Testing for the footprints of stabilization economic policy in forecast errors

Area of Science:

• Econometrics
• Financial Modeling
• Policy Analysis

Background:

• Traditional policy impact analysis often lacks complete data.
• Assessing stabilization policies requires robust statistical methods.
• Dynamic financial models generate forecast errors that can reveal policy footprints.

Purpose of the Study:

• Introduce the Policy Effects Lagrange Multiplier (PELM) test.
• Detect stabilization policy effects from forecast error distributions.
• Evaluate policy efficiency without explicit intervention data.

Main Methods:

• Developed the novel Policy Effects Lagrange Multiplier (PELM) test.
• Applied the PELM test to sovereign bond yield data from 33 countries.
• Conducted robustness checks across various model specifications.

Main Results:

• The PELM test identified stabilization policy footprints in several countries.
• Countries with confirmed stabilization policies showed significant budgetary improvements post-2014 crisis.
• The test demonstrated predictive properties and validated findings through robustness checks.

Conclusions:

• The PELM test is a valuable tool for evaluating stabilization policies.
• The test facilitates better forecasting and assessment of policy efficiency.
• This method overcomes limitations of traditional policy impact analyses due to data constraints.