每日简报

多成分高斯混合模型下 EM 算法的收敛与最优性AI 解读

Summary Gaussian mixture models are fundamental statistical tools for modelling heterogeneous data. Due to the nonconcavity of the likelihood function, the expectation-maximization (EM) algorithm is widely used for parameter estimation of each Gaussian component. Existing analyses of the EM algorithm’s convergence to the true parameter focus on either the two-component case or multi-component settings with known mixing probabilities and isotropic covariance matrices. In this…

利用预测识别新闻冲击AI 解读

ABSTRACT We propose a method to identify the anticipated components of macroeconomic shocks in a structural VAR. We include empirical forecasts about each time series in the VAR. This introduces enough linear restrictions to identify every structural shock and to further decompose each one into “news” and “surprise” shocks. We estimate a VAR on U.S. time series using forecast data from the SPF, CBO, Federal Reserve, and asset prices. Unanticipated fiscal stimulus and…

任意带宽选择下的预测模型权重形状优化AI 解读

Parameter instability has been widely documented to lead to poor predictive accuracy for economic forecasting models. Adaptive schemes for data weighting, such as rolling-window estimation, only involve a single tuning parameter (akin to a bandwidth) and have been widely used to address this issue. The importance of the shape of the weighting scheme (kernel) has received far less attention in the forecasting literature. In this paper we show that in practice there can be a…

时变内生随机系数面板模型的识别与估计AI 解读

This paper proposes a correlated random coefficient linear panel data model, where regressors can be correlated with time-varying and individual-specific random coefficients through both a fixed effect and a time-varying random shock. I develop a new panel data-based method to identify the average partial effect and the local average response function. The identification strategy employs a sufficient statistic to control for the fixed effect and a control variable for the…

经济与金融中的贝叶斯方法：统一综述与分类AI 解读

This special issue brings together 21 papers that reflect the expanding scope and methodological evolution of Bayesian econometrics. We introduce a new organizing taxonomy that distinguishes between Traditional Bayesian Approaches - rooted in conjugate priors, standard likelihood-based estimation, and established Markov Chain Monte Carlo (MCMC) techniques - and Contemporary Bayesian Frontiers, characterized by nonparametric methods, variational inference, high-dimensional…

双侧测量误差下的分布效应：代际收入流动应用AI 解读

This paper considers identification and estimation of distributional effect parameters that depend on the joint distribution of an outcome and another variable of interest (“treatment”) in a setting with “two-sided” measurement error—that is, where both variables are possibly measured with error. Examples of these parameters in the context of intergenerational income mobility include transition matrices, rank-rank correlations, and the poverty rate of children as a function…

多解释变量异方差回归的 AICAI 解读

The original and corrected Akaike information criteria (AIC) have been routinely used for model selection for ages. The penalty terms in these criteria are tied to the classical normal linear regression, characterized by conditional homoskedasticity and a small number of regressors relative to the sample size. We derive, from the same principles, a general version that takes account of conditional heteroskedasticity and regressor numerosity. The new AICm penalty takes a form…

基于个体特征的分位数因子模型估计AI 解读

This paper studies the estimation of characteristics-based quantile factor models where the factor loadings are unknown functions of observed individual characteristics, and the idiosyncratic error terms are subject to conditional quantile restrictions. We propose a three-stage estimation procedure that is easy to implement and has nice properties. The convergence rates, the limiting distributions of the estimated factors and loading functions, plus a consistent selection…

边界断点设计的估计与推断：基于距离的方法AI 解读

We study nonparametric distance-based (isotropic) local polynomial methods for estimating the boundary average treatment effect curve, a causal functional that captures treatment effect heterogeneity in boundary discontinuity designs. We establish identification, estimation, and inference results both pointwise and uniformly along the treatment assignment boundary. We show that the geometric regularity of the boundary, a one-dimensional manifold, plays a central role in…

未知形式异方差下非基本 ARMA 模型的时域估计AI 解读

This paper considers time domain estimation of possibly non-fundamental (that is, non-causal and/or non-invertible) non-Gaussian linear ARMA models with martingale difference innovations that may display conditional heteroskedasticity of unknown form. Instead of explicitly parametrizing the underlying volatility process (the higher order dependence) and employing maximum likelihood procedures, we propose a time domain minimum distance objective function based on innovations…

多维匹配的半非参数模型：最优传输方法AI 解读

This paper develops a set of empirically tractable and flexible sieve estimators for semi-nonparametric multidimensional matching models with transferable utility, focusing on worker-job matching. We generalize the parametric quadratic-Gaussian framework employed by Bojilov and Galichon (2016) and Lindenlaub (2017), which relies on joint normality of observed characteristics. We allow unrestricted distributions of characteristics and show identification of the production…

非参数二元选择模型的核化估计方法AI 解读

We propose a new estimator for nonparametric binary choice models that does not impose a parametric structure on either the systematic function of covariates or the distribution of the error term. A key advantage of our approach is its computational scalability in the number of covariates. For instance, even when assuming a normal error distribution as in probit models, commonly used sieves for approximating an unknown function of covariates can lead to a large-dimensional…

通过有限跨观测信息汇集界定处理效应AI 解读

We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. Namely, the bounds are constructed as…

奇异向量自回归AI 解读

This paper develops methods for the empirical analysis of singular processes, particularly in macroeconomics. A strong rationale, a well-developed theoretical framework and, as we show, empirical support exist for multivariate time series with a singular spectral density. A singular spectral density is consistent with economic theory underlying, for example, DSGE models in which the number of variables is greater than the number of structural shocks. This assumption…

工具变量回归中弱工具稳健的子向量推断：拉格朗日乘子检验与 Anderson–Rubin 置信集AI 解读

We propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condition. This is the first weak-instrument-robust subvector test for instrumental variables regression to recover the degrees of freedom of the commonly used non-weak-instrument-robust Wald test. Additionally, we provide a closed-form solution for subvector confidence sets obtained by inverting the…

采用非共轭先验的贝叶斯模型平均AI 解读

This paper considers the case of non-conjugate prior distributions for Bayesian model averaging (BMA). Although the natural conjugate setting is the default choice for BMA, mainly for reasons of analytical tractability, it has come under considerable criticism due to its unrealistic assumptions about prior information, among others. In this study, we extend the literature by considering two special cases of the multivariate Student-t distribution. We obtain closed-form…

具有定向作用的工具变量与处理效应AI 解读

Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. Our discussion revolves around the concept of targeting: which instruments target which treatments. It allows us to establish conditions under which counterfactual averages and treatment effects are point- or partially-identified for composite complier groups. We explore the additional identifying power of a positive…

集中化 MCMC 估计AI 解读

This paper studies a concentrated Markov Chain Monte Carlo (MCMC) estimation method that offers significant computational advantages while delivering valid inference for parameters of interest. By reducing the dimension of parameters to be sampled, this method substantially decreases computational time and memory usage relative to both traditional and some state-of-the-art Bayesian algorithms. Its applicability extends to correctly specified or misspecified likelihood…

非对称风险下的宏观审慎政策：贝叶斯结构分位数 VAR 方法AI 解读

Macro-prudential policymakers face a risk-management problem: they must assess medium-term downside risks to the real economy stemming from financial imbalances and the associated threat of future financial crises, while accounting for the cost of policy interventions in terms of foregone upside potential. This trade-off between limiting downside risks and preserving upside growth is inherently asymmetric and depends on the distribution of future macroeconomic outcomes. We…

基于方差分解检验高频网络连通性的差异AI 解读

We develop a Bayesian Wald-type test for detecting temporal differences in connectedness from variance decompositions, and establish the asymptotic normality of impulse response functions and variance decomposition matrices at fixed local time points. The resulting test statistic is asymptotically χ 2 distributed under the null hypothesis and performs well in Monte Carlo simulations. Applying our methodology to high-frequency options data on sectoral exchange-traded funds…

离散数据联立方程模型的似然设定AI 解读

In this article, we examine the foundations of a rich and diverse literature in economics and derive the likelihood function of simultaneous equation models for discrete data as the invariant distribution of a suitably specified Markov process. This formulation offers a well-defined reduced form of the model and dispenses with the need for controversial recursivity requirements and ad hoc indeterminacy rules. The derivation resolves puzzling paradoxes highlighted in earlier…

局部稳健且半参数有效的贝叶斯推断AI 解读

We propose a framework for making Bayesian parametric models robust to local misspecification. Suppose in a baseline parametric model, a parameter of interest has an interpretation in an encompassing semiparametric model. Bayesian and maximum likelihood estimators are generally biased under local misspecification. We propose to augment the baseline likelihood by a multiplicative factor that involves scores for the baseline model, the efficient scores for the encompassing…

空间处理的因果推断AI 解读

Many events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units. I approach the spatial treatment setting from an experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between units near realized treatment locations and units near counterfactual (unrealized) candidate locations, which differs from…

局部投影的双重稳健性与 VAR 推断的隐患AI 解读

We consider impulse response inference in a locally misspecified vector autoregression (VAR) model. The conventional local projection (LP) confidence interval has correct coverage even when the misspecification is so large that it can be detected with probability approaching 1. This result follows from a “double robustness” property analogous to that of popular partially linear regression estimators. By contrast, the conventional VAR confidence interval with…