Interest Rates and Money in the Measurement of Monetary Policy
货币政策测度中的利率与货币AI 解读 ↓
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货币政策测度中的利率与货币AI 解读 ↓
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Over the last twenty-five years, a set of influential studies has placed interest rates at the heart of analyses that interpret and evaluate monetary policies.In light of this work, the Federal Reserve's recent policy of "quantitative easing," with its goal of affecting the supply of liquid assets, appears to be a radical break from standard practice.Alternatively, one could posit that the monetary aggregates, when measured properly, never lost their ability to explain aggregate fluctuations and, for this reason, represent an important omission from standard models and policy discussions.In this context, the new policy initiatives can be characterized simply as conventional attempts to increase money growth.This view is supported by evidence that superlative (Divisia) measures of money often help in forecasting movements in key macroeconomic variables.Moreover, the statistical fit of a structural vector autoregression deteriorates significantly if such measures of money are excluded when identifying monetary policy shocks.These results cast doubt on the adequacy of conventional models that focus on interest rates alone.They also highlight that all monetary disturbances have an important "quantitative" component, which is captured by movements in a properly measured monetary aggregate.
We tackle the issue of producing point, sign, and density forecasts for the monthly real price of carbon in the European Union Emissions Trading System (EU ETS). We show that Bayesian Vector Autoregressive (BVAR) models augmented with factors based on macroeconomic and financial variables yield improvements in point and density forecasting performance, with the largest point-forecast gains emerging at intermediate and longer horizons. In particular, the one-factor BVAR model…
Abstract This paper shows how the structural representation of a vector autoregressive model can support forecast analysis. We offer a unified framework that formalizes how the structural form of the model can help form a narrative for two key statistics in real-time VAR forecasting: the forecast errors at the outturn of the data, and the consequent revisions of the forecast. We conduct an exercise on the UK, focusing on the inflation surge that started in 2022. We show that…
具有 Cholesky 随机波动率的大型 VAR:贝叶斯推断、在线预测与模型选择
We consider $K$-dimensional Bayesian vector autoregressions (BVARs) with Cholesky stochastic volatility (SV), in which the innovation covariance matrix is a lower-triangular linear transform of $K$ independent univariate SV processes. Such models are widely used in empirical macroeconomics to capture time-varying uncertainty and improve forecast accuracy, but the cost of posterior simulation is the binding constraint on the size of the system, and a major bottleneck for…
面向二元、删失与计数变量的大型贝叶斯VAR
We extend the vector autoregression (VAR) to model binary, censored, count and unrestricted variables jointly, treating missing observations of any type in the same way. Each restricted observation is linked to a latent Gaussian variable, so that the standard machinery for VARs, such as shrinkage priors, stochastic volatility and outlier components, applies directly to the VAR for the latent variables. The latent variables have a truncated normal conditional distribution…
VAR 预测与脉冲响应的误设稳健收缩及选择
Vector autoregressions (VARs) are vulnerable to dynamic misspecification, forcing researchers to navigate complex bias-variance trade-offs when forecasting or estimating impulse response functions (IRFs). We derive novel, task-specific selection criteria -- PC for forecasting and IRFC for IRF estimation -- based on asymptotically unbiased estimates of frequentist risk under local dynamic misspecification. These criteria give empirical researchers a fully data-driven and…
多层网络向量自回归的时变模型平均
In this paper, we introduce a flexible time-varying multi-layer network vector autoregression (VAR) model framework for large-scale time series, allowing agents in dynamic systems to interact through multiple channels and incorporating multiple adjacency matrices to capture network spillover effects. We propose a penalized model averaging method to determine a time-varying optimal combination of multi-layer network VAR candidate models whose number may be divergent. Under…