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专利、新闻与经济周期AI 解读

Abstract We exploit information in patent applications to construct an instrumental variable for the identification of technology news shocks that relaxes all the identifying assumptions traditionally used in the literature. The instrument recovers news shocks that have no effect on aggregate productivity in the short-run, but are a significant driver of its trend component. The shock prompts a broad-based expansion in anticipation of the future increase in total factor…

一般条件下基于局部投影的推断AI 解读

Abstract This article develops the uniform asymptotic theory for local projection (LP) regression when the true lag order of the model is unknown and potentially infinite. The theory allows for varying degrees of persistence in the data, growing response horizons, and general conditionally heteroskedastic martingale-difference shocks. Based on the theory, we make two main contributions. First, we show that LPs can achieve semiparametric efficiency at a given horizon under…

对美联储的政治压力AI 解读

Abstract This paper combines new data and a narrative approach to identify variation in political pressure on the Federal Reserve. From archival records, I build a data set of personal interactions between U.S. Presidents and Fed officials between 1933 and 2016. Since personal interactions do not necessarily reflect political pressure, I develop a narrative identification strategy based on President Nixon’s pressure on Fed Chair Burns. I exploit this narrative through…

Abstract We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive “imputation” form when treatment-effect heterogeneity is unrestricted. We…

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…

马尔可夫机制转换的似然比检验AI 解读

Abstract Markov regime-switching models are very common in economics and finance. Despite persisting interest in them, the asymptotic distributions of likelihood ratio-based tests for detecting regime switching remain unknown. This study examines such tests and establishes their asymptotic distributions in the context of nonlinear models, allowing multiple parameters to be affected by regime switching. The analysis addresses three difficulties: (i) some nuisance parameters…

非基本结构VARMA模型的识别与估计AI 解读

Abstract The basic assumption of a structural vector autoregressive moving average (SVARMA) model is that it is driven by a white noise whose components are uncorrelated or independent and can be interpreted as economic shocks, called “structural” shocks. When the errors are Gaussian, independence is equivalent to non-correlation and these models face two identification issues. The first identification problem is “static” and is due to the fact that there is an infinite…

存在总量冲击时的估计AI 解读

Abstract Aggregate shocks affect most households’ and firms’ decisions. Using three stylized models, we show that inference based on cross-sectional data alone generally fails to correctly account for decision making of rational agents facing aggregate uncertainty. We propose an econometric framework that overcomes these problems by explicitly parameterizing the agents’ decision problem relative to aggregate shocks. Our framework and examples illustrate that the…

非线性DSGE模型的剪枝状态空间系统：理论与实证应用AI 解读

This article studies the pruned state-space system for higher-order perturbation approximations to dynamic stochastic general equilibrium (DSGE) models. We show the stability of the pruned approximation up to third order and provide closed-form expressions for first and second unconditional moments and impulse response functions. Our results introduce generalized method of moments (GMM) estimation and impulse-response matching for DSGE models approximated up to third order…

结构向量自回归的解析：衡量财政乘数的统一框架AI 解读

Do tax cuts and spending increases stimulate output? Studies that identify fiscal shocks using structural vector autoregressions (SVAR) have reached different conclusions. In this article, we show analytically that this lack of consensus reflects different assumptions on the fiscal rules that—by relating tax and spending policies to macroeconomic conditions—determine the identification of fiscal shocks and the associated fiscal multipliers. We then propose a new…

存在结构不稳定性的高维因子模型收缩估计AI 解读

In large-scale panel data models with latent factors the number of factors and their loadings may change over time. Treating the break date as unknown, this article proposes an adaptive group-LASSO estimator that consistently determines the numbers of pre- and post-break factors and the stability of factor loadings if the number of factors is constant. We develop a cross-validation procedure to fine-tune the data-dependent LASSO penalties and show that after the number of…

货币政策的信号效应AI 解读

We develop a dynamic general equilibrium model in which the policy rate signals the central bank’s view about macroeconomic developments to price setters. The model is estimated with likelihood methods on a U.S. data set that includes the Survey of Professional Forecasters as a measure of price setters’ inflation expectations. This model improves upon existing perfect information models in explaining why, in the data, inflation expectations respond with delays to monetary…

部分不稳定广义矩模型中的有效推断AI 解读

This paper considers time series Generalized Method of Moments (GMM) models where a subset of the parameters are time varying. We focus on an empirically relevant case with moderately large instabilities, which are well approximated by a local asymptotic embedding that does not allow the instability to be detected with certainty, even in the limit. We show that for many forms of the instability and a large class of GMM models, usual GMM inference on the subset of stable…

多重结构突变模型的估计与预测AI 解读

This paper develops a new approach to change-point modelling that allows the number of change-points in the observed sample to be unknown. The model we develop assumes that regime durations have a Poisson distribution. It approximately nests the two most common approaches: the time-varying parameter (TVP) model with a change-point every period and the change-point model with a small number of regimes. We focus considerable attention on the construction of reasonable…

回归系数一般持续性时变的有效检验AI 解读

There are a large number of tests for instability or breaks in coefficients in regression models designed for different possible departures from the stable model. We make two contributions to this literature. First, we consider a large class of persistent breaking processes that lead to asymptotically equivalent efficient tests. Our class allows for many or relatively few breaks, clustered breaks, regularly occurring breaks, or smooth transitions to changes in the regression…

灵活损失函数下预测理性的估计与检验AI 解读

In situations where a sequence of forecasts is observed, a common strategy is to examine "rationality" conditional on a given loss function. We examine this from a different perspective—supposing that we have a family of loss functions indexed by unknown shape parameters, then given the forecasts can we back out the loss function parameters consistent with the forecasts being rational even when we do not observe the underlying forecasting model? We establish identification…

动态似不相关协整回归AI 解读

We propose the parametric Dynamic Seemingly Unrelated Regression (DSUR) estimator for simultaneous estimation of multiple cointegrating regressions. DSUR is efficient when the equilibrium errors are correlated across equations and is applicable for panel cointegration estimation in environments where the cross section is small relative to the available time series. We study the asymptotic and small sample properties of the DSUR estimator for both heterogeneous and…

未知形式条件异方差下时间序列条件均值模型的广义谱检验AI 解读

Economic theories in time series contexts usually have implications on and only on the conditional mean dynamics of underlying economic variables. We propose a new class of specification tests for time series conditional mean models, where the dimension of the conditioning information set may be infinite. Both linear and nonlinear conditional mean specifications are covered. The tests can detect a wide range of model misspecifications in mean while being robust to…

多元时间序列共同断点的检验与定年AI 解读

This paper develops methods for constructing asymptotically valid confidence intervals for the date of a single break in multivariate time series, including I(0), I(1), and deterministically trending regressors. Although the width of the asymptotic confidence interval does not decrease as the sample size increases, it is inversely related to the number of series which have a common break date, so there are substantial gains to multivariate inference about break dates. These…

动态均衡经济：比较模型与数据的框架AI 解读

We propose a constructive, multivariate framework for assessing agreement between (generally misspecified) dynamic equilibrium models and data, which enables a complete second-order comparison of the dynamic properties of models and data. We use bootstrap algorithms to evaluate the significance of deviations between models and data, and we use goodness-of-fit criteria to produce estimators that optimize economically-relevant loss functions. We provide a detailed illustrative…

会计约束下的测量误差：潜在数据的点与区间估计及英国 GDP 应用AI 解读

An econometric methodology is proposed for reconciling inaccurate measures of latent data which are subject to accounting constraints. The method deals with the case in which the measurement errors are serially correlated, generalizing previous contributions. A class of efficient estimators are derived for the latent data. Consistent estimators for the weight matrices applied to the observed information based on a linear regression procedure are obtained together with…

协整下两阶段最小二乘估计量的统计性质AI 解读

We derive the limiting properties of the two-stage least squares estimator of an equation in a dynamic simultaneous model when variables are nonstationary and cointegrated. The implication on hypothesis testing is also discussed. It is shown that in a structural equation approach what one needs to worry about are the classical issues of identification and estimation, not nonstationarity and cointegration. Conventional formulae for computing the asymptotic covariance of the…

相关误差下若干单位根检验的改进及其局部渐近性质AI 解读

Many unit root tests have distorted sizes when the root of the error process is close to the unit circle. This paper analyses the properties of the Phillips-Perron tests and some of their variants in the problematic parameter space. We use local asymptotic analyses to explain why the Phillips-Perron tests suffer from severe size distortions regardless of the choice of the spectral density estimator but that the modified statistics show dramatic improvements in size when used…

含确定性趋势变量的非线性计量模型AI 解读

This paper considers an alternative asymptotic framework to standard sequential asymptotics for nonlinear models with deterministically trending variables. The asymptotic distributions of generalized method of moments estimators and corresponding test statistics are derived using this framework. The asymptotic distributions are shown to be the same with deterministically trending variables as with non-trending variables. That is, the distributions are normal and chi-squared…