Aguirre, H. A. and E. F. Blanco (2015). Credit and macroprudential policy in an emerging economy: a structural model assessment. Bank of International Settlement Working Paper 504.
Anand, R. and P. Khera (2016). Macroeconomic impact of product and labor market reforms on informality and unemployment in india. International Monetary Fund Working Paper 16/47.
Angeloni, I. and E. Faia (2013). Capital regulation and monetary policy with fragile banks. Journal of Monetary Economics 60, 311–324.
Batini, N., P. Levine, E. Lotti, and B. Yang (2011). Informality, frictions and monetary policy. School of Economics, University of Surrey, School of Economics Discussion Papers 0711.
Blanchard, O. and J. Galí (2010). Labor markets and monetary policy: A new keynesian model with unemployment. American Economic Journal: Macroeconomics 2, 1–30.
Clarida, R., J. Galí, and M. Gertler (2002). A simple framework for international monetary policy analysis. Journal of Monetary Economics 49, 879–904.
Corsetti, G., L. Dedola, and S. Leduc (2010). Optimal monetary policy in open economies. In B. Friedman and M. Woodford (Eds.), Handbook of Monetary Economics Vol.3B, Chapter 16, pp. 862–933. Elsevier.
De Paoli, B. and M. Paustian (2017). Coordinating monetary and macroprudential policies. Journal of Money, Credit, and Banking 49, 319–349.
Gerali, A., S. Neri, L. Sessa, and F. M. Signoretti (2010). Credit, and banking in a DSGE model of the euro area. Journal of Money, Credit and Banking 42, 107–141.
Iacoviello, M. and S. Neri (2010). Housing market spillovers: Evidence from an estimated DSGE model. American Economic Journal: Macroeconomics 2, 125–164.
Jouini, N. and N. Rebei (2014). The welfare implications of services liberalization in a developing country. Journal of Development Economics 106, 1–14.
Lambertini, L., C. Mendicino, and M. T. Punzi (2013). Leaning against boom–bust cycles in credit and housing prices. Journal of Economic dynamics and Control 37, 1500–1522.
Laureys, L. and R. Meeks (2018). Monetary and macroprudential policies under rules and discretion. Economics Letters 170, 104–108.
Lewis, V. and S. Villa (2016). The interdependence of monetary and macroprudential policy under the zero lower bound. National Bank of Belgium Working Paper 310.
Medina, L. and F. Schneider (2018). Shadow economies around the world: What did we learn over the last 20 years? International Monetary Fund Working Paper 18/17.
Mumtaz, H. and F. Zanetti (2017). The effect of labor and financial frictions on aggregate fluctuations. Macroeconomic Dynamics, Forethcoming.
Ozkan, F. G. and D. F. Unsal (2014). On the use of monetary and macroprudential policies for small open economies. International Monetary Fund Working Paper 14/112.
Pappa, E., R. Sajedi, and E. Vella (2015). Fiscal consolidation with tax evasion and corruption. Journal of International Economics 96, 56–75.
Quint, D. and P. Rabanal (2014). Monetary and macroprudential policy in an estimated DSGE model of the euro area. International Journal of Central Banking 37, 169–236.
Schmitt-Grohe, S. and M. Uribe (2007). Optimal simple and implementable monetary and fiscal rules. Journal of Monetary Economics 54, 1702–1725.
Schneider, F., A. Buehn, and M. C. Punzi (2010). New estimates for the shadow economies all over the world. International Economic Journal 24, 443–461.
Shapiro, Alan, F. and A. Gonzalez (2015). Macroprudential policy and labor market dynamics in emerging economies. International Monetary Fund Working Paper 15/18.
Smets, F. and R. Wouters (2003). An estimated dynamic and stochastic general equilibrium model for the euro area. Journal of the European Economic Association 5, 1123–1175.
Unsal, D. F. (2013). Capital flows and financial stability: Monetary policy and macroprudential responses. International Journal of Central Banking 9, 233–285.
The estimation results of this paper could be also used as an alternative reference to document the size of the informal sector as it infers its value from the dynamics of a set of macroeconomic and financial data in the context of an estimated DSGE model.
Without loss of generality, we assume that
To simplify the bank sector specification, we consider that individual banks are perfectly competitive in the deposit market — i.e.,
This value implies an implicit size of the informal sector of around 30 percent, once the rest of the parameters are calibrated to their prior averages.
To calculate the posterior distribution to evaluate the marginal likelihood of the model, the Metropolis-Hastings algorithm is employed. We compute the posterior moments of the parameters using a sample of generated 500, 000 while discarding the first 20 percent.
Based on the maximization of the proposed welfare measure, we limit our attention to policy coefficients in the interval (1,5] for ρπ, [0,1] for ρy, and [0,3] for ρs as in (Schmitt-Grohe and Uribe, 2007), and in the interval [0,3] for macroprudential instruments.
As the optimized policy parameter values are virtually unchanged in the independent and cooperative settings, we can infer the outcome of optimal macroprudential policy by simply taking the difference of welfare gains from the results shown in Table 3. Interestingly, Laureys and Meeks (2018) show that including a reaction to the bank capital ratio in the monetary policy rule could improve the outcome in terms of the objective function defined as a weighted average of the volatilties of some key variables. Further, the coefficients on inflation and output gaps significantly decline. Our results differ from those results as the coefficient of the optimized monetary rule remain virtually unchanged, which could come from the nature of the definition of the metric — welfare gain versus weighted sum of unconditional variances.
One can notice that informality generates welfare losses, which is explained by the distortionary tax on informal production.
We recognize that our results are specific to the macroprudential rules specified in the present paper, which are commonly adopted in the literature. Besides, further research could extend the coverage of the countercyclical rules as a robustness check.
In the sensitivity analysis exercise, we only consider the parameters of the alternative macroprudential rules that generate higher welfare.