Public debt management and the interaction between fiscal and monetary policies

PurposeThis paper aims to investigate the existing relations between the management of public bonds on the dynamics of debt, term structure of interest rates and economic cycle, through a dynamic stochastic general equilibrium model (DSGE), which was estimated through Bayesian inference techniques using data from Brazil.Design/methodology/approachThe model developed was used to investigate the effects of the public debt average maturity management when the economy faces a monetary policy shock. For this, three management scenarios are evaluated, including Brazilian securities average term.FindingsContrary to what might be inferred from DSGE models that limited the analysis of the debt term by imposing only one-period bonds, a contractionary monetary policy shock does not necessarily cause public debt to increase significantly. Debt term structure plays a crucial role in this result since the government does not need to roll the debt over at higher costs when the debt term profile is longer, reducing the debt service costs and then the impact on the overall debt.Originality/valueDespite the relevance of this theme and its implications for the dynamics of the economy, there is still a gap to be filled in the literature when using DSGE models, since most part of the work that used this methodology limited the analysis of the debt term by imposing that government issues only one-period bonds. This paper differs from the others insofar as it promotes an investigation focused on the role played by debt maturity management on the performance of the contractionary monetary policy. This approach can generate a better understanding of debt management policy and its interaction with fiscal and monetary policies.

VALIDATING DSGE MODELS WITH SVARS AND HIGH-DIMENSIONAL DYNAMIC FACTOR MODELS

A popular validation procedure for Dynamic Stochastic General Equilibrium (DSGE) models consists in comparing the structural shocks and impulse-response functions obtained by estimation-calibration of the DSGE with those obtained in an Structural Vector Autoregressions (SVAR) identified by means of some of the DSGE restrictions. I show that this practice can be seriously misleading when the variables used in the SVAR contain measurement errors. If this is the case, for generic values of the parameters of the DSGE, the shocks estimated in the SVAR are not “made of” the corresponding structural shocks plus measurement error. Rather, each of the SVAR shocks is contaminated by noncorresponding structural shocks. We argue that High-Dimensional Dynamic Factor Models are free from this drawback and are the natural model to use in validation procedures for DSGEs.

Public Debt Frontier: A Python Toolkit for Analyzing Public Debt Sustainability

This study proposes a synthetic visual indicator with which to perform debt sustainability analysis using dynamic general equilibrium models. In a single diagram, we summarized the general equilibrium relationships among economic activity, government budget, and the maximum amount of sustainable public debt. Then, we measured sustainability using the distance of actual debt from the model-consistent maximum debt. This indicator can be implemented with any DSGE model; as a backing theory, we used a neoclassical model augmented with endogenous tax revenues, disaggregated public spending, different production technologies for public and private goods, non-atomistic wage setters in public labor (unions), and a fully specified maturity curve for public bonds. We provided an example of its usage using the case of Greece during the last public debt crisis. To perform the numerical analysis, we developed original software, whose advantage is allowing an audience without expertise in DSGE models to perform general equilibrium debt sustainability analyses without requiring an understanding of the technicalities of DSGE models.

The Role of Wage Formation in Empirical Macroeconometric Models

The specification of model equations for nominal wage setting has important implications for the properties of macroeconometric models and requires system thinking and multiple equation modeling. The main models classes are the Phillips curve model (PCM), the wage–price equilibrium correction model (WP-ECM), and the New Keynesian Phillips curve (NKPCM). The PCM was included in the macroeconometric models of the 1960s. The WP‑ECM arrived in the late 1980s. The NKPCM is central in dynamic stochastic general equilibrium models (DSGEs). The three model classes can be interpreted as different specifications of the system of stochastic difference equations that define the supply side of a medium-term macroeconometric model. This calls for an appraisal of the different wage models, in particular in relation to the concept of the non-accelerating inflation rate of unemployment (NAIRU, or natural rate of unemployment), and of the methods and research strategies used. The construction of macroeconomic model used to be based on the combination of theoretical and practical skills in economic modeling. Wage formation was viewed as being forged between the forces of markets and national institutions. In the age of DSGE models, macroeconomics has become more of a theoretical discipline. Nevertheless, producers of DSGE models make use of hybrid forms if an initial theoretical specification fails to meet a benchmark for acceptable data fit. A common ground therefore exists between the NKPC, WP‑ECM, and PCM, and it is feasible to compare the model types empirically.

Characterizing and Communicating the Balance of Risks of Macroeconomic Forecasts: A Predictive Density Approach for Colombia

Since July 2021, Banco de la República strengthened its forecasting process and communication instruments, by involving predictive densities on the projections of its models, PATACON and 4GM. This paper presents the main theoretical and empirical elements of the predictive density approach for macroeconomic forecasting. This model-based methodology allows to characterize the balance of risks of the economy, and quantify their effects through a joint probability distribution of forecasts. We estimate this distribution based on the simulation of DSGE models, preserving the general equilibrium relationships and their macroeconomic consistency. We also illustrate the technical criteria used to represent the prospective factors of risk through the probability distributions of shocks.

Forecasting for the Optimal Numbers of COVID-19 Infection to Maintain Economic Circular Flows of Thailand

We evaluated the movement in the daily number of COVID-19 cases in response to the real GDP during the COVID-19 pandemic in Thailand from Q1 2020 to Q1 2021. The aim of the study was to find the number of COVID-19 cases that could maintain circulation of the country’s economy. This is the question that most of the world’s economies have been facing and trying to figure out. Our theoretical model introduced dynamic stochastic general equilibrium (DSGE) models with a special emphasis on Bayesian inference. From the results of the study, it was found that the most reasonable number of COVID-19 cases that still maintains circulation of the country’s economy is about 3000 per month or about 9000 per quarter. This demonstrates that the daily number of COVID-19 cases significantly affects the growth of Thailand’s real GDP. Economists and policymakers can use the results of empirical studies to come up with guidelines or policies that can be implemented to reduce the number of infections to satisfactory levels in order to avoid Thailand lockdown. Although the COVID-19 outbreak can be suppressed through lockdown, the country cannot be locked down all the time.