usually employed to approximate asymptotic confidence intervals, p ‐values, and critical values of tests, can be also constructively used to build a test for forms of…

国家分裂的经济学：评估分离独立的经济影响AI 解读

Abstract This paper presents estimates of the economic effects of secession for a large panel of countries that gained independence between 1940 and 2016. It relies on a semi‐parametric identification strategy that controls for the confounding effects of past gross domestic product (GDP) dynamics, anticipation effects, unobserved heterogeneity, model uncertainty, and effect heterogeneity. Our findings indicate that secession lowered per capita GDP, on average by around 24%…

不确定的王国：国内生产总值及其修订的即时预测AI 解读

Summary We propose a release‐augmented dynamic factor model (RA‐DFM) that allows to quantify the role of a country's data flow in nowcasting both early Gross Domestic Product (GDP) releases and subsequent revisions of official estimates. We use the RA‐DFM to study UK GDP early revision rounds and assemble a comprehensive and novel mixed‐frequency dataset that features 12 years of real‐time data vintages. The RA‐DFM improves over the standard DFM in real time when forecasting…

稳健经验贝叶斯置信区间AI 解读

We construct robust empirical Bayes confidence intervals (EBCIs) in a normal means problem. The intervals are centered at the usual linear empirical Bayes estimator, but use a critical value accounting for shrinkage. Parametric EBCIs that assume a normal distribution for the means (Morris (1983b)) may substantially undercover when this assumption is violated. In contrast, our EBCIs control coverage regardless of the means distribution, while remaining close in length to the…

经济学中的实证策略：照亮从原因到结果的路径AI 解读

The view that empirical strategies in economics should be transparent and credible now goes almost without saying. By revealing for whom particular instrumental variables (IV) estimates are valid, the local average treatment effects (LATE) framework helped make this so. This lecture uses empirical examples, mostly involving effects of charter and exam school attendance, to illustrate the value of the LATE framework for causal inference. LATE distinguishes independence…

空间相关稳健推断AI 解读

We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar “estimator plus and minus a standard error times a critical value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a…

家庭杠杆与经济衰退AI 解读

We evaluate and partially challenge the household leverage view of the Great Recession. In the data, employment and consumption declined more in U.S. states where household debt declined more. We study a model of a monetary union composed of many regions in which liquidity constraints shape the response of employment and consumption to changes in debt. We estimate the model with Bayesian methods combining state and aggregate data. Changes in household credit explain 40% of…

评 Andrews（1991）《异方差与自相关一致协方差矩阵估计》AI 解读

This comment includes a solution to a problem in Section 8 in Andrews (1991) and points out a method to generalize the mean‐squared error (MSE) bounds appearing in Andrews (1988) and Andrews (1991).

巴西失业与失业保险对犯罪的影响AI 解读

We investigate the impact of job loss on crime and the mitigating role of unemployment benefits, exploiting detailed individual‐level data linking employment careers, criminal records, and welfare registries for the universe of male workers in Brazil. The probability of committing crimes increases on average by 23% for workers displaced by mass layoffs, and by slightly less for their cohabiting sons. Using causal forests, we show that the effect is entirely driven by young…

协变量依赖转移概率的马尔可夫状态转换模型最大似然估计AI 解读

This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency of the ML estimator and local asymptotic normality for the models under general conditions, which allow for autoregressive dynamics in the observable process, Markov regime sequences with covariate‐dependent transition matrices, and possible model misspecification. A Monte Carlo study examines the finite‐sample properties of the ML…

局部稳健的半参数估计AI 解读

Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where first steps have no effect, locally, on average moment functions. Using these orthogonal moments reduces model selection and regularization bias, as is important in many applications, especially for machine learning first steps. Also, associated standard errors are robust to misspecification…

现代高斯—马尔可夫定理AI 解读

This paper presents finite‐sample efficiency bounds for the core econometric problem of estimation of linear regression coefficients. We show that the classical Gauss–Markov theorem can be restated omitting the unnatural restriction to linear estimators, without adding any extra conditions. Our results are lower bounds on the variances of unbiased estimators. These lower bounds correspond to the variances of the the least squares estimator and the generalized least squares…

因果与结构效应的自动去偏机器学习AI 解读

Many causal and structural effects depend on regressions. Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediation, and parameters of economic structural models. The regressions may be high‐dimensional, making machine learning useful. Plugging machine learners into identifying equations can lead to poor inference due to bias from regularization and/or model selection. This paper gives automatic debiasing for…