Research
-
Distributional Dynamics
We develop a new method for deriving high-frequency synthetic distributions of consumption, income, and wealth. Modern theories of macroeconomic dynamics identify the joint distribution of consumption, income, and wealth as a key determinant of aggregate dynamics. Our novel method allows us to study their distributional dynamics over time. The method can incorporate different microdata sources, regardless of their frequency and coverage of variables, to generate high-frequency synthetic distributional data. We extend existing methods by allowing for more flexible data inputs. The core of the method is to treat the distributional data as a time series of functions whose underlying factor structure follows a state-space model, which we estimate using Bayesian techniques. We show that the novel method provides the high-frequency distributional data needed to understand better the dynamics of consumption and its distribution over the business cycle.
-
Not All Oil Supply Shocks are Alike: The Macroeconomic and Distributional Impact of Oil Supply Shocks
Oil supply shocks shape U.S. inequality, and the type of oil supply shock matters for who bears the cost. In a Bayesian VAR that embeds the joint distribution of U.S. household income, consumption, and wealth alongside a standard oil-market block, sudden production shortfalls (Baumeister and Hamilton, 2019) raise inequality, whereas oil supply news (Känzig, 2021) mostly reduces it. These differences are not visible in the aggregate dynamics, which appear similar across shocks. A causal mediation analysis (Dufour and Wang, 2024) of these aggregate dynamics shows these shocks operate through distinct channels and it is this mechanism wedge, rather than the aggregate footprint, that determines who bears the cost. The analysis shares the view of Kilian and Lewis (2011) that policy responses should depend on the underlying causes of oil price shocks.
-
Oil Supply Shocks and Monetary Policy
I revisit the role of monetary policy in the transmission of oil supply shocks within a single Bayesian VAR that jointly identifies a Känzig (2021) oil supply news shock and six orthogonal monetary policy shocks. I combine the causal mediation framework of Dufour and Wang (2024), to recover what the Federal Reserve's reaction function loads on, with the sufficient-statistics counterfactual of Caravello, McKay and Wolf (2024), to evaluate three alternative rules: a nominal rate peg, strict CPI-inflation targeting, and an optimal dual-mandate rule. Four findings emerge. (i) Aggregate responses are largely insensitive to the rule at short constraint horizons, with the Fed reacting primarily to inflation, the oil price, and demand-side mediators, and negligibly to the oil-market block. (ii) Policy duration is what discriminates the rules at the macro level: held for only a few quarters the three rules look near-identical, but enforced over the full twenty-quarter horizon the rate peg and dual-mandate converge to one another and deliver materially better output, inflation, and unemployment stability than the realized path. (iii) Distributional responses discriminate even at short horizons: the dual-mandate rule tracks the realized Gini paths, while the rate peg and strict inflation targeting both compress income, wealth, and consumption inequality, with strict-π amplifying the realized compression by roughly twenty per cent for income, two-and-a-half-fold for consumption, and threefold for wealth. (iv) The strict-π compression is driven by redistribution toward the bottom of the distribution rather than top-tail destruction. The cross-section, and the duration over which a rule is held, thus discriminate between rules that headline macro aggregates at short horizons cannot.
-
Spatial standard errors for several commonly used M-estimators
We provide a unified implementation of existing asymptotic theory that computes Conley-style spatial HAC standard errors for a wide range of commonly used (non-linear) estimators. We cover OLS, logit, probit, Poisson, and negative binomial regressions, as well as the fixed-effects estimators areg and reghdfe—extending commonly used publicly available routines (currently limited to linear models) to nonlinear M-estimators and fixed-effects workflows. We provide Stata and Python software implementing the procedure.
- Monetary Policy According to Data
- Job Levels II