A Note on the Volatilities of the Interest Rate and the Exchange Rate Under Different Monetary Policy Instruments: Mexico 1998-2008.

To advance our understanding of the mechanisms through which monetary policy affect the economy, in this note we analyze the volatilities of the Mexican short-term interest rate and of the peso-Dollar exchange rate under two monetary policy instruments: a non-borrowed reserves requirement target (the "Corto") and an interest rate target. Using tests for multiple structural changes, we document that both volatilities decreased around the time Banco de México started the transition from the former to the latter. With respect to the volatility transmission from interest rates to exchange rates and vice versa, we find, using a bivariate GARCH model and causality-in-variance tests, bi-causality during the period of the Corto, but no causal relation after the transition started.Corto, granger causality, multiple structural breaks, multivariate volatility.

Inflation Dynamics in Latin America

We analyze inflation's persistence in the 1980-2006 period for the ten largest Latin American economies using univariate time-series techniques. Although the estimated degree of inflation persistence appears to be different across countries, for the region as a whole the persistence seems to be very high. However, the estimated degree of persistence falls in all countries once we permit structural breaks in the mean of inflation. The timing of these breaks coincides with shifts in the monetary policy regimes and is similar across countries. Regardless of the changes in the mean, the degree of persistence appears to be decreasing in the region, even though for some countries persistence does not seem to be changing.Inflation, Inflation Persistence, Latin America, Monetary Policy, Multiple Breaks, Time Series

Forecasting Exchange Rate Volatility: The Superior Performance of Conditional Combinations of Time Series and Option Implied Forecasts

This paper provides empirical evidence that combinations of option implied and time series volatility forecasts that are conditional on current information are statistically superior to individual models, unconditional combinations, and hybrid forecasts. Superior forecasting performance is achieved by both, taking into account the conditional expected performance of each model given current information, and combining individual forecasts. The method used in this paper to produce conditional combinations extends the application of conditional predictive ability tests to select forecast combinations. The application is for volatility forecasts of the Mexican Peso-US Dollar exchange rate, where realized volatility calculated using intra-day data is used as a proxy for the (latent) daily volatility.Composite Forecasts, Forecast Evaluation, GARCH, Implied volatility, Mexican Peso-U.S. Dollar Exchange Rate, Regime-Switching

Does Inflation Targeting Affect the Dispersion of Inflation Expectations?

In this paper we examine the effect of having an inflation targeting framework on the dispersion of inflation forecasts from professional forecasters. We use a panel data set of 26 countries -including 14 inflation targeters- with monthly information from the last 16 years. We find that the dispersion of long-run inflation expectations is lower in targeting regimes after controlling for country-specific effects, time-specific effects, initial dispersion, the level and the variance of inflation, disinflation periods, and global inflation. When we differentiate between developed and developing countries, we find different dynamics for each group. In particular, the mentioned effect of inflation targeting seems to be present only on the developing countries.Monetary Policy, Survey Data, Panel Data

Bayesian Analysis of ODE's: solver optimal accuracy and Bayes factors

In most relevant cases in the Bayesian analysis of ODE inverse problems, a numerical solver needs to be used. Therefore, we cannot work with the exact theoretical posterior distribution but only with an approximate posterior deriving from the error in the numerical solver. To compare a numerical and the theoretical posterior distributions we propose to use Bayes Factors (BF), considering both of them as models for the data at hand. We prove that the theoretical vs a numerical posterior BF tends to 1, in the same order (of the step size used) as the numerical forward map solver does. For higher order solvers (eg. Runge-Kutta) the Bayes Factor is already nearly 1 for step sizes that would take far less computational effort. Considerable CPU time may be saved by using coarser solvers that nevertheless produce practically error free posteriors. Two examples are presented where nearly 90% CPU time is saved while all inference results are identical to using a solver with a much finer time step.Comment: 28 pages, 6 figure

Forecast Combinations

We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based forecasts. We also provide an analysis of the importance of model instability for explaining gains from forecast combination. Analytical and simulation results uncover break scenarios where forecast combinations outperform the best individual forecasting model.Factor Based Forecasts, Non-linear Forecasts, Structural Breaks, Survey Forecasts, Univariate Forecasts.