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存在内生性的非线性协整幂函数回归AI 解读

This paper develops an asymptotic theory for nonlinear cointegrating power function regression. The framework extends earlier work on the deterministic trend case and allows for both endogeneity and heteroskedasticity, which makes the models and inferential methods relevant to many empirical economic and financial applications, including predictive regression. A new test for linear cointegration against nonlinear departures is developed based on a simple linearized…

利用时变波动率识别结构冲击AI 解读

Abstract I propose to identify an SVAR, up to shock ordering, using the autocovariance structure of the squared innovations implied by an arbitrary stochastic process for the shock variances. These higher moments are available without parametric assumptions on the variance process. In contrast, previous approaches exploiting heteroskedasticity rely on the path of innovation covariances, which can only be recovered from the data under specific parametric assumptions on the…

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…

单指数 copula 模型的半参数估计与变量选择AI 解读

Summary A copula with a flexibly dependence structure can capture complexity and heterogeneity in economic and financial time series. Based on the recently proposed single‐index copula, we propose a simultaneous variable selection and estimation procedure. This method allows for choosing the most relevant state variables by using a penalized estimation with large sample properties derived. Simulation results demonstrate the good performance of the method in selecting…

简约式因子扩展 VAR：利用稀疏性纳入具有经济含义的因子AI 解读

Summary Induced sparsity in the factor loading matrix identifies the factor basis, while rotational identification is obtained ex post by clustering methods closely related to machine learning. We extract meaningful economic concepts from a high‐dimensional data set, which together with observed variables follow an unrestricted, reduced‐form VAR process. Including a comprehensive set of economic concepts allows reliable, fundamental structural analysis, even of the factor…

矩阵补全、反事实与缺失数据的因子分析AI 解读

This paper proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data. Assuming that a strong factor structure holds for the full panel of data and its sub-blocks, it is shown that the common component can be consistently estimated at four different rates of convergence without requiring regularization or iteration. An asymptotic analysis of the…

人工对照的反事实分析：推断、高维与非平稳性AI 解读

Recently, there has been growing interest in developing statistical tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial counterfactual from a pool of “untre ated” peers, organized in a panel data structure. In this article, we consider a general framework for counterfactual…

反事实与合成控制的精确稳健共形推断方法AI 解读

We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably…

利用互联网搜索数据与 PRISM 预测失业保险初次申请人数AI 解读

Big data generated from the Internet offer great potential for predictive analysis. Here we focus on using online users’ Internet search data to forecast unemployment initial claims weeks into the future, which provides timely insights into the direction of the economy. To this end, we present a novel method Penalized Regression with Inferred Seasonality Module (PRISM), which uses publicly available online search data from Google. PRISM is a semiparametric method, motivated…

《文本选择》一文的讨论AI 解读

Kelly, Manela, and Moreira provided an economic model of word choice in text. A writer is modeled as someone who is first choosing whether to use a word at all (selection problem) and then deciding how often a selected word should be used (positive counts problem). The resulting model leads to better sufficient reduction for large number of words/phrases in the text as demonstrated many diverse applications that use information captured from the text of the front page of the…

文本选择AI 解读

Text data is ultra-high dimensional, which makes machine learning techniques indispensable for textual analysis. Text is often selected—journalists, speechwriters, and others craft messages to target their audiences’ limited attention. We develop an economically motivated high-dimensional selection model that improves learning from text (and from sparse counts data more generally). Our model is especially useful when the choice to include a phrase is more interesting than…

价格股利比与长期股票收益：得分驱动的状态空间模型AI 解读

Abstract–In this article, we develop a general framework to analyze state space models with time-varying system matrices, where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying matrices. We use this method to study the time-varying relationship between the price dividend ratio, expected stock returns and expected dividend growth in the United…

分位数格兰杰因果性的度量AI 解读

We consider measures of Granger causality in quantiles, which detect and quantify both linear and nonlinear causal effects between random variables. The measures are based on nonparametric quantile regressions and defined as logarithmic functions of restricted and unrestricted expectations of quantile check loss functions. They can consistently be estimated by replacing the unknown expectations of check loss functions by their nonparametric kernel estimates. We derive a…

预测支配关系的一般条件AI 解读

Recent studies have analyzed whether one forecast method dominates another under a class of consistent scoring functions. While the existing literature focuses on empirical tests of forecast dominance, little is known about the theoretical conditions under which one forecast dominates another. To address this question, we derive a new characterization of dominance among forecasts of the mean functional. We present various scenarios under which dominance occurs. Unlike…

协调高斯似然与 Whittle 似然：频域估计的应用AI 解读

In time series analysis there is an apparent dichotomy between time and frequency domain methods. The aim of this paper is to draw connections between frequency and time domain methods. Our focus will be on reconciling the Gaussian likelihood and the Whittle likelihood. We derive an exact, interpretable, bound between the Gaussian and Whittle likelihood of a second order stationary time series. The derivation is based on obtaining the transformation which is biorthogonal to…

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…

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…

因子模型的极限定理AI 解读

This paper establishes central limit theorems (CLTs) and proposes how to perform valid inference in factor models. We consider a setting where many counties/regions/assets are observed for many time periods, and when estimation of a global parameter includes aggregation of a cross-section of heterogeneous microparameters estimated separately for each entity. The CLT applies for quantities involving both cross-sectional and time series aggregation, as well as for quadratic…

具有无限方差 GARCH 误差的非平稳线性过程AI 解读

Recently, Cavaliere, Georgiev, and Taylor (2018, Econometric Theory 34, 302–348) (CGT) considered the augmented Dickey–Fuller (ADF) test for a unit-root model with linear noise driven by i.i.d. infinite variance innovations and showed that ordinary least squares (OLS)-based ADF statistics have the same distribution as in Chan and Tran (1989, Econometric Theory 5, 354–362) for i.i.d. infinite variance noise. They also proposed an interesting question to extend their results…