We consider impulse response inference in a locally misspecified stationary vector autoregression (VAR) model. The conventional local projection (LP) confidence interval has correct coverage even when the misspecification is so large that it can be detected with probability approaching 1. This...

We study the relation between inflation and real activity over the business cycle. We employ a Trend-Cycle VAR model to control for low-frequency movements in inflation, unemployment, and growth that are pervasive in the post-WWII period. We show that cyclical fluctuations of inflation are...

Most macroeconomic models view economic outcomes as being generated by a combination of endogenous and exogenous dynamic forces. In particular, the exogenous forces are generally modelled as a set of independent dynamics processes. In this paper we begin by showing that this dual dynamic...

We conduct a simulation study of Local Projection (LP) and Vector Autoregression (VAR) estimators of structural impulse responses across thousands of data generating processes, designed to mimic the properties of the universe of U.S. macroeconomic data. Our analysis considers various...

In this paper we use the functional vector autoregression (VAR) framework of Chang, Chen, and Schorfheide (2024) to study the effects of monetary policy shocks (conventional and informational) on the cross-sectional distribution of U.S. earnings (from the Current Population Survey), consumption,...

Benchmark finance and macroeconomic models appear to deliver conflicting estimates of the natural rate and bond risk premia. This natural rate puzzle applies not only in the U.S. but across many advanced economies. We use a unified no-arbitrage macro- finance model with two trend factors to...

This paper combines new data and a narrative approach to identify shocks to political pressure on the Federal Reserve. From archival records, I build a data set of personal interactions between U.S. Presidents and Fed officials between 1933 and 2016. Since personal interactions do not...

The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the...

We develop a method to identify the individual latent propensity to select into treatment and marginal treatment effects. Identification is achieved with survey data on individuals' subjective expectations of their treatment propensity and of their treatment-contingent outcomes. We use the...

We propose a new tool to filter non-linear dynamic models that does not require the researcher to specify the model fully and can be implemented without solving the model. If two conditions are satisfied, we can use a flexible statistical model and a known measurement equation to back out the...