Latin American Journal of Central Banking最新文献

We developed a dynamic stochastic general equilibrium (DSGE) model for a small, open economy with a banking sector and endogenous default to assess two macroprudential tools: countercyclical capital buffers (CCB) and dynamic provisions (DP). The model is estimated with data for Uruguay, where dynamic provisioning has existed since the early 2000s. Both tools force banks to build buffers, but DP seem to outperform the CCB in smoothing the cycle. We also find that the source of the shock affecting the financial system matters in assessing the relative performance of both tools. Given a positive external shock, the credit-to-GDP ratio decreases, which should discourage its use as an indicator variable to activate countercyclical regulation.

In this paper, we develop a structural model to estimate the current level of the natural rate for a small open economy, featuring a rich set of shocks to provide economic intuition for its underlying drivers. The model follows the New Keynesian tradition with several frictions and is able to draw implications for a monetary policy stance. In contrast to other DSGE models in the literature, this framework includes two main blocks—one related to the foreign sector and one associated with the local economy, linked by the uncovered interest rate parity condition. With this structure, the natural rate is affected by local and external factors, disaggregated in permanent and transitory shocks. Using Bayesian techniques, the model estimates the natural interest rate for two example cases, Mexico and Canada, considering data from these economies and the United States. Results show that the US economy is relevant to explaining natural rates in both countries. For the Mexican case, the drivers are shocks to the US risk premium and the marginal efficiency of investment, as well as country risk premium variations. For Canada, shocks to the households’ discount factor play an important role.

The adoption of inflation-targeting regimes has led central banks to devote considerable efforts to improve their transparency. Following this trend, several authors have developed tools to measure and compare the levels of transparency of central banks. This paper undertakes this task for the Central Bank of Uruguay by applying two different transparency indexes. The first one was designed in the mid-2000s and has been applied in a sample with a significant number of countries. The second index is based on a new approach that seeks to reflect the best practices of the most advanced inflation-forecast-targeting regimes.

In this paper, we focus on the pass-through of exchange rate fluctuations into prices of final goods and services and examine whether contrasting pass-through rates are associated with regional and/or product-specific characteristics. Using CPI micro-data from 2002 to 2010, we estimate industry-specific rates of pass-through across regions in Mexico. By looking at within-country price responses, we alleviate shortcomings of cross-country studies that assess pass-through determinants. The results indicate that pass-through rates differ across regions and industries: low pass-through regions exhibit nearly one-quarter of the elasticity shown by high pass-through regions after twelve months. This heterogeneity prevails at longer horizons. The findings suggest that full pass-through is rejected for all regions and industries. Most of these differences in transmission rates are explained by regional and product characteristics: demand conditions, economic development, distance to the US border, import intensity, price change dispersion and expenditure share play a clear role in increasing pass-through, whereas market density dampens pass-through rates. The evidence confirms pricing-to-market theories and has implications for the design of monetary and exchange rate policies.

We propose a framework for consistently evaluating core inflation measures via a straightforward application of sound statistical inference principles. Under this framework, inflation measures (both headline and core) are regarded as estimators tracking the economy’s true, unobserved inflation rate. We depart from the arbitrary convention in the literature of approximating true (or trend) inflation as some moving average of the observed headline inflation. Instead, we regard trend inflation as the unobserved inflation rate that corresponds to the whole population of consumer price changes while the observed inflation measures are estimators of trend inflation based on particular samples of consumer price changes. Hence, the evaluation of inflation measures is rigorously derived from the sampling distribution properties of the corresponding estimators, in contrast to the use of ad hoc criteria for evaluating core inflation measures, prevalent both in the academic literature and in most central banks’ practices. We implement our evaluation approach for the Guatemalan Consumer Price Index (CPI) data by applying a computational bootstrapping technique. Finally, we showcase the evaluation results for the Guatemalan data regarding the performance of some widely used core inflation measures.

In this study, we aim to assess the relevance of credit composition to entrepreneurship empirically in light of the Schumpeterian perspective. The results of such an analysis can imply whether central banks should continue with the so-called neutrality principle or undertake an active credit policy. We employ a panel data model to quantify the effect of credit composition on entrepreneurship in 54 high- and middle-income economies from 2001 to 2016. To capture credit composition, we disaggregate total credit as credit to non-financial and financial businesses as well as credit to households and mortgages, and we hypothesize that the larger share of credit for non-financial businesses and households would be associated with greater entrepreneurship. The results indicate that credit composition is important for both high- and middle-income economies, but the effective composition of credit is different in the two sub-samples, which is why the effectiveness of credit allocation should not be taken for granted and active remedies are required. This paper corroborates the Schumpeterian view on the ties between credit allocation and entrepreneurship in both high- and middle-income economies.

We compute and characterize several labor flow measures using administrative tax records for all formal Chilean firms and employees. Our results show that labor mobility in Chile is significant by international standards, with the reallocation rate averaging 37% over the last decade, the highest value among the 25 OECD countries with comparable data. The magnitude of labor reallocation is highly heterogeneous among firms and industries, highest in Agriculture and Construction. Job reallocation is also high for smaller companies, primarily due to high firm creation and destruction rates, and for firms that pay lower wages. Finally, there is a significant procyclical behavior of workers’ entry rate and, in smaller magnitude, a countercyclical reaction of the exit rate, consistent with international evidence that shows job creation as the main adjustment mechanism over the business cycle.

The concept of centrality is widely used to monitor systems with a network structure because it allows identifying their most influential participants. This monitoring task can be difficult if the number of system participants is considerably large or if the wide variety of centrality measures currently available produce non-coincident (or mixed) signals. This document uses robust principal component analysis to evaluate a set of centrality measures calculated for the financial institutions that participate in Colombia's four financial market infrastructures. The results obtained are used to construct general indices of centrality, using the most robust measures of centrality as inputs and leaving aside those considered redundant.

We estimate real estate’s exposure in Chile to five weather risks, including labor productivity loss due to heat, fires, floods, drought coastal deterioration as measured by the Chilean Climatic Risk Atlas (ARCLIM) and Climate Impact Explorer (CIE) sources. According to our joint ARCLIM-CIE indicator, we measure risk exposure for the appraisal value of all properties of 39% for Chile and 51%, 36%, 36% and 27% for the Central, North, Metropolitan and South macrozones, respectively. Flooding is the greatest risk for Chile, followed by drought. We find that the CIE source underestimates the climate exposures in Chile relative to the ARCLIM measures, particularly for the flooding and drought risks.

In this paper, we explore machine learning (ML) methods to improve inflation forecasting in Brazil. An extensive out-of-sample forecasting exercise is designed with multiple horizons, a large database of 501 series, and 50 forecasting methods, including new ML techniques proposed here, traditional econometric models and forecast combination methods. We also provide tools to identify the key variables to predict inflation, thus helping to open the ML black box. Despite the evidence of no universal best model, the results indicate that ML methods can, in numerous cases, outperform traditional econometric models in terms of mean-squared error. Moreover, the results indicate the existence of nonlinearities in the inflation dynamics, which are relevant to forecasting inflation. The set of top forecasts often includes forecast combinations, tree-based methods (such as random forest and xgboost), breakeven inflation, and survey-based expectations. Altogether, these findings offer a valuable contribution to macroeconomic forecasting, especially, focused on Brazilian inflation.