Abstract
This paper explores the interaction between monetary policy and prudential regulation in an agent-based modeling framework. Firms borrow funds from the banking system in an economy regulated by a central bank. The central bank carries out monetary policy, by setting the interest rate, and prudential regulation, by establishing the banking capital requirement. Different combinations of interest rate rule and capital requirement rule are evaluated with respect to both macroeconomic and financial stability. Several relevant policy implications were drawn. First, the efficacy of a given capital requirement rule or interest rate rule depends on the specification of the rule of the other type it is combined with. More precisely, less aggressive interest rate rules perform better when the range of variation of the capital requirement is narrower. Second, interest rate smoothing is more effective than the other interest rate rules assessed, as it outperforms those other rules with respect to financial stability and macroeconomic stability. Third, there is no tradeoff between financial and macroeconomic stability associated with a variation of either the capital requirement or the smoothing interest rate parameter. Finally, our results reinforce the cautionary finding of other studies regarding how output can be ravaged by a low inflation targeting.
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Notes
While the role of financial disturbances in generating the 2008 financial crisis has been emphasized in most of the related studies, many authors highlight the importance of previous real sector imbalances in this process. For instance, the mismatch between real wages and productivity growth generated a structural flaw in aggregate demand in the U.S. economy prior to 2007 (Setterfield 2012). The relationship between income inequality and the recent financial crisis (e.g., through the fueling of financial bubbles) has also been extensively discussed (see, e.g., Palley 2016; Skott 2013).
By AB models we are referring to computational agent-based models. There are also models of heterogeneous interacting agents, solvable analytically through technics coming from statistical physics or Markov chains (Gallegati and Kirman 2012).
See the excellent review in Fagiolo and Roventini (2012).
EURACE is a large-scale, multi-sector agent-based model and simulator, which is under development since 2006 within an EU-funded research grant (Cincotti et al. 2011).
Tables 1 and 2, in which the representation of a stock–flow consistent (SFC) model is displayed, are simplified versions of, respectively, Tables 1.3 and 1.2 in Godley and Lavoie (2007). The government net worth with a plus sign (i.e., a negative government net worth) can be interpreted as “net government advances” to the other agents (dos Santos and Zezza 2008).
On consumers’ imperfect price knowledge, see, for instance, Rotemberg (2008).
The assumption of markup pricing behavior goes in hand with robust survey data evidence (e. g., Fabiani et al. 2006).
No calibration exercise on empirical data was performed, but using reasonable parameter values was always a major concern. In fact, most of the parameter values were drawn from existing studies (e.g., Riccetti et al. 2013b). We run simulations on a range of reasonable values and chose a set of parameters whose results were not counterintuitive on empirical grounds.
We also tested both different values of \(\psi \) and rules in which wages are adjusted previously or concomitantly with the price level \(({w_t =f({p_t } )})\). Although in some cases the nominal wages lost their constancy, real wages did not display a remarkable different dynamics.
In our simulations, the average correlation varies between 0.42 and 0.60, depending on the combination between the interest rate rule and the capital requirement rule.
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The authors benefitted from quite useful comments received from Lucca Riccetti, Alberto Russo and participants in seminars at the Central Bank of Brazil, the Marche Polytechnic University (Italy) and the 43rd Brazilian Meeting of Economics (ANPEC), as well as four anonymous referees. Errors and omissions are of course the authors’ sole responsibility.
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Appendix: Parameters and initial conditions
Appendix: Parameters and initial conditions
Number of Monte Carlo simulations | \(100^\mathrm{a}\) | |
---|---|---|
Symbol | Meaning | Value |
Parameters | ||
N | Number of firms | 500 |
\(\alpha \) | Production parameter (see Eq. 1) | 3 |
\(\beta \) | Production parameter (see Eq. 1) | 0.7 |
\(t_D \) | Duration of debts (in periods) | 10 |
\(\eta \) | Labor productivity | 1 |
\(\psi \) | Real wage lower limit parameter (see Eq. 3) | 0.95 |
\(\delta \) | Fraction of profits distributed as dividends | 0.25 |
\(\tau \) | Tax rate | 0.25 |
\(\phi \) | Markup sensitivity to a change in market share (see Eq. 6) | 0.2 |
\(\lambda \) | Leverage sensitivity to a change in demand (see Eq. 10) | 1 |
\(i^{B}\) | Base interest rate | 0.02 |
k | Capital requirement ratio | 0.08 |
\(\gamma \) | Risk premium parameter (see Eq. 11) | 0.02 |
Initial conditions | ||
\(NW_{i,0} ^\mathrm{b}\) | Firms’ initial net worth | \(NW_{i,0} \sim N\left( {10,2} \right) \) |
\(\mu _{i,0} ^\mathrm{c}\) | Firms’ initial markup | \(\mu _{i,0} \sim N\left( {0.15,0.03} \right) \) |
\(w_0 \) | Initial nominal wage | 1 |
\(l_{i,0}^*\) | Firms’ initial leverage target | \(l_{i,0}^*\sim U\left( {0.01,3} \right) \) |
\(s_{i,0} \) | Firms’ initial market share | \({NW_{i,0} }/{\sum {NW_{i,0} } }\) |
\(NW_o^B \) | Banking system’s initial net worth | \(2k\sum {NW_{i,0} } \) |
\(\Gamma _0 \) | Government initial surplus | 10,000 |
Policy rules parameters | ||
\(r^{*}\) | Equilibrium real interest rate | 0.02 |
\({\pi }^{T}\) | Inflation target | 0 |
\(\chi \) | Smoothing parameter of the interest rate rule | \(0.5^\mathrm{d}\) |
\({\theta }_{1}\) | Sensitivity of the interest rate to the inflation | 0.5 |
\({\theta }_{2}\) | Sensitivity of the interest rate to the output gap | 0.5 |
\({\theta }_{3}\) | Sensitivity of the interest rate to the credit-to-output gap | \([0.1, 0.2, 0.5]^\mathrm{e}\) |
\({\theta }^{C}\) | Sensitivity of the capital requirement to the credit-to-output gap (UNB and CRS rules) or to the credit gap (CGR rule) | 0.5 |
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Alexandre, M., Lima, G.T. Combining monetary policy and prudential regulation: an agent-based modeling approach. J Econ Interact Coord 15, 385–411 (2020). https://doi.org/10.1007/s11403-017-0209-0
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DOI: https://doi.org/10.1007/s11403-017-0209-0