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利用报纸报道识别制裁对伊朗经济的影响AI 解读

Summary This paper focuses on the identification and quantitative estimation of sanctions on the Iranian economy over the period 1989–2019. It provides a new time series approach and proposes a novel measure of sanctions intensity based on daily newspaper coverage. In absence of sanctions, Iran's average annual growth could have been around 4–5%, as compared to the 3% realized. Estimates of the proposed sanctions‐augmented structural VAR show that sanctions significantly…

税收政策与企业非连续大额投资：中国增值税改革的证据AI 解读

Abstract We incorporate the lumpy nature of firm-level investment into the study of how tax policy affects investment behaviour. We show that tax policies can directly impact the lumpiness of investment. Extensive-margin responses to tax policy are key to understanding the effects of different tax reforms and to designing effective stimulus policies. We illustrate these results by studying China’s 2009 VAT reform, which lowered the tax cost of investment and reduced partial…

重印：广义自回归条件异方差AI 解读

A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an empirical example relating to the uncertainty of the inflation rate is presented.

重印：随机前沿生产函数模型的设定与估计AI 解读

Previous studies of the so-called frontier production function have not utilized an adequate characterization of the disturbance term for such a model. In this paper we provide an appropriate specification, by defining the disturbance term as the sum of symmetric normal and (negative) half-normal random variables. Various aspects of maximum-likelihood estimation for the coefficients of a production function with an additive disturbance term of this sort are then considered.

重印：动态面板模型的初始条件与矩限制AI 解读

Estimation of the dynamic error components model is considered using two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator. Both estimators require restrictions on the initial conditions process. Asymptotic efficiency comparisons and Monte Carlo simulations for the simple AR(1) model demonstrate the dramatic improvement in performance of the proposed estimators compared to the usual first-differenced GMM…

初始条件与 Blundell–Bond 估计量AI 解读

We place the Blundell–Bond paper in the context of the early development of panel data estimators that accounted for unobserved heterogeneity, dynamics and persistent economic series. The initial work focused on appropriate econometric methods to estimate dynamic models using unbalanced panel data with many firms and/or individuals but covering a small number of time periods. Eliminating the unobserved firm-specific ‘fixed’ effects by taking first-differences and using as…

利用离散傅里叶变换检验高维因子模型的结构变化AI 解读

We propose a new test for structural changes in large dimensional factor models using a discrete Fourier transform (DFT) approach. When structural changes occur, the conventional principal component analysis may fail to estimate the common factors and factor loadings consistently, and the estimated residuals contain information about the structural changes. This allows us to compare the DFT of the estimated residuals weighted by the estimated common factors with the null…

含缺失观测的高维潜在因子模型及其因果推断应用AI 解读

This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We propose an easy-to-use all-purpose estimator for a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data. We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under an approximate factor model and general…

基于高频数据识别潜在因子AI 解读

This paper tests whether the continuous component of an observable candidate factor is in the space spanned by the counterparts of latent common factors with high-frequency financial data. We introduce two identification strategies corresponding to two types of regressions: the regressions of intraday asset returns on the estimated factors and the candidate, and the regression of the candidate factor on the estimated ones. We construct the test statistics by adding…

潜在因子模型的信息准则：因子广泛性与自适应性AI 解读

We study the information criteria extensively under general conditions for high-dimensional latent factor models. Upon carefully analyzing the estimation errors of the principal component analysis method, we establish theoretical results on the estimation accuracy of the latent factor scores, incorporating the impact from possibly weak factor pervasiveness; our analysis does not require the same factor strength of all the leading factors. To estimate the number of the latent…

高维因子模型突变点的拟极大似然估计AI 解读

This paper proposes a quasi-maximum likelihood (QML) estimator of the break point for large-dimensional factor models with a single structural break in the factor loading matrix. We show that the QML estimator is consistent for the true break point when the covariance matrix of the pre- or post-break factor loading (or both) is singular. Consistency here means that the deviation of the estimated break date from the true break date k0 converges to zero as the sample size…

少量时间期数下交互固定效应模型的处理效应AI 解读

This paper considers identifying and estimating the Average Treatment Effect on the Treated (ATT) when untreated potential outcomes are generated by an interactive fixed effects model. That is, in addition to time-period and individual fixed effects, we consider the case where there is an unobserved time invariant variable whose effect on untreated potential outcomes may change over time and which can therefore cause outcomes (in the absence of participating in the…

含共同因子的高维向量自回归模型AI 解读

This paper studies high-dimensional vector autoregressions (VARs) augmented with common factors that allow for strong cross-sectional dependence. Models of this type provide a convenient mechanism for accommodating the interconnectedness and temporal co-variability that are often present in large dimensional systems. We propose an ℓ1-nuclear-norm regularized estimator and derive the non-asymptotic upper bounds for the estimation errors as well as large sample asymptotics for…

大型因子模型多重结构突变的组融合 LASSO 方法AI 解读

This paper reformulates the identification of multiple structural breaks in factor loadings as a problem of detecting structural breaks in a factor regression, where the estimated pseudo factor corresponding to the largest eigenvalue is regressed on the remaining estimated factors. As a result, a group fused Lasso based estimation procedure is proposed to identify the break dates. Our procedure is practically easy-to-implement with standard statistical packages, overcoming…

高维面板数据缺失值与协方差的因子插补AI 解读

Economists are blessed with a wealth of data for analysis, but more often than not, values in some entries of the data matrix are missing. Various methods have been proposed to handle missing observations in a few variables. We exploit the factor structure in panel data of large dimensions. Our tall-project algorithm first estimates the factors from a tall block in which data for all rows are observed, and projections of unit specific sample size are then used to estimate…

协方差矩阵 Kronecker 积结构的检验AI 解读

We propose a test for a covariance matrix to have Kronecker Product Structure (KPS). KPS implies a reduced rank restriction on an invertible transformation of the covariance matrix and the new procedure is an adaptation of the Kleibergen-Paap (2006) reduced rank test. The main extension concerns the singularity of the covariance matrix estimator involved in the rank test which complicates the derivation of its limiting distribution. We show this limiting distribution to be…

因子结构误差下网络溢出效应的收缩估计AI 解读

This paper explores the estimation of a panel data model with cross-sectional interaction that is flexible both in its approach to specifying the network of connections between cross-sectional units, and in controlling for unobserved heterogeneity. It is assumed that there are different sources of information available on a network, which can be represented in the form of multiple weights matrices. These matrices may reflect observed links, different measures of…

组成员身份与系数发生结构突变的面板分组模型估计AI 解读

This paper considers linear panel data models with a grouped pattern of heterogeneity when the latent group membership structure and/or the values of slope coefficients change at a break point. We propose a least squares approach to jointly estimate the break point, group membership structure, and coefficients. The proposed estimators are consistent, and the asymptotic distribution of the coefficient estimators is identical to that under known break point and group structure…

异质分布下的多维潜在分组结构AI 解读

This paper aims to identify the multi-dimensional latent grouped heterogeneity of distributional effects. We consider a panel quantile regression model with additive cross-section and time fixed effects. The cross-section effects and quantile slope coefficients are both characterized by grouped patterns of heterogeneity, but each unit can belong to different groups for cross-section effects and slopes. We propose a composite-quantile approach to jointly estimate…

衰退与复苏期间对小企业的支持AI 解读

We use variation in the access to a government credit certification program to estimate the financial and real effects of supporting small firms. This program was first implemented during the global financial crisis, but has remained active ever since, allowing us to analyze its effects both during recessions and recoveries. Eligible firms have access to government loan guarantees and a credit quality certification. We estimate real effects using a multidimensional…

用于车队车辆保险定价的分层随机效应模型AI 解读

Summary We propose a count‐data model with hierarchical random effects for the posterior insurance ratemaking of vehicles belonging to a fleet, by allowing random effects for the fleet, the vehicles, and time. We derive a simple closed‐form ratemaking formula based on a hierarchical random‐effects specification. We estimate the corresponding econometric model and compute insurance premiums according to the past experience of both the vehicle and the fleet. Our model can be…

宏观数据不确定性下的贝叶斯向量自回归密度预测AI 解读

Summary Macroeconomic data are subject to data revisions. Yet, the usual way of generating real‐time density forecasts from Bayesian Vector Autoregressive (BVAR) models makes no allowance for data uncertainty from future data revisions. We develop methods of allowing for data uncertainty when forecasting with BVAR models with stochastic volatility. First, the BVAR forecasting model is estimated on real‐time vintages. Second, the BVAR model is jointly estimated with a model…

金融状况指数对经济增长的实时预测信息AI 解读

Summary We provide evidence on the real‐time predictive content of the National Financial Conditions Index (NFCI), for conditional quantiles of U.S. real GDP growth. Our work is distinct from the literature in two specific ways. First, we construct (unofficial) real‐time vintages of the NFCI. This allows us to conduct out‐of‐sample analysis without introducing the kind of look‐ahead biases that are naturally introduced when using a single current vintage. We then develop…

重新评估1973年以来经济增长与金融状况的依赖关系AI 解读

Summary Adrian, Boyarchenko and Giannone ((2019), ABG) adapt quantile regression (QR) methods to examine the relationship between US economic growth and financial conditions. We confirm their empirical findings, using their methodology and their pre‐2016 sample. Mindful of the importance of the Covid‐19 pandemic, we extend the sample to 2021Q3 and find attenuation of the key estimated coefficients using ABG's empirical methods. Given the pandemic observations, we provide…