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Oliver de Groot

27 December 2012
WORKING PAPER SERIES - No. 1503
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Abstract
Do capital markets impose fiscal discipline on governments? We investigate the responses of fiscal variables to a change in the interest rate paid by governments on their debt in a panel of 14 European countries over four decades. This is done in the context of a panel vector autoregressive (PVAR) model, using sign restrictions via the penalty function method of Mountford and Uhlig (2009) to identify structural cost of borrowing shocks. Our baseline estimation shows that a one percentage point rise in the cost of borrowing leads to a cumulative improvement of the primary balance-to-GDP ratio of approximately 1.9 percentage points over 10 years, with the fiscal response becoming significantly evident only two years after the shock. We also find that the bulk of fiscal adjustment takes place via a rise in government revenue rather than a cut in primary expenditure. The size of the total fiscal adjustment, however, is insufficient to avoid the gross government debt-to-GDP ratio from rising as a consequence of the shock. Sub-dividing our sample, we also find that for countries participating in Economic and Monetary Union (EMU) the primary balance response to a cost of borrowing shock was stronger in the period after 1992 (the year in which the Maastricht Treaty was signed) than prior to 1992.
JEL Code
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects
E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy
H60 : Public Economics→National Budget, Deficit, and Debt→General
24 June 2020
WORKING PAPER SERIES - No. 2426
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Abstract
This paper develops a simple, consistent methodology for generating empirically realistic forward guidance simulations using existing macroeconomic models by modifying expectations about policy announcements. The main advantage of our method lies in the exact preservation of all other shock transmissions. We describe four scenarios regarding how agents incorporate information about future interest rate announcements: “inattention”, “credibility”, “finite planning horizon”, and “learning”. The methodology consists of describing a single loading matrix that augments the equilibrium decision rules and can be applied to any standard DSGE, including large-scale policy-institution models. Finally, we provide conditions under which the forward guidance puzzle is resolved.
JEL Code
C63 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computational Techniques, Simulation Modeling
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
27 November 2020
WORKING PAPER SERIES - No. 2495
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Abstract
We estimate the effects of interest rate forward guidance (FG) using a parsimonious VAR, augmented with survey forecast data. The identification strategy of FG shocks via sign and zero restrictions is successfully tested by the recovery of true IRFs from simulated data. The identified shocks from the VAR suggest that FG has a stronger effect on macro variables and deviations are more instantaneous compared to the hump-shaped response following unanticipated changes in monetary policy. We apply this evidence to calibrate free parameters of an otherwise estimated DSGE model in order to dampen the FG Puzzle.
JEL Code
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
19 May 2021
WORKING PAPER SERIES - No. 2555
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Abstract
This paper presents a toolkit for generating optimal policy projections. It makes five contributions. First, the toolkit requires a minimal set of inputs: only a baseline projection for target and instrument variables and impulse responses of those variables to policy shocks. Second, it solves optimal policy projections under commitment, limited-time commitment, and discretion. Third, it handles multiple policy instruments. Fourth, it handles multiple constraints on policy instruments such as a lower bound on the policy rate and an upper bound on asset purchases. Fifth, it allows alternative approaches to address the forward guidance puzzle. The toolkit that accompanies this paper is Dynare compatible, which facilitates its use. Examples replicate existing results in the optimal monetary policy literature and illustrate the usefulness of the toolkit for highlighting policy trade-offs. We use the toolkit to analyse US monetary policy at the height of the Great Financial Crisis. Given the Fed’s early-2009 baseline macroeconomic projections, we find the Fed’s planned use of the policy rate was close to optimal whereas a more aggressive QE program would have been beneficial.
JEL Code
C61 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Optimization Techniques, Programming Models, Dynamic Analysis
C63 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computational Techniques, Simulation Modeling
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies