| Summary: | Nowcasting enables policymakers to obtain forecasts of key macroeconomic indicators using higher frequency data, resulting in more timely information to guide proposed policy changes. A significant shortcoming of nowcasting estimators is their “reduced-form” nature, which means they cannot be used to assess the impact of policy changes, for example, on the baseline nowcast of real GDP. This paper outlines two separate methodologies to address this problem. The first is a partial equilibrium approach that uses an existing baseline nowcasting regression and single-equation forecasting models for the high-frequency data in that regression. The second approach uses a non-parametric structural VAR estimator recently introduced in Ouliaris and Pagan (2022) that imposes minimal identifying restrictions on the data to estimate the impact of structural shocks. Each approach is illustrated using a country-specific example
|
|---|