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用于宏观经济分析的贝叶斯神经网络AI 解读

Macroeconomic data is characterized by a limited number of observations (small T ), many time series (big K ) but also by featuring temporal dependence. Neural networks, by contrast, are designed for datasets with millions of observations and covariates. In this paper, we develop Bayesian neural networks (BNNs) that are well-suited for handling datasets commonly used for macroeconomic analysis in policy institutions. Our approach avoids extensive specification searches…

Abstract This paper studies the estimation and inference of time-varying impulse response functions in structural vector autoregressions (SVARs) identified with external instruments. Building on kernel estimators that allow for nonparametric time variation, we derive the asymptotic distributions of the relevant quantities. Our estimators are simple and computationally trivial and allow for potentially weak instruments. Simulations suggest satisfactory empirical coverage even…

高斯过程向量自回归与宏观经济不确定性AI 解读

We develop a nonparametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values.The main building block of our model is a Gaussian process prior on the functional relationship that determines the conditional mean of the model, hence, the name of Gaussian process vector autoregression (GP-VAR).A flexible stochastic volatility specification is used to provide…

宏观实证中分位数回归的模型设定选择AI 解读

ABSTRACT Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks. This paper examines various choices in the specification of quantile regressions for macro applications, including how and to what extent to include shrinkage and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, measured with quantile scores and quantile‐weighted continuous ranked…

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…

利用多国非参数分位数因子模型研究增长风险AI 解读

We develop a nonparametric quantile panel regression model.Within each quantile, the quantile function is a combination of linear and nonlinear parts, which we approximate using Bayesian Additive Regression Trees (BART).Cross-sectional information is captured through a conditionally heteroscedastic latent factor.The nonparametric feature enhances flexibility, while the panel feature increases the number of observations in the tails.We develop Bayesian methods for inference…

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…

以周频率即时预测经济活动的尾部风险AI 解读

Summary This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information used to produce nowcasts on a weekly basis. We consider Bayesian mixed frequency regressions with stochastic volatility and Bayesian quantile regressions. Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward within a quarter, making…

通胀波动的全球成分AI 解读

Summary Global developments play an important role for domestic inflation rates. Earlier literature has found that a substantial amount of the variation in a large set of national inflation rates can be explained by a single global factor. However, inflation volatility has been typically neglected, although it is clearly relevant both from a policy point of view and for structural analysis and forecasting. We study the evolution of inflation rates in several countries, using…

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…

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…

无套利先验、时变波动率与利率期限结构AI 解读

Abstract We use a Bayesian vector autoregression with stochastic volatility to forecast government bond yields. We form the conjugate prior from a no‐arbitrage affine term structure model. The model improves on the accuracy of point and density forecasts from a no‐change random walk and an affine term structure model with stochastic volatility. Our proposed approach may succeed by relaxing the no‐arbitrage affine term structure model's requirements that yields obey a factor…

马尔可夫切换三遍回归滤波器AI 解读

We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-switching three-pass regression filter (MS-3PRF), is suitable for datasets with large cross-sectional dimensions, since estimation and inference are straightforward, as opposed to existing…

大型时变参数VAR：一种非参数方法AI 解读

Summary In this paper we introduce a nonparametric estimation method for a large Vector Autoregression (VAR) with time‐varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs. When applied to the problem of forecasting key macroeconomic variables, the method…

含随机波动率与非共轭先验的大型贝叶斯向量自回归AI 解读

Recent research has shown that a reliable vector autoregression (VAR) for forecasting and structural analysis of macroeconomic data requires a large set of variables and modeling time variation in their volatilities. Yet, there are no papers that provide a general solution for combining these features, due to computational complexity. Moreover, homoskedastic Bayesian VARs for large data sets so far restrict substantially the allowed prior distributions on the parameters. In…

含移动平均成分的混频模型AI 解读

Summary Temporal aggregation in general introduces a moving‐average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed‐frequency (MF) model. The MA component is generally neglected, likely to preserve the possibility of ordinary least squares estimation, but the consequences have never been properly studied in the MF context. In this paper we show, analytically, in Monte Carlo…

结构 FECM：大规模结构 FAVAR 模型中的协整AI 解读

Summary Starting from the dynamic factor model for nonstationary data we derive the factor‐augmented error correction model (FECM) and its moving‐average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long‐run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by…

危机以来，标准 VAR 是否保持稳定？AI 解读

Summary Small vector autoregressions are commonly used in macroeconomics for forecasting and evaluating shock transmission. This requires VAR parameters to be stable over the evaluation and forecast sample or modeled as time‐varying. Prior work has considered whether there were sizable parameter changes in the early 1980s and in the subsequent period until the beginning of the new century. This paper conducts a similar analysis focused on the period since the recent crisis.…

部门与地区冲击对价格设定的重要性AI 解读

Summary We use novel disaggregate sectoral‐regional euro‐area data to investigate the sources of price changes, introducing a new method to extract factors from overlapping data blocks that allows for estimation of aggregate, sectoral, country‐specific and regional components of price changes. Our sectoral component explains much less variation in disaggregate inflation rates and exhibits much less volatility and more persistence than previous findings for the US indicate.…

货币、财政与石油冲击：混频结构 FAVAR 的证据AI 解读

Large scale factor models have been often adopted both for forecasting and to identify structural shocks and their transmission mechanism. Mixed frequency factor models have been also used in a reduced form context, but not for structural applications, and in this paper we close this gap. First, we adapt a simple technique developed in a small scale mixed frequency VAR and factor context to the large scale case, and compare the resulting model with existing alternatives.…

工具变量回归中基于因子的识别稳健推断AI 解读

Summary Robust methods for instrumental variable inference have received considerable attention recently. Their analysis has raised a variety of problematic issues such as size/power trade‐offs resulting from weak or many instruments. We show that information reduction methods provide a useful and practical solution to this and related problems. Formally, we propose factor‐based modifications to three popular weak‐instrument‐robust statistics, and illustrate their validity…

大型贝叶斯 VAR 中的共同时变波动率AI 解读

The general pattern of estimated volatilities of macroeconomic and financial variables is often broadly similar. We propose two models in which conditional volatilities feature comovement and study them using U.S. macroeconomic data. The first model specifies the conditional volatilities as driven by a single common unobserved factor, plus an idiosyncratic component. We label this model BVAR with general factor stochastic volatility (BVAR-GFSV) and we show that the loss in…

多元自回归指数模型的结构分析AI 解读

We address the issue of parameter dimensionality reduction in Vector Autoregressive models (VARs) for many variables by imposing specific reduced rank restrictions on the coefficient matrices that simplify the VARs into Multivariate Autoregressive Index (MAI) models. We derive the Wold representation implied by the MAIs and show that it is closely related to that associated with dynamic factor models. Then, the theoretical analysis is extended to the case of general rank…

In this article, we develop a mixed frequency dynamic factor model in which the disturbances of both the latent common factor and of the idiosyncratic components have time-varying stochastic volatilities. We use the model to investigate business cycle dynamics in the euro area and present three sets of empirical results. First, we evaluate the impact of macroeconomic releases on point and density forecast accuracy and on the width of forecast intervals. Second, we show how…