Latin American Journal of Central Banking最新文献

Chile implemented large pension withdrawals during the pandemic relative to other countries. Afterwards, Chile increased non-contributory benefits in a quasi-universal scheme. Simulating the future pensions, I show that the average loss in contributory pension income is 27.9%, with losses of 23.9% and 31.4% for men and women, respectively. After accounting for public transfers, the average loss in total pension income is just 6.2%, with losses of 7.5% and 5.2% for men and women, respectively. Current retirees lost just 1.1% of their pension income after accounting for the government transfers. The state may end up covering 92% of the total value of the pension withdrawals through the increased transfers.

The output gap, while inherently unobservable, plays a pivotal role in informing policymakers due to its significant implications for forecasting inflation rates and understanding the mechanisms of monetary policy transmission. Traditional filters frequently employed in estimating the output gap face criticism for being purely statistical and lacking economic theory. Conversely, estimates derived from Dynamic Stochastic General Equilibrium (DSGE) models encounter challenges stemming from the assumption of constant parameters over time. This study focuses on estimating the output gap for Brazil, employing a full specified DSGE model that incorporates Markov-Switching elements (MS-DSGE) to account for potential regime shifts. We introduce four model versions, some of which incorporate variations in volatilities and Taylor’s rule parameters. In order to compare our output gap estimate with other approaches, we perform prediction tests, both with the central bank’s reaction function and with the free price inflation Phillips curve. Our results in the first test indicate that the HP (Hodrick–Prescott) filter estimate performs better in the short and mid-term, but the MS-DSGE estimate presented better results in the long run. In the second exercise, no output gap series stands out among the approaches considered. In this context, the estimated output gap from the MS-DSGE framework emerges as a valuable asset within the arsenal available to policymakers, contributing meaningfully to their analytical toolkit.

This paper estimates a standard Dynamic Stochastic General Equilibrium (DSGE) model that includes a wage and price Phillips curves with different expectation formation processes for Brazil and the USA. Other than the standard rational expectation process, we also use a limited rationality process, the adaptative learning model. In this context, we show that the separate inclusion of a labor market in the model helps to anchor inflation even in a situation of adaptive expectations, a positive output gap and inflation above target. The estimation results show that the adaptive learning model does a better job in fitting the data in Brazil. In addition, the estimation shows that expectations are more backward-looking and started to drift away sooner in 2021 in Brazil than in the USA. We then conduct optimal policy exercises that prescribe front-loading monetary policy tightening and easing earlier than the estimated monetary policy rule in the context of positive output gaps and inflation far above the central bank target.

Emerging Market and Developing Economies (EMDE) countries are the leading destinations of Foreign Direct Investment (FDI). We investigate whether prudent monetary and fiscal policy through indicators that reflect the expectations concerning the central bank's commitment to a target and the sustainability of government finance affects FDI inflows. Based on a large sample of 75 EMDE countries from 1990 to 2019, we provide empirical evidence through panel data analysis that prudent macroeconomic policies are an essential driver of FDI inflows. The findings support the view that using prudent monetary and fiscal policy can help enhance FDI inflows in EMDE countries.

Distributed ledger technologies (DLT) are increasingly considered to enhance payment systems’ and market infrastructures’ functionalities like their accessibility and interoperability. We explore DLT architecture options to enable a new tiered access in the RTGS system of Chile for new payment service providers (PSP). We find that by introducing decentralized apps (DApps) the RTGS system would become more accessible and interoperable by enabling a tiered access to new participants. Our work also suggests that central banks exploring design alternatives for retail Central Bank Digital Currencies (CBDC) could find relevant our approach as a pivot to build a far-reaching PSP network.

COVID-19 has proven to be a unique and complex shock for firms. In this paper we analyze the performance of individual Chilean firms during this episode drawing on administrative datasets. In particular we empirically characterize the international trade adjustment at the firm and product level. Importer firms, specially in the manufacturing sector, have adjusted their import flow through three margins along 2020/21. In 2020 imports declined as some firms either stopped their import activity, or they imported less product varieties (product and country of origin) or by reducing the intensity of imported varieties. In this period importers faced a short-lived increase in imported input costs. In 2021 imports rebound strongly. While exporter firms (excluding mining) kept their export activity as well as their selling price stable. We also explore if foreign factors such as the incidence of COVID-19 and containment measures in partner countries had an impact on Chilean trade during 2020. We find that these foreign factors had an impact on intermediate rather than consumption goods imports.

This paper provides the first steps towards a theoretical and structural modelling framework through which optimal decision making in financial market infrastructures such as payments clearing and settlement systems can be assessed from a market microstructure perspective. In particular, the paper focuses on the application of agent-based computational economics and stochastic games in modelling the bilateral credit limit establishing behaviour of Participants in loss sharing arrangements within financial market infrastructures such as the Canadian Large Value Payments System (LVTS). With specific focus on the LVTS, the paper presents a structural model where the payments system represents a market in which bilateral credit limits are the pricing mechanisms for intraday liquidity provisioning and the credit risk arising from the loss sharing arrangement. The data-driven stochastic game framework further illustrates how payments data, in conjunction with other financial market and credit data, can be used to assess emergent macroscopic outcomes in clearing and settlement systems from the underpinning interactions of autonomous decision making agents. The paper speaks to potential policy issues such as the effectiveness of policy levers such as the System-Wide Percentage, regulatory concerns around procyclicality and free-riding arising from the market microstructure behaviours, and design of the System.

This paper examines the impact of fiscal and monetary policies on carbon dioxide emissions in Trinidad and Tobago, using data from 1970 to 2020. We use a fiscal policy index based on government revenue and expenditure, a monetary policy index based on interest rates and reserve requirement data, and a Non-linear Autoregressive Distributed Lag technique. Our results show that expansionary fiscal policy raises emissions, while contractionary fiscal policy reduces emissions. Intriguingly, expansionary monetary policy increases emissions, while contractionary monetary policy lowers them. These findings hold significance for fiscal and monetary policymakers working on climate change mitigation strategies.

This paper analyzes the contribution of supply, demand, and labor market shocks, to the evolution of regional production and inflation of manufactured goods in Mexico within the context of the pandemic. Under the identification of a Bayesian Structural Vector Autoregressive (SBVAR) model with zero and sign restrictions, it is found that since 2021, external demand shocks increased their contribution relative to local shocks in explaining the growth of manufactured goods production in all regions except the South; meanwhile, external supply shocks increased their positive contribution in explaining inflationary pressures across all regions. On the other hand, from 2022 onwards, labor supply shocks have contributed to the production and inflation of manufactured goods mainly in the Northern and North-Central regions, while wage bargaining shocks have emerged as drivers of inflationary pressures in all regions.

Colombia is particularly affected by the El Niño Southern Oscillation (ENSO) weather fluctuations. In this context, this study explores how adverse weather events linked to ENSO affect inflation expectations in Colombia and how to incorporate these second-round effects into a small open economy New Keynesian model. Using BVARx models, we find evidence that inflation expectations – obtained from surveys and break-even inflation measures – are influenced by weather-related supply shocks. Building on this stylized fact, we modify one of the core forecasting models of the Banco de la República to incorporate the mechanisms through which weather-related shocks could affect marginal costs and inflation expectations. We conclude that ENSO shocks play a significant role in influencing both inflation and the dynamics of inflation expectations, a fact that should be considered by policymakers.