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多重冲击的最大份额识别：不确定性与金融条件的应用AI 解读

We generalize the Max Share approach to allow for simultaneous identification of a multiplicity of shocks in a Structural Vector Autoregression. Our machinery therefore overcomes the well-known drawbacks that individually identified shocks (i) tend to be correlated to each other or (ii) can be separated under orthogonalizations with weak economic ground. We show that identification corresponds to solving a non-trivial optimization problem. We provide conditions for…

具有内生波动率状态切换的统计识别结构向量自回归AI 解读

We introduce a structural vector autoregressive model with endogenously switching conditional covariance matrix.The structural shocks are identified by simultaneously diagonalizing the reduced form error covariance matrices.It is not, however, always clear whether the condition for the full statistical identification is satisfied, and its validity is difficult to justify formally.Therefore, we provide general sets of conditions, that allow to combine sign and zero…

协整向量自回归过程的统一推断AI 解读

Uniformly valid inference for cointegrated vector autoregressive processes has so far proven difficult due to certain discontinuities arising in the asymptotic distribution of the least squares estimator. We extend asymptotic results from the univariate case to multiple dimensions and show how inference can be based on these results. Furthermore, we show that lag augmentation and a recent instrumental variable procedure can also yield uniformly valid tests and confidence…

作为宏观经济尾部风险的自然灾害AI 解读

We introduce quantile and moment impulse response functions for structural quantile vector autoregressive models. We use them to study how climate-related natural disasters affect the predictive distribution of output growth and inflation. Disasters strongly shift the forecast distribution particularly in the tails. They result in an initial sharp increase of the downside risk for growth, followed by a temporary rebound. Upside risk to inflation increases markedly for a few…

从 MIDAS 视角看不确定性、偏度与商业周期AI 解读

ABSTRACT We employ a mixed‐frequency quantile regression approach to model the time‐varying conditional distribution of the US real GDP growth rate. We show that monthly information on financial conditions improves the predictive power of an otherwise quarterly‐only model. We combine selected quantiles of the estimated conditional distribution to produce novel measures of uncertainty and skewness. Embedding these measures in a VAR framework, we show that unexpected changes…

脉冲响应的均匀先验AI 解读

There has been a call for caution regarding the standard procedure for Bayesian inference in set‐identified structural vector autoregressions on the grounds that the common practice of using a uniform prior over the set of orthogonal matrices induces a non‐uniform prior for individual impulse responses or other quantities of interest. This paper challenges this call by formally showing that when the focus is on joint inference, the uniform prior over the set of orthogonal…

使用影子利率 VAR 进行预测AI 解读

Vector autoregressions (VARs) are popular for forecasting, but ill‐suited to handle occasionally binding constraints, like the effective lower bound on nominal interest rates. We examine reduced‐form “shadow rate VARs” that model interest rates as censored observations of a latent shadow rate process and develop an efficient Bayesian estimation algorithm that accommodates large models. When compared to a standard VAR, our better‐performing shadow rate VARs generate superior…

高维时间序列的一致因果推断AI 解读

A methodology for high-dimensional causal inference in a time series context is introduced. Time series dynamics are captured by a Gaussian copula, and estimation of the marginal distribution of the data is not required. The procedure can consistently identify the parameters that describe the dynamics of the process and the conditional causal relations among the possibly high-dimensional variables, under sparsity conditions. Identification of the causal relations is in the…

非参数冲击贝叶斯 VAR 的快速且排序不变推断AI 解读

Summary The shocks that hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non‐Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper that uses a Dirichlet process mixture (DPM) to model the reduced‐form shocks. However, we do not follow the obvious strategy of simply modeling the VAR errors with a DPM as this would lead to computationally infeasible Bayesian inference in larger VARs…

平滑转换向量自回归模型的易实施贝叶斯估计AI 解读

Summary We develop a tractable way of estimating the parameters ruling the nonlinearity in the popular smooth transition VAR model, and identify structural shocks using external instruments. This jointly offers an alternative to the option of identifying shocks recursively and calibrating key parameters. In an illustration, we show that monetary policy shocks generate larger effects on economic activity during economic expansions compared to economic recessions. We then…

Summary In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local projections, while leaving the impulse response parameter of interest unpenalized. We establish the uniform asymptotic normality of the proposed estimator under general conditions. Finally, we demonstrate small sample performance through a simulation study and consider two canonical…

Proxy-VAR 中强外生性的检验AI 解读

Proxy variables have gained widespread prominence as indispensable tools for identifying structural VAR models. Analogous to instrumental variables, proxies need to be exogenous, i.e. uncorrelated with all non-target shocks. Assessing the exogeneity of proxies has traditionally relied on economic arguments rather than statistical tests. We argue that the economic rationale underlying the construction of commonly used proxy variables aligns with a stronger form of exogeneity.…

随机子空间局部投影AI 解读

Abstract We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over regressions estimated over different combinations of subsets of these controls. We document three key results: (i) Our approach can successfully recover the impulse response functions across Monte Carlo experiments representative of different…

利用离散时间观测估计连续时间线性 DSGE 模型AI 解读

We provide a general state space framework for estimation of the parameters of continuous-time linear DSGE models from discrete-time data. Our approach relies on the exact discrete-time representation of the equilibrium dynamics, hence avoiding discretization errors. We construct the exact likelihood for data sampled either as stocks or flows, based on the Kalman filter, and provide necessary and sufficient conditions for local identification of the frequency-invariant…

非高斯结构向量自回归模型的设定检验AI 解读

We propose specification tests for independent component analysis and structural vector autoregressions that assess the cross-sectional independence of non-Gaussian shocks by comparing their joint cumulative distribution with the product of their marginals at both discrete and continuous grids of argument values, the latter yielding a consistent test. We explicitly consider the sampling variability from computing the shocks using consistent estimators. We study the finite…

美国银行间流动性风险网络的情景分位数连通性AI 解读

We characterize the U.S. interbank liquidity risk network based on a supervisory dataset, using a scenario-based quantile network connectedness approach. In terms of methodology, we consider a quantile vector autoregressive model with unobserved heterogeneity and propose a Bayesian nuclear norm estimation method. A common factor structure is employed to deal with unobserved heterogeneity that may exhibit endogeneity within the network. Then we develop a scenario-based…

具有动态因子系数与条件异方差误差的向量自回归AI 解读

We introduce a new and general methodology for analyzing vector autoregressive models with time-varying coefficient matrices and conditionally heteroskedastic disturbances. The proposed approach is transparent and simple to implement. It allows the derivation of well-defined impulse response functions that rely on the overall stability of the system. We present the finite sample properties of the model in a simulation study. In an empirical illustration we investigate the…

局部投影与向量自回归：数千种数据生成过程的启示AI 解读

We conduct a simulation study of Local Projection (LP) and Vector Autoregression (VAR) estimators of structural impulse responses across thousands of data generating processes, designed to mimic the properties of the universe of U.S. macroeconomic data. Our analysis considers various identification schemes and several variants of LP and VAR estimators, employing bias correction, shrinkage, or model averaging. A clear bias–variance trade-off emerges: LP estimators have lower…

不稳定环境中的局部投影AI 解读

This paper develops a local projection estimator for estimating impulse responses in the presence of time variation. Importantly, we allow local instabilities in both slope coefficients and variances. Monte Carlo simulations illustrate that the method performs well in practice. Using our proposed estimator, we shed new light on the effects of fiscal policy shocks and the size of government spending multipliers. Our analysis uncovers the existence of instabilities that were…

状态依赖的局部投影AI 解读

Do state-dependent local projections asymptotically recover the population responses of macroeconomic aggregates to structural shocks? The answer to this question depends on how the state of the economy is determined and on the magnitude of the shocks. When the state is exogenous, the local projection estimator recovers the population response regardless of the shock size. When the state depends on macroeconomic shocks, as is common in empirical work, local projections only…

带线性约束的大型贝叶斯结构向量自回归模型AI 解读

This paper develops a Markov Chain Monte Carlo (MCMC) algorithm for Bayesian inference in large structural vector autoregressions (SVARs) with linear restrictions. Our proposed method is based on a novel parameter transformation scheme, which aims to facilitate sampling from the posterior distribution of model parameters when linear equality and inequality restrictions are imposed on contemporaneous impulse responses. A prominent feature of the proposed methodology is its…

向分数布朗运动导数的弱收敛AI 解读

It is well known that, under suitable regularity conditions, the normalized fractional process with fractional parameter d converges weakly to fractional Brownian motion (fBm) for $d>\frac {1}{2}$ . We show that, for any nonnegative integer M , derivatives of order $m=0,1,\dots ,M$ of the normalized fractional process with respect to the fractional parameter d jointly converge weakly to the corresponding derivatives of fBm. As an illustration, we apply the results to the…

含动态潜在因子的小维度 VAR 模型中的工具变量推断AI 解读

We study semiparametric inference in a small-dimensional vector autoregressive (VAR) model of order p augmented by unobservable common factors with a dynamic described by a VAR process of order q . This state-space specification is useful to measure separately the direct causality effects and the responses to dynamic common factors. We show that the state-space parameters are identifiable from the autocovariance function of the observed process. We estimate the model by…

基于因子调整向量自回归的高维时间序列分段AI 解读

Vector autoregressive (VAR) models are popularly adopted for modelling high-dimensional time series, and their piecewise extensions allow for structural changes in the data. In VAR modelling, the number of parameters grow quadratically with the dimensionality which necessitates the sparsity assumption in high dimensions. However, it is debatable whether such an assumption is adequate for handling datasets exhibiting strong serial and cross-sectional correlations. We propose…