A Nonparametric Dynamic Causal Model for Macroeconometrics

70 Pages Posted: 25 Mar 2019

Date Written: March 1, 2019

Abstract

This paper uses potential outcome time series to provide a nonparametric framework for quantifying dynamic causal effects in macroeconometrics. This provides sufficient conditions for the nonparametric identification of dynamic causal effects as well as clarify the causal content of several common assumptions and methods in macroeconomics. Our key identifying assumption is shown to be non-anticipating treatments which enables nonparametric inference on dynamic causal effects. Next, we provide a formal definition of a "shock'' and this leads to a shocked potential outcome time series. This is a nonparametric statement of the Frisch-Slutzky paradigm. The common additional assumptions that the causal effects are additive and that the treatments are shocks place substantial restrictions on the underlying dynamic causal estimands. We use this structure to causally interpret several common estimation strategies. We provide sufficient conditions under which local projections is causally interpretable and show that the standard assumptions for local projections with an instrument are not sufficient to identify dynamic causal effects. We finally show that the structural vector moving average form is causally equivalent to a restricted potential outcome time series under the usual invertibility assumption.

Keywords: Causality, nonparametric, potential outcomes, shocks, time series

JEL Classification: C22, C21, C26, E47, E37

Suggested Citation

Rambachan, Ashesh and Shephard, Neil, A Nonparametric Dynamic Causal Model for Macroeconometrics (March 1, 2019). Available at SSRN: https://ssrn.com/abstract=3345325 or http://dx.doi.org/10.2139/ssrn.3345325

Ashesh Rambachan

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Neil Shephard (Contact Author)

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
39
Abstract Views
267
PlumX Metrics