论文检索

利用大型贝叶斯向量自回归估计并分解产出缺口AI 解读

Summary We consider how to estimate the trend and cycle of a time series, such as real gross domestic product, given a large information set. Our approach makes use of the Beveridge–Nelson decomposition based on a vector autoregression, but with two practical considerations. First, we show how to determine which conditioning variables span the relevant information by directly accounting for the Beveridge–Nelson trend and cycle in terms of contributions from different…

具有非对称尾部的金融收益条件分布建模AI 解读

Summary This paper proposes a conditional density model that allows for differing left/right tail indices and time‐varying volatility based on the dynamic conditional score (DCS) approach. The asymptotic properties of the maximum likelihood estimates are presented under verifiable conditions together with simulations showing effective estimation with practical sample sizes. It is shown that tail asymmetry is prevalent in global equity index returns and can be mistaken for…

极端收益与交易强度AI 解读

Summary Asymmetric information models of market microstructure claim that variables such as trading intensity are proxies for latent information on the value of financial assets. We consider the interval‐valued time series (ITS) of low/high returns and explore the relationship between these extreme returns and the intensity of trading. We assume that the returns (or prices) are generated by a latent process with some unknown conditional density. At each period of time, from…

大型时变参数VAR：一种非参数方法AI 解读

Summary In this paper we introduce a nonparametric estimation method for a large Vector Autoregression (VAR) with time‐varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs. When applied to the problem of forecasting key macroeconomic variables, the method…

离散医疗设施选择模型中的测量误差：塞内加尔城市案例AI 解读

Summary We use individual‐level health facility choice data from urban Senegal to estimate consumer preferences for facility characteristics related to maternal health services. We find that consumers consider a large number of quality‐related facility characteristics, as well as travel costs, when making their health facility choice. In contrast to the typical assumption in the literature, our findings indicate that individuals frequently bypass the facility nearest their…

含偶尔生效约束模型的似然计算AI 解读

Summary Applied researchers interested in estimating key parameters of dynamic stochastic general equilibrium models face an array of choices regarding numerical solution and estimation methods. We focus on the likelihood evaluation of models with occasionally binding constraints. We document how solution approximation errors and likelihood misspecification, related to the treatment of measurement errors, can interact and compound each other.

数据丰富环境中的宏观经济预测精度AI 解读

Summary The performance of six classes of models in forecasting different types of economic series is evaluated in an extensive pseudo out‐of‐sample exercise. One of these forecasting models, regularized data‐rich model averaging (RDRMA), is new in the literature. The findings can be summarized in four points. First, RDRMA is difficult to beat in general and generates the best forecasts for real variables. This performance is attributed to the combination of regularization…

混合比例风险模型中的内生删失及其在最优失业保险中的应用AI 解读

Summary We examine the sensitivity of estimates of the MPH model with respect to assumptions on the censoring mechanism in the context of an economic model of optimal unemployment insurance. We assume a parametric model for the duration of interest and leave the distribution of censoring unrestricted, allowing it to be correlated with observed and unobserved characteristics. We provide a practical characterization of the identified set with moment inequalities and suggest…

外生不确定性与使用外部工具变量的结构向量自回归识别AI 解读

Summary We provide necessary and sufficient conditions for the identification (point‐identification) of structural vector autoregressions (SVARs) with external instruments considering the case in which r instruments are used to identify g structural shocks of interest, r ≥ g ≥ 1. Novel frequentist estimation methods are discussed by considering both a “partial shocks” identification strategy, where only g structural shocks are of interest and are instrumented, and a “full…

民主发展中的隐藏分组模式：分组异质性的贝叶斯推断AI 解读

Summary We propose a nonparametric Bayesian approach to estimate time‐varying grouped patterns of heterogeneity in linear panel data models. Unlike the classical approach in Bonhomme and Manresa ( Econometrica , 2015, 83 , 1147–1184), our approach can accommodate selection of the optimal number of groups and model estimation jointly, and also be readily extended to quantify uncertainties in the estimated group structure. Our proposed approach performs well in Monte Carlo…

大多无害的模拟？用蒙特卡洛研究选择估计量AI 解读

Summary We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to help select a treatment effect estimator under unconfoundedness. We show theoretically that neither is likely to be informative except under restrictive conditions that are unlikely to be satisfied in many contexts. To test empirical relevance, we also apply the approaches to a real‐world setting where estimator performance is known. Both approaches are worse than…

含内生回归变量的异质面板中的结构变化AI 解读

Summary This paper extends Pesaran's ( Econometrica , 2006, 74 , 967–1012) common correlated effects (CCE) by allowing for endogenous regressors in large heterogeneous panels with unknown common structural changes in slopes and error factor structure. Since endogenous regressors and structural breaks are often encountered in empirical studies with large panels, this extension makes Pesaran's CCE approach empirically more appealing. In addition to allowing for slope…

含内生虚拟解释变量的广义双变量Probit模型估计AI 解读

Summary The purpose of this paper is to provide guidelines for empirical researchers who use a class of bivariate threshold crossing models with dummy endogenous variables. A common practice employed by the researchers is the specification of the joint distribution of unobservables as a bivariate normal distribution, which results in a bivariate probit model . To address the problem of misspecification in this practice, we propose an easy‐to‐implement semiparametric…

抵押贷款信贷可得性的测量：前沿估计方法AI 解读

Summary We construct a new measure of mortgage credit availability using a technique developed for production frontier estimation. The resulting “loan frontier” describes the maximum amount obtainable by a borrower of given characteristics. We estimate this frontier using mortgage originations data from 2001 to 2014. We find a substantial expansion of mortgage credit for all borrowers during the housing boom, not only for low‐score or low‐income borrowers. The subsequent…

用野生自助法改善聚类工具变量模型推断的两个应用AI 解读

Summary Microeconomic data often have within‐cluster dependence, which affects standard error estimation and inference. When the number of clusters is small, asymptotic tests can be severely oversized. In the instrumental variables (IV) model, the potential presence of weak instruments further complicates hypothesis testing. We use wild bootstrap methods to improve inference in two empirical applications with these characteristics. Building from estimating equations and…

尾部的故事：高维尾部相依性AI 解读

Summary We propose a simple and flexible framework that allows for a comprehensive analysis of tail interdependence in high dimensions. We use co‐exceedances to capture the structure of the dependence in the tails and, relying on the concept of multi‐information, define the coefficient of tail interdependence. Within this framework, we develop statistical tests of (i) independence in the tails, (ii) goodness‐of‐fit of the tail interdependence structure of a hypothesized…

固定时间维度面板中的共同相关效应估计AI 解读

Summary The presence of unobserved heterogeneity and its likely detrimental effect on inference has recently motivated the use of factor‐augmented panel regression models. The workhorse of this literature is based on first estimating the unknown factors using the cross‐section averages of the observables, and then applying ordinary least squares conditional on the first‐step factor estimates. This is the common correlated effects (CCE) approach, the existing asymptotic…

美国各州住房价格的共同变动与非对称尾部依赖：非参数方法AI 解读

Summary We reexamine the methods used in estimating comovements among US regional home prices and find that there are insufficient moments to ensure a normal limit necessary for employing the quasi‐maximum likelihood estimator. Hence we propose applying the self‐weighted quasi‐maximum exponential likelihood estimator and a bootstrap method to test and account for the asymmetry of comovements as well as different magnitudes across state pairs. Our results reveal interstate…

利用州级数据估计美国产出缺口AI 解读

Summary This paper develops a method to estimate the U.S. output gap by exploiting the cross‐sectional variation of state‐level output and unemployment rate data. The model assumes that there are common output and unemployment rate trend and cycle components, and that each state's output and unemployment rate are subject to idiosyncratic trend and cycle perturbations. I estimate the model with Bayesian methods using quarterly data from 2005:Q1 to 2018:Q2 for the 50 states…

全球通胀能否帮助预测工业化国家的通胀？AI 解读

Summary Ciccarelli and Mojon (CM; Review of Economics and Statistics , 2010, 92 (3), 524–535) propose an inflation forecasting model incorporating a global inflation factor and show that it consistently beats several standard forecasting benchmarks. We show that CM's global inflation model does not improve upon the Atkeson and Ohanian (AO; Federal Reserve Bank of Minneapolis Quarterly Review , 2001, 25 (1), 2–11) naive benchmark. However, we find that augmenting the AO model…

含移动平均成分的混频模型AI 解读

Summary Temporal aggregation in general introduces a moving‐average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed‐frequency (MF) model. The MA component is generally neglected, likely to preserve the possibility of ordinary least squares estimation, but the consequences have never been properly studied in the MF context. In this paper we show, analytically, in Monte Carlo…

风险厌恶异质性与风险分担回归AI 解读

Summary Heterogeneity in risk attitudes, if not properly accounted for, may induce a bias on the income coefficient of standard consumption insurance regressions. We show that, extending the theoretical analysis and empirical findings in Schulhofer‐Wohl ( Journal of Political Economy , 2011, 119 , 925–958), the sign of the bias is ambiguous, and depends on cycle‐related variables and on the covariances of both aggregate and idiosyncratic risk with individual risk aversion.

合并还是不合并：面板回归参数估计与预测的策略AI 解读

Summary This paper considers estimating the slope parameters and forecasting in potentially heterogeneous panel data regressions with a long time dimension. We propose a novel optimal pooling averaging estimator that makes an explicit trade‐off between efficiency gains from pooling and bias due to heterogeneity. By theoretically and numerically comparing various estimators, we find that a uniformly best estimator does not exist and that our new estimator is superior in…

泡沫与危机：对Anundsen等人研究结果的复现AI 解读

Summary This paper both narrowly and widely replicates the results of Anundsen et al. ( Journal of Applied Econometrics , 2016, 31 (7), 1291–1311). I am able to reproduce the same results as theirs. Furthermore, I find that allowing for time‐varying parameters of early warning system models can considerably improve the in‐sample model fit and out‐of‐sample forecasting performance based on an expanding window forecasting exercise.