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动态处理效应非参数模型中的识别AI 解读

This paper develops a nonparametric model that represents how sequences of outcomes and treatment choices influence one another in a dynamic manner. In this setting, we are interested in identifying the average outcome for individuals in each period, had a particular treatment sequence been assigned. The identification of this quantity allows us to identify the average treatment effects (ATE’s) and the ATE’s on transitions, as well as the optimal treatment regimes, namely,…

利用时变波动率识别 VAR：内生不确定性应用AI 解读

We develop a structural vector autoregression with stochastic volatility in which one of the variables can impact both the mean and the variance of the other variables. We provide conditional posterior distributions for this model, develop an MCMC algorithm for estimation, and show how stochastic volatility can be used to provide useful restrictions for the identification of structural shocks. We then use the model with US data to show that some variables have a significant…

含非线性解释变量的结构动态模型脉冲响应分析AI 解读

We study the construction of nonlinear impulse responses in linear structural dynamic models that include nonlinearly transformed regressors. We derive the closed-form solution for the population impulse responses to a given shock and propose a control function approach to estimating these responses without taking a stand on how the remainder of the model is identified. Our plug-in estimator dispenses with the need for simulations and, unlike conventional local projection…

大型向量自回归中的潜在群组识别AI 解读

This work introduces the stochastic block vector autoregressive (SB-VAR) model. In this class of vector autoregressions, the time series are partitioned into latent groups such that spillover effects are stronger among series that belong to the same group than otherwise. A key question that arises in this framework is how to detect the latent groups from a sample of observations generated by the model. To this end, we propose a group detection algorithm based on the…

贝叶斯代理工具变量 SVAR 的推断AI 解读

Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), we develop an algorithm for exact finite sample inference in this class of time series models, commonly known as Proxy-SVARs. Our algorithm makes independent draws from any posterior distribution over the structural parameterization of a Proxy-SVAR. Our approach allows researchers to simultaneously use proxies and traditional zero and sign restrictions to identify…

利用高阶无条件矩识别结构向量自回归AI 解读

This paper pursues two objectives. First, we determine the sufficient condition for local, statistical identification of SVAR processes through the third and fourth unconditional moments of the reduced-form innovations. Our findings provide novel insights when the entire system is not identified, as they highlight which subset of structural parameters is identified and which is not. Second, we elaborate a tractable testing procedure to verify whether the identification…

外部工具变量识别的结构VAR推断AI 解读

This paper studies Structural Vector Autoregressions in which a structural shock of interest (e.g., an oil supply shock) is identified using an external instrument. The external instrument is taken to be correlated with the target shock (the instrument is relevant) and to be uncorrelated with other shocks of the model (the instrument is exogenous). The potential weak correlation between the external instrument and the target structural shock compromises the large-sample…

受限随机占优的改进自助检验AI 解读

Bootstrap Testing for restricted stochastic dominance of a pre-specified order between two distributions is of interest in many areas of economics. This paper develops a new method for improving the performance of such tests that employ a moment selection procedure: tilting the empirical distribution in the moment selection procedure. We propose that the amount of tilting be chosen to maximize the empirical likelihood subject to the restrictions of the null hypothesis, which…

大型面板数据模型的递归估计：理论与实践AI 解读

Bai (2009) proposes recursive estimation for panel data models with interactive effects. We study the behaviours of this recursive estimator. The recursive formula is established that shows the behaviours of recursive estimators depend on the initial estimator, the population structure and the iterative steps. Under some general scenarios, we find that the recursive estimator becomes consistent after the first iteration from any initials. We also obtain the optimal number of…

半参数 GARCH 模型的自适应推断AI 解读

This paper considers a semiparametric generalized autoregressive conditional heteroskedasticity (S-GARCH) model. For this model, we first estimate the time-varying long run component for unconditional variance by the kernel estimator, and then estimate the non-time-varying parameters in GARCH-type short run component by the quasi maximum likelihood estimator (QMLE). We show that the QMLE is asymptotically normal with the parametric convergence rate. Next, we construct a…

时变工具变量估计AI 解读

We develop non-parametric instrumental variable estimation and inferential theory for econometric models with possibly endogenous regressors whose coefficients can vary over time either deterministically or stochastically, and the time-varying and uniform versions of the standard Hausman exogeneity test. After deriving the asymptotic properties of the proposed procedures, we assess their finite sample performance by means of a set of Monte Carlo experiments, and illustrate…

网络形成的计量模型：企业董事交叉任职应用AI 解读

We study identification of the players’ preferences in a network formation game featuring complete information, nonreciprocal links, and a spillover effect. We decompose the network formation game into local games such that the network formation game is in equilibrium if and only if each local game is in equilibrium. This decomposition helps us prove equilibrium existence, reduce the number of moment inequalities characterising the identified set, and simplify the…

零常返时间序列中的稳健非线性回归估计AI 解读

In this article, we study parametric robust estimation in nonlinear regression models with regressors generated by a class of non-stationary and null recurrent Markov processes. The nonlinear regression functions can be either integrable or asymptotically homogeneous, covering many commonly-used functional forms in parametric nonlinear regression. Under regularity conditions, we derive both the consistency and limit distribution results for the developed general robust…

空间横截面与面板模型的同方差诊断检验AI 解读

We propose an Adjusted Quasi-Score (AQS) method for constructing tests for homoskedasticity in spatial econometric models. We first obtain an AQS function by adjusting the score-type function from the given model to achieve unbiasedness, and then develop an Outer-Product-of-Martingale-Difference (OPMD) estimate of its variance. In standard problems where a genuine (quasi) score vector is available, the AQS–OPMD method leads to finite sample improved tests over the usual…

基于指数倾斜 Hellinger 距离的稳健估计AI 解读

This paper is concerned with estimation of parameters defined by moment equalities. We introduce the exponentially tilted Hellinger distance (ETHD) estimator which is efficient under correct specification, and robust to both global and local misspecification. In the spirit of Schennach (2007), ETHD combines the Hellinger distance and the Kullback–Leibler information criterion. We show that it achieves optimal minimax robust properties under local deviations from the model…

高阶随机波动率模型的简便估计与推断AI 解读

We study the problem of estimating higher-order stochastic volatility [SV(p)] models. Due to the difficulty of evaluating the likelihood function, this remains a challenging problem, even in the relatively simple SV(1) case. We propose simple moment-based winsorized ARMA-type estimators, which are computationally inexpensive and remarkably accurate. The proposed estimators do not require choosing a sampling algorithm, initial parameter values, or an auxiliary model. We show…

期权隐含状态价格密度的筛估计AI 解读

This paper proposes a nonparametric estimator for the state price density implied by a single cross-section of European options with different strikes and the same maturity. The proposed estimator has two distinctive features. First, it extracts information from both call and put options, as opposed to only call options. Second, it does not require estimating any second-order derivative; instead, it solves a constrained and penalized linear regression. The asymptotic…

非平稳随机波动率下的自助法AI 解读

In this paper we investigate to what extent the bootstrap can be applied to conditional mean models, such as regression or time series models, when the volatility of the innovations is random and possibly non-stationary. In fact, the volatility of many economic and financial time series displays persistent changes and possible non-stationarity. However, the theory of the bootstrap for such models has focused on deterministic changes of the unconditional variance and little…

具有良好尺寸与功效表现的简便股票收益可预测性检验AI 解读

We develop easy-to-implement tests for return predictability which, relative to extant tests in the literature, display attractive finite sample size control and power across a wide range of persistence and endogeneity levels for the predictor. Our approach is based on the standard regression t-ratio and a variant where the predictor is quasi-GLS (rather than OLS) demeaned. In the strongly persistent near-unit root environment, the limiting null distributions of these…

具有共同冲击的动态空间面板数据模型AI 解读

This paper studies dynamic spatial panel data models with common shocks to deal with both weak and strong cross-sectional correlations. Weak correlations are captured by a spatial structure and strong correlations are captured by a factor structure. The proposed quasi-maximum likelihood estimator (QMLE) is capable of handling both types of cross sectional dependence. We provide a rigorous analysis for the asymptotic theory of the QMLE, demonstrating its desirable properties.…

协趋势与协整综合模型中简单能量平衡方程的统计检验AI 解读

We develop new tests for the linear relationship between temperature and forcing, which is one of the most studied implications from a simple energy balance model. We consider a bivariate system of temperature and forcing where the time path of well-mixed-greenhouse-gases forcing is included as a potential common trend function in addition to a stochastic trend and a broken linear trend. Our test statistics are first devised as the likelihood ratio and then are modified to…

持久性经济系统中预测回归的有效推断AI 解读

This paper studies standard predictive regressions in economic systems governed by persistent vector autoregressive dynamics for the state variables. In particular, all – or a subset – of the variables may be fractionally integrated, which induces a spurious regression problem. We propose a new inference and testing procedure – the Local speCtruM (LCM) approach – for joint significance of the regressors, that is robust against the variables having different integration…

利用平滑聚类标准误进行时间序列模型推断AI 解读

This paper proposes a long run variance estimator for conducting inference in time series regression models that combines the nonparametric approach with a cluster approach. The basic idea is to divide the time periods into non-overlapping clusters. The long run variance estimator is constructed by first aggregating within clusters and then kernel smoothing across clusters or applying the nonparametric series method to the clusters with Type II discrete cosine transform. We…

时变参数高维预测回归的 Boosting 估计AI 解读

High dimensional predictive regressions are useful in wide range of applications. However, the theory is mainly developed assuming that the model is stationary with time invariant parameters. This is at odds with the prevalent evidence for parameter instability in economic time series, but theories for parameter instability are mainly developed for models with a small number of covariates. In this paper, we present two L2 boosting algorithms for estimating high dimensional…