IMF Working Papers

Assessing the Impact of Policy Changes on a Nowcast

By Sam Ouliaris, Celine Rochon

July 28, 2023

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Sam Ouliaris, and Celine Rochon. Assessing the Impact of Policy Changes on a Nowcast, (USA: International Monetary Fund, 2023) accessed November 6, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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.

Subject: Econometric analysis, Economic forecasting, Oil prices, Prices, Structural vector autoregression, Vector autoregression

Keywords: Baseline Nowcasting regression, Caribbean, Frequency data, Global, High frequency indicators, Impulse responses, Nowcasting, Oil prices, Real GDP of Dominica, Structural models, Structural vector autoregression, USA real GDP, Vector autoregression, Year-on-year percentage

Publication Details

  • Pages:

    18

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2023/153

  • Stock No:

    WPIEA2023153

  • ISBN:

    9798400249716

  • ISSN:

    1018-5941