Clustered Local Projections for Time-Varying Models

TL;DR

This paper introduces a clustered local projection method for estimating impulse responses in time-varying models, effectively capturing the effects of macroeconomic and monetary policy uncertainty on Treasury yields.

Contribution

The paper develops a novel clustered local projection approach that handles endogenous and exogenous variables, improving impulse response estimation in time-varying settings.

Findings

Clustered LP accurately approximates the conditional average response.

Uncertainty amplifies risk premiums and affects market expectation adjustments.

Method successfully applied to U.S. Treasury yields post-monetary policy shocks.

Abstract

We propose a clustered local projection (clustered LP) method to estimate impulse response functions in a class of time-varying models where parameter variation is linked to a low-dimensional matrix of observables. We show that the clustered LP recovers the conditional average response when the driving variables are exogenous and a weighted average of the conditional marginal effects when they are endogenous. We propose an iterative estimation method that first classifies the data using k-means, estimates impulse response functions via GMM, and evaluates differences across clustered LP estimates. Our Monte Carlo simulations illustrate the ability of clustered LP to approximate the conditional average response function. We employ our technique to examine how uncertainty influences the transmission of a contractionary monetary policy shock to the 5- and 10-year U.S. nominal Treasury yields. Our estimation results suggest macroeconomic and monetary policy uncertainty operate through complementary but distinct channels: the former primarily amplifies the risk compensation embedded in the term premium, while the latter governs the speed and persistence with which markets revise their expectations about the future rate path following a monetary policy shock.