Macroeconomists have long been concerned with the causal effects of monetary policy. When the identification of causal effects is based on a selection-on-observables assumption, non-causality amounts to the conditional independence of outcomes and policy changes. This paper develops a...

Time series data are widely used to explore causal relationships, typically in a regression framework with lagged dependent variables. Regression-based causality tests rely on an array of functional form and distributional assumptions for valid causal inference. This paper develops a...

We develop a flexible semiparametric time series estimator that is then used to assess the causal effect of monetary policy interventions on macroeconomic aggregates. Our estimator captures the average causal response to discrete policy interventions in a macro-dynamic setting, without the need...

We develop semiparametric tests for conditional independence in time series models of causal effects. Our approach is motivated by empirical studies of monetary policy effects and is semiparametric in the sense that we model the process determining the distribution of treatment—the policy...