Search Results
Working Paper
Revealing Cluster Structures Based on Mixed Sampling Frequencies
This paper proposes a new nonparametric mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel regression with mixed frequency data. The nonparametric MIDAS estimation method is more flexible and substantially simpler to implement than competing approaches. We show that the proposed clustering algorithm successfully recovers true membership in the cross-section, both in theory and in simulations, without requiring prior knowledge of the number of clusters. This methodology is applied to a mixed-frequency Okun's law model for state-level data in the U.S. and ...
Working Paper
Local Projections
A central question in applied research is to estimate the effect of an exogenous intervention or shock on an outcome. The intervention can affect the outcome and controls on impact and over time. Moreover, there can be subsequent feedback between outcomes, controls and the intervention. Many of these interactions can be untangled using local projections. This method’s simplicity makes it a convenient and versatile tool in the empiricist’s kit, one that is generalizable to complex settings. This article reviews the state-of-the art for the practitioner, discusses best practices and ...
Working Paper
Foreign Effects of Higher U.S. Interest Rates
This paper analyzes the spillovers of higher U.S. interest rates on economic activity in a large panel of 50 advanced and emerging economies. We allow the response of GDP in each country to vary according to its exchange rate regime, trade openness, and a vulnerability index that includes current account, foreign reserves, inflation, and external debt. We document large heterogeneity in the response of advanced and emerging economies to U.S. interest rate surprises. In response to a U.S. monetary tightening, GDP in foreign economies drops about as much as it does in the United States, with a ...
Working Paper
A Composite Likelihood Approach for Dynamic Structural Models
We describe how to use the composite likelihood to ameliorate estimation, computational, and inferential problems in dynamic stochastic general equilibrium models. We present a number of situations where the methodology has the potential to resolve well-known problems. In each case we consider, we provide an example to illustrate how the approach works and its properties in practice.
Working Paper
Breaks in the Phillips Curve: Evidence from Panel Data
We revisit time-variation in the Phillips curve, applying new Bayesian panel methods with breakpoints to US and European Union disaggregate data. Our approach allows us to accurately estimate both the number and timing of breaks in the Phillips curve. It further allows us to determine the existence of clusters of industries, cities, or countries whose Phillips curves display similar patterns of instability and to examine lead-lag patterns in how individual inflation series change. We find evidence of a marked flattening in the Phillips curves for US sectoral data and among EU countries, ...
Working Paper
Analysis of Multiple Long Run Relations in Panel Data Models with Applications to Financial Ratios
This paper provides a new methodology for the analysis of multiple long-run relations in panel data models where the cross-section dimension, n, is large relative to the time-series dimension, T. For panel data models with large n, researchers have focused on panels with a single long-run relationship. The main difficulty has been to eliminate short-run dynamics without generating significant uncertainty for identification of the long run. We overcome this problem by using non-overlapping sub-sample time averages as deviations from their full-sample counterpart and estimating the number of ...
Report
Micro Responses to Macro Shocks
We study panel data regression models when the shocks of interest are aggregate and possibly small relative to idiosyncratic noise. This speaks to a large empirical literature that targets impulse responses via panel local projections. We show how to interpret the estimated coefficients when units have heterogeneous responses and how to obtain valid standard errors and confidence intervals. A simple recipe leads to robust inference: including lags as controls and then clustering at the time level. This strategy is valid under general error dynamics and uniformly over the degree of ...
Working Paper
Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels
This paper develops Mean Group Distributed Lag (MGDL) estimation of impulse responses of common shocks in large panels with one or two cross-section dimensions. We derive sufficient conditions for asymptotic normality, and document satisfactory small sample performance using Monte Carlo experiments. Three empirical illustrations showcase the usefulness of MGDL estimators: crude oil price pass-through to U.S. city- and product-level retail prices; retail price effects of U.S. monetary policy shocks; and house price effects of U.S. monetary policy shocks.
Working Paper
Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels
This paper develops Mean Group Distributed Lag (MGDL) estimation of impulse responses in large panels with one or two cross-section dimensions. Sufficient conditions for asymptotic consistency and asymptotic normality are derived, and satisfactory small sample performance is documented using Monte Carlo experiments. MGDL estimators are used to estimate the effects of crude oil price increases on U.S. city- and product-level retail prices.