Francesco Ravazzolo

Real-Time Inflation Forecasting in a Changing World

This paper revisits inflation forecasting using reduced-form Phillips curve forecasts, that is, inflation forecasts that use activity and expectations variables. We propose a Phillips-curve-type model that results from averaging across different regression specifications selected from a set of potential predictors. The set of predictors includes lagged values of inflation, a host of real-activity data, term structure data, nominal data, and surveys. In each individual specification, we allow for stochastic breaks in regression parameters, where the breaks are described as occasional shocks of random magnitude. As such, our framework simultaneously addresses structural change and model uncertainty that unavoidably affect Phillips-curve-based predictions. We use this framework to describe personal consumption expenditure (PCE) deflator and GDP deflator inflation rates for the United States in the post-World War II period. Over the full 1960-2008 sample, the framework indicates several structural breaks across different combinations of activity measures. These breaks often coincide with policy regime changes and oil price shocks, among other important events. In contrast to many previous studies, we find less evidence of autonomous variance breaks and inflation gap persistence. Through a real-time out-of-sample forecasting exercise, we show that our model specification generally provides superior one-quarter-ahead and one-year-ahead forecasts for quarterly inflation relative to an extended range of forecasting models that are typically used in the literature.

Measuring Sovereign Contagion in Europe

This paper analyzes the sovereign risk contagion using credit default swaps (CDS) and bond premiums for the major eurozone countries. By emphasizing several econometric approaches (nonlinear regression, quantile regression and Bayesian quantile regression with heteroskedasticity) we show that propagation of shocks in Europes CDS has been remarkably constant for the period 2008-2011 even though a significant part of the sample periphery countries have been extremely affected by their sovereign debt and fiscal situations. Thus, the integration among the different eurozone countries is stable, and the risk spillover among these countries is not affected by the size of the shock, implying that so far contagion has remained subdue. Results for the CDS sample are confirmed by examining bond spreads. However, the analysis of bond data shows that there is a change in the intensity of the propagation of shocks in the 2003-2006 pre-crisis period and the 2008-2011 post-Lehman one, but the coefficients actually go down, not up! All the increases in correlation we have witnessed over the last years come from larger shocks and the heteroskedasticity in the data, not from similar shocks propagated with higher intensity across Europe. This is the fi rst paper, to our knowledge, where a Bayesian quantile regression approach is used to measure contagion. This methodology is particularly well-suited to deal with nonlinear and unstable transmission mechanisms.

The power of weather

Weather information demonstrates predictive power in forecasting electricity prices in day-ahead markets in real time. In particular, next-day weather forecasts improve the forecast accuracy of Scandinavian day-ahead electricity prices in terms of point and density forecasts. This suggests that weather forecasts can price the weather premium on electricity prices. By augmenting with weather forecasts, GARCH-type time-varying volatility models statistically outperform specifications which ignore this information in density forecasting.

Term Structure Forecasting Using Macro Factors and Forecast Combination

We examine the importance of incorporating macroeconomic information and, in particular, accounting for model uncertainty when forecasting the term structure of U.S. interest rates. We start off by analyzing and comparing the forecast performance of several individual term structure models. Our results confirm and extend results found in previous literature that adding macroeconomic information, through factors extracted from a large number of individual series, tends to improve interest rate forecasts. We then show, however, that the predictive power of individual models varies over time significantly. Models with macro factors are the more accurate in and around recession periods. Models without macro factors do particularly well in low-volatility subperiods such as the late 1990s. We demonstrate that this problem of model uncertainty can be mitigated by combining individual model forecasts. Combining forecasts leads to encouraging gains in predictability, especially for longer-dated maturities, and importantly, these gains are consistent over time.

Forecast densities for economic aggregates from disaggregate ensembles

Abstract We extend the “bottom up” approach for forecasting economic aggregates with disaggregates to probability forecasting. Our methodology utilises a linear opinion pool to combine the forecast densities from many disaggregate forecasting specifications, using weights based on the continuous ranked probability score. We also adopt a post-processing step prior to forecast combination. These methods are adapted from the meteorology literature. In our application, we use our approach to forecast US Personal Consumption Expenditure inflation from 1990q1 to 2009q4. Our ensemble combining the evidence from 16 disaggregate PCE series outperforms an integrated moving average specification for aggregate inflation in terms of density forecasting.

Forecasting Macroeconomic Variables Using Disaggregate Survey Data

We construct factor models based on disaggregate survey data for forecasting national aggregate macroeconomic variables. Our methodology applies regional and sectoral factor models to Norges Bank’s regional survey and to the Swedish Business Tendency Survey. The analysis identifies which of the pieces of information extracted from the individual regions in Norges Bank’s survey and the sectors for the two surveys perform particularly well at forecasting different variables at various horizons. The results show that several factor models beat an autoregressive benchmark in forecasting inflation and the unemployment rate. However, the factor models are most successful at forecasting GDP growth. Forecast combinations using the past performances of regional and sectoral factor models yield the most accurate forecasts in the majority of the cases.

The Macroeconomic Forecasting Performance of Autoregressive Models with Alternative Specifications of Time-Varying Volatility

This paper compares alternative models of time-varying macroeconomic volatility on the basis of the accuracy of point and density forecasts of macroeconomic variables. In this analysis, we consider both Bayesian autoregressive and Bayesian vector autoregressive models that incorporate some form of time-varying volatility, precisely stochastic volatility (both with constant and time-varying autoregressive coefficients), stochastic volatility following a stationary AR process, stochastic volatility coupled with fat tails, GARCH and mixture of innovation models. The comparison is based on the accuracy of forecasts of key macroeconomic time series for real-time post War-II data both for the United States and United Kingdom. The results show that the AR and VAR specifications with widely-used stochastic volatility dominate models with alternative volatility specifications, in terms of point forecasting to some degree and density forecasting to a greater degree.

Bayesian Model Averaging in the Presence of Structural Breaks

This paper develops a return forecasting methodology that allows for instabil ity in the relationship between stock returns and predictor variables, for model uncertainty, and for parameter estimation uncertainty. The predictive regres sion speci¯cation that is put forward allows for occasional structural breaks of random magnitude in the regression parameters, and for uncertainty about the inclusion of forecasting variables, and about the parameter values by em ploying Bayesian Model Averaging. The implications of these three sources of uncertainty, and their relative importance, are investigated from an active investment management perspective. It is found that the economic value of incorporating all three sources of uncertainty is considerable. A typical in vestor would be willing to pay up to several hundreds of basis points annually to switch from a passive buy-and-hold strategy to an active strategy based on a return forecasting model that allows for model and parameter uncertainty as well as structural breaks in the regression parameters.

Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data

Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying combination strategies are introduced. In particular, a weight dynamics driven by the past performance of the predictive densities is considered and the use of learning mechanisms. The approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.

Predicting the Term Structure of Interest Rates: Incorporating Parameter Uncertainty, Model Uncertainty and Macroeconomic Information

We forecast the term structure of U.S. Treasury zero-coupon bond yields by analyzing a range of models that have been used in the literature. We assess the relevance of parameter uncertainty by examining the added value of using Bayesian inference compared to frequentist estimation techniques, and model uncertainty by combining forecasts from individual models. Following current literature we also investigate the benefits of incorporating macroeconomic information in yield curve models. Our results show that adding macroeconomic factors is very beneficial for improving the out-of-sample forecasting performance of individual models. Despite this, the predictive accuracy of models varies over time considerably, irrespective of using the Bayesian or frequentist approach. We show that mitigating model uncertainty by combining forecasts leads to substantial gains in forecasting performance, especially when applying Bayesian model averaging.