On the Properties of Various Estimators for Fiscal Reaction Functions [electronic resource] / Oya Celasun.
Material type: TextSeries: IMF Working Papers; Working Paper ; No. 06/182Publication details: Washington, D.C. : International Monetary Fund, 2006Description: 1 online resource (29 p.)ISBN: 1451864426 :ISSN: 1018-5941Subject(s): Dynamic Models | Equation | Equations | Fiscal Reaction Functions | Fiscal Reaction | National Deficit Surplus | Brazil | United KingdomAdditional physical formats: Print Version:: On the Properties of Various Estimators for Fiscal Reaction FunctionsOnline resources: IMF e-Library | IMF Book Store Abstract: This paper evaluates the bias of the least-squares-with-dummy-variables (LSDV) method in fiscal reaction function estimations. A growing number of studies estimate fiscal policy reaction functions-that is, relationships between the primary fiscal balance and its determinants, including public debt and the output gap. A previously unexplored methodological issue in these estimations is that lagged debt is not a strictly exogenous variable, which biases the LSDV estimator in short panels. We derive the bias analytically to understand its determinants and run Monte Carlo simulations to assess its likely size in empirical work. We find the bias to be smaller than the bias of the LSDV estimator in a comparable autoregressive dynamic panel model and show the LSDV method to outperform a number of alternatives in estimating fiscal reaction functions.This paper evaluates the bias of the least-squares-with-dummy-variables (LSDV) method in fiscal reaction function estimations. A growing number of studies estimate fiscal policy reaction functions-that is, relationships between the primary fiscal balance and its determinants, including public debt and the output gap. A previously unexplored methodological issue in these estimations is that lagged debt is not a strictly exogenous variable, which biases the LSDV estimator in short panels. We derive the bias analytically to understand its determinants and run Monte Carlo simulations to assess its likely size in empirical work. We find the bias to be smaller than the bias of the LSDV estimator in a comparable autoregressive dynamic panel model and show the LSDV method to outperform a number of alternatives in estimating fiscal reaction functions.
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