论文检索

用于政府债券收益率建模的广义贝叶斯时变参数VARAI 解读

Summary US yield curve dynamics are subject to time‐variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time‐varying parameters and stochastic volatility, which treats the nature of parameter dynamics as unknown. Coefficients can evolve according to a random walk, a Markov switching process, observed predictors, or depend on a mixture of these. To decide which form is supported by the data and to carry out…

结构VAR与金融网络：空间建模的最小距离方法AI 解读

Summary In this paper, I interpret a time series spatial model (T‐SAR) as a constrained structural vector autoregressive (SVAR) model. Based on these restrictions, I propose a minimum distance approach to estimate the (row‐standardized) network matrix and the overall network influence parameter of the T‐SAR from the SVAR estimates. I also develop a Wald‐type test to assess the distance between these two models. To implement the methodology, I discuss machine learning methods…

时间序列回归能告诉我们哪些政策反事实？AI 解读

We show that, in a general family of linearized structural macroeconomic models, knowledge of the empirically estimable causal effects of contemporaneous and news shocks to the prevailing policy rule is sufficient to construct counterfactuals under alternative policy rules. If the researcher is willing to postulate a loss function, our results furthermore allow her to recover an optimal policy rule for that loss. Under our assumptions, the derived counterfactuals and optimal…

贝叶斯结构向量自回归模型的新后验采样器AI 解读

We develop an importance sampler for sign restricted Bayesian structural vector autoregressive models. The algorithm nests as a special case the sampler associated with the popular Normal inverse Wishart Uniform prior, while allowing to move beyond such prior in medium sized models. We then propose a prior on contemporaneous impulse responses that provides flexibility on the magnitude and shape of the impact responses. We illustrate the quantitative relevance of the choice…

局部投影、自相关与估计效率AI 解读

It is well known that Local Projections (LP) residuals are autocorrelated. Conventional wisdom says that LP have to be estimated by OLS and that GLS is not possible because the autocorrelation process is unknown and/or because the GLS estimator would be inconsistent. I show that the autocorrelation process of LP can be written as a Vector Moving Average (VMA) process of the Wold errors and impulse responses and that autocorrelation can be corrected for using a consistent GLS…

供需对油价的重要性：来自非高斯性的证据AI 解读

When quantifying the importance of supply and demand for oil price fluctuations, a wide range of estimates have been reported. Models identified via a sharp upper bound on the short‐run price elasticity of supply find supply shocks to be minor drivers. In turn, when replacing the upper bound with a weakly informative prior, supply shocks turn out to be substantially more important. In this paper, I revisit the evidence in a model that combines weakly informative priors with…

高维关联度表估计：从部门溢出理解大缓和AI 解读

We estimate sectoral spillovers around the Great Moderation with the help of forecast error variance decomposition tables. Obtaining such tables in high dimensions is challenging because they are functions of the estimated vector autoregressive coefficients and the residual covariance matrix. In a simulation study, we compare various regularization methods on both and conduct a comprehensive analysis of their performance. We show that standard estimators of large…

货币政策、外部工具变量与异方差AI 解读

We develop a structural vector autoregressive framework that combines external instruments and heteroskedasticity for identification of monetary policy shocks. We show that exploiting both types of information sharpens structural inference, allows testing the relevance and exogeneity condition for instruments separately using likelihood ratio tests, and facilitates the economic interpretation of the structural shock of interest. We test alternative instruments and find that…

具有偶尔约束生效机制的结构VARAI 解读

We develop a structural VAR in which an occasionally-binding constraint generates censoring of one of the dependent variables. Once the censoring mechanism is triggered, we allow some of the coefficients for the remaining variables to change. We show that a necessary condition for a unique reduced form is that regression functions for the non-censored variables are continuous at the censoring point and that parameters satisfy some mild restrictions. In our application the…

使用大型贝叶斯向量自回归进行即时预测AI 解读

Monitoring economic conditions in real time, or nowcasting, and Big Data analytics share some challenges, sometimes called the three “Vs”. Indeed, nowcasting is characterized by the use of a large number of time series (Volume), the complexity of the data covering various sectors of the economy, with different frequencies and precision and asynchronous release dates (Variety), and the need to incorporate new information continuously and in a timely manner (Velocity). In this…

持续性预测回归中的参数不稳定与结构变化检验AI 解读

This paper develops parameter instability and structural change tests within predictive regressions for economic systems governed by persistent vector autoregressive dynamics. Specifically, in a setting where all – or a subset – of the variables may be fractionally integrated and the predictive relation may feature cointegration, we provide sup-Wald break tests that are constructed using the Local speCtruM (LCM) approach. The new tests cover both parameter variation and…

VAR 模型中脉冲响应的联合贝叶斯推断AI 解读

We derive the Bayes estimator of vectors of structural VAR impulse responses under a range of alternative loss functions. We also discuss the construction of joint credible regions for vectors of impulse responses as the lowest posterior risk region under the same loss functions. We show that conventional impulse response estimators such as the posterior median response function or the posterior mean response function are not in general the Bayes estimator of the impulse…

什么推动了美国国债收益率变动？AI 解读

We identify a yield news shock as an innovation that does not move Treasury yields contemporaneously but explains a maximum share of their future variation. Yields do not immediately respond to the news shock as the initial reaction of term premiums and expected short rates offset each other. While the impact on term premiums fades quickly, expected short rates and thus yields decline persistently. As a result, the shock explains a staggering 50% of Treasury yield variation…

收益预测回归的一致局部谱推断AI 解读

This paper studies the properties of predictive regressions for asset returns in economic systems governed by persistent vector autoregressive dynamics. In particular, we allow for the state variables to be fractionally integrated, potentially of different orders, and for the returns to have a latent persistent conditional mean, whose memory is difficult to estimate consistently by standard techniques in finite samples. Moreover, the predictors may be endogenous and…

叙事限制与代理变量AI 解读

We compare two approaches to using information about the signs of structural shocks at specific dates within a structural vector autoregression (SVAR): imposing "narrative restrictions" (NR) on the shock signs in an otherwise set-identified SVAR; and casting the information about the shock signs as a discrete-valued "narrative proxy" (NP) to point-identify the impulse responses. The NP is likely to be "weak" given that the sign of the shock is typically known in a small…

评 Giacomini、Kitagawa 与 Read 的《叙事限制与代理变量》AI 解读

In a series of recent studies, Raffaella Giacomini and Toru Kitagawa have developed an innovative methodological approach to estimating sign-identified structural VAR models that seeks to build a bridge between Bayesian and frequentist approaches in the literature. Their latest papers with Matthew Read contain thought-provoking new insights about modeling narrative restrictions in sign-identified structural VAR models. My discussion puts their contribution into the context…

对《叙事限制与代理变量》的讨论AI 解读

I am grateful for the chance to discuss this characteristically insightful paper by Giacomini, Kitagawa, and Read (hence-forth GKR). Since the seminal contribution of Antolín-Díaz and Rubio-Ramírez (2018), narrative restrictions have rapidly become one of the go-to tools for sharpening causal inference in SVAR analysis. Giacomini, Kitagawa, and Read (2021) con-tributed greatly to our understanding of the role of subjective prior beliefs and the appropriate form of the…

代理变量 SVAR 中渐近有效的自助法推断AI 解读

Proxy structural vector autoregressions identify structural shocks in vector autoregressions with external variables that are correlated with the structural shocks of interest but uncorrelated with all other structural shocks. We provide asymptotic theory for this identification approach under mild α-mixing conditions that cover a large class of uncorrelated, but possibly dependent innovation processes. We prove consistency of a residual-based moving block bootstrap (MBB)…

对潜在非平稳性稳健的结构 VAR 工具变量估计AI 解读

This paper considers the estimation of dynamic causal effects using a proxy structural vector-autoregressive model with possibly nonstationary regressors. We provide general conditions under which the asymptotic normal approximation remains valid. In this case, the asymptotic variance depends on the persistence property of each series. We further provide a consistent asymptotic covariance matrix estimator that requires neither knowledge of the presistence properties of the…

Summary I develop algorithms to facilitate Bayesian inference in structural vector autoregressions that are set-identified with sign and zero restrictions by showing that the system of restrictions is equivalent to a system of sign restrictions in a lower-dimensional space. Consequently, algorithms applicable under sign restrictions can be extended to allow for zero restrictions. Specifically, I extend algorithms proposed in Amir-Ahmadi and Drautzburg (2021) to check whether…

VAR 分布误设下渐近有效的贝叶斯推断AI 解读

Inaccurately imposing Gaussian distributional assumptions in standard multivariate time series models does not affect inference on the autoregressive coefficients but distorts both classical and Bayesian inference on the volatility matrix whenever the true error distribution has excess kurtosis relative to the multivariate normal density. Inference on the intercept is also affected whenever the innovations are generated from a non-symmetric distribution. As a result of…

多国环境下的宏观经济预测AI 解读

Abstract In this paper, we propose a hierarchical shrinkage approach for multi‐country VAR models. In implementation, we consider three different scale mixtures Normals priors and provide new theoretical results. Empirically, we examine how model specifications and prior choices affect the forecasting performance for GDP growth, inflation, and a short‐term interest rate for the G7 economies. We find that hierarchical shrinkage, particularly as implemented with the Horseshoe…

用于估计时间平均协方差矩阵的翼帆型滞后窗AI 解读

Summary Lag windows are commonly used in time series analysis, econometrics, steady-state simulation and Markov chain Monte Carlo to estimate time-average covariance matrices. In the presence of positive correlation in the underlying process, estimators of this matrix almost always exhibit significant negative bias, leading to undesirable finite-sample properties. We propose a new family of lag windows specifically designed to improve finite-sample performance by offsetting…

动态方差分解的工具变量识别AI 解读

Macroeconomists increasingly use external sources of exogenous variation for causal inference. However, unless such external instruments (proxies) capture the underlying shock without measurement error, existing methods are silent on the importance of that shock for macroeconomic fluctuations. We show that, in a general moving-average model with external instruments, variance decompositions for the instrumented shock are interval-identified, with informative bounds. Various…