Abstract

http://ssrn.com/abstract=1348680
 
 

References (15)



 


 



On the Identification of Fiscal Policy Behavior


Bing Li


Indiana University Bloomington - Department of Economics

March 9, 2009

CAEPR Working Paper No. 026-2008

Abstract:     
In the current literature, fiscal policy is usually characterized by a single-equation rule, in which primary surplus is generally defined as a function of lagged government debt and other controlled variables. To apply Ordinary Least Squares (OLS) method on the single-equation rule has been one of the common approaches to identify fiscal policy behavior. From the rational expectations general equilibrium perspective, this paper illustrates that lagged government debt is generally endogenous and the OLS approach suffers from simultaneity bias. Consequently, the OLS-based identification of fiscal policy behavior is unreliable. As a solution, we apply the Generalized Method of Moments (GMM) for estimation and inference. Monte Carlo experiments demonstrate that GMM provides more reliable results than OLS in terms of accuracy of the estimator, size and power. In short, people should be cautious of the existing OLS-based identification results of fiscal policy behavior and the empirical researchers should not consider OLS regression as a reliable tool when trying to identify fiscal policy behavior in the future.

Number of Pages in PDF File: 39

Keywords: Fiscal Policy Rule, Non-Ricardian, Ricardian, Simultaneity Bias, OLS, GMM, Size, Power

JEL Classification: C12, C13, E63

working papers series


Download This Paper

Date posted: February 26, 2009 ; Last revised: March 25, 2009

Suggested Citation

Li, Bing, On the Identification of Fiscal Policy Behavior (March 9, 2009). CAEPR Working Paper No. 026-2008. Available at SSRN: http://ssrn.com/abstract=1348680 or http://dx.doi.org/10.2139/ssrn.1348680

Contact Information

Bing Li (Contact Author)
Indiana University Bloomington - Department of Economics ( email )
Bloomington, IN 47405-6620
United States
Feedback to SSRN


Paper statistics
Abstract Views: 573
Downloads: 110
Download Rank: 145,077
References:  15

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo3 in 0.468 seconds