Sebastiano Manzan

Differential Interpretation in the Survey of Professional Forecasters

In this paper we estimate a simple Bayesian learning model to expectations data from the Survey of Professional Forecasters. We reformulate the model in terms of forecast revisions, which allows to abstract from differences in priors and to focus the analysis on the relationship between revisions and signal. The model depends on two parameters, the forecaster’s belief about the signal bias and its weight. The empirical analysis shows that there is significant heterogeneity in the parameters among forecasters, in particular at longer forecast horizons. The cross-sectional distribution of the estimated bias parameter has a median close to zero, while its dispersion decreases with the horizon. A similar result is obtained for the weight parameter, with the median across forecasters increasing toward one and the dispersion decreasing when approaching the target date. The exception to this pattern is CPI inflation for which we find that dispersion, in particular for the estimated weight, increases closer to the target date. Furthermore, the results indicate that a possible explanation for the persistence in dispersion, even at short horizons, is the heterogeneity of interpretation of new information, in the sense that agents with optimistic (relative to the other forecasters) priors are also likely to believe that the signal underestimates the future realization of the variable, and the opposite for forecasters with pessimistic views. We also find that the latter type of forecasters are more likely to assign a low weight to the signal, while optimistic forecasters incorporate the new information faster.

A Semiparametric Analysis of Gasoline Demand in the United States Reexamining The Impact of Price

The evaluation of the impact of an increase in gasoline tax on demand relies crucially on the estimate of the price elasticity. This article presents an extended application of the Partially Linear Additive Model (PLAM) to the analysis of gasoline demand using a panel of U.S. households, focusing mainly on the estimation of the price elasticity. Unlike previous semiparametric studies that use household-level data, we work with vehicle-level data within households that can potentially add richer details to the price variable. Both households and vehicles data are obtained from the Residential Transportation Energy Consumption Survey (RTECS) of 1991 and 1994, conducted by the U.S. Energy Information Administration (EIA). As expected, the derived vehicle-based gasoline price has significant dispersion across the country and across grades of gasoline. By using a PLAM specification for gasoline demand, we obtain a measure of gasoline price elasticity that circumvents the implausible price effects reported in earlier studies. In particular, our results show the price elasticity ranges between −0.2, at low prices, and −0.5, at high prices, suggesting that households might respond differently to price changes depending on the level of price. In addition, we estimate separately the model to households that buy only regular gasoline and those that buy also midgrade/premium gasoline. The results show that the price elasticities for these groups are increasing in price and that regular households are more price sensitive compared to nonregular.

Forecasting the return distribution using high-frequency volatility measures.

The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a benchmark GARCH model. The results suggest that the model outperforms an asymmetric GARCH specification when applied to the S&P 500 futures returns, in particular on the right tail of the distribution. However, the model provides similar accuracy to a GARCH (1,1) model when the 30-year Treasury bond futures return is considered.

Forecasting the Distribution of Economic Variables in a Data-Rich Environment

This article investigates the relevance of considering a large number of macroeconomic indicators to forecast the complete distribution of a variable. The baseline time series model is a semiparametric specification based on the quantile autoregressive (QAR) model that assumes that the quantiles depend on the lagged values of the variable. We then augment the time series model with macroeconomic information from a large dataset by including principal components or a subset of variables selected by LASSO. We forecast the distribution of the h-month growth rate for four economic variables from 1975 to 2011 and evaluate the forecast accuracy relative to a stochastic volatility model using the quantile score. The results for the output and employment measures indicate that the multivariate models outperform the time series forecasts, in particular at long horizons and in tails of the distribution, while for the inflation variables the improved performance occurs mostly at the 6-month horizon. We also illustrate the practical relevance of predicting the distribution by considering forecasts at three dates during the last recession.

Are macroeconomic variables useful for forecasting the distribution of U.S. inflation

Much of the inflation forecasting literature examines the ability of macroeconomic indicators to predict the mean inflation accurately. For the period after 1984, the existing empirical evidence largely suggests that the likelihood of predicting inflation accurately using macroeconomic indicators is no better than a random walk model. We expand the scope of inflation predictability by exploring whether macroeconomic indicators are useful in predicting the distribution of inflation. We consider six commonly-used macro indicators and core/non-core versions of the Consumer Price Index (CPI) and the Personal Consumption Expenditure (PCE) deflator as measures of inflation. Based on monthly data and for the forecast period after 1984, we find that some of the macro indicators, such as the unemployment rate, housing starts and the term spread, provide significant out-of-sample predictability for the distribution of core inflation. An analysis of the quantiles of the predictive distribution reveals interesting patterns which would otherwise be ignored by existing inflation forecasting approaches that rely only on forecasting the mean. We also illustrate the importance of inflation distribution forecasting in evaluating some events which are of policy interest by focusing on predicting the likelihood of deflation.

A Semiparametric Analysis of Gasoline Demand in the US: Reexamining The Impact of Price

The evaluation of the impact of an increase in gasoline tax on demand relies crucially on the estimate of the price elasticity. This paper presents an extended application of the Partially Linear Additive Model (PLAM) to the analysis of gasoline demand using a panel of US households, focusing mainly on the estimation of the price elasticity. Unlike previous semi-parametric studies that use household-level data, we work with vehicle-level data within households that can potentially add richer details to the price variable. Both households and vehicles data are obtained from the Residential Transportation Energy Consumption Survey (RTECS) of 1991 and 1994, conducted by the US Energy Information Administration (EIA). As expected, the derived vehicle-based gasoline price has significant dispersion across the country and across grades of gasoline. By using a PLAM specification for gasoline demand, we obtain a measure of gasoline price elasticity that circumvents the implausible price effects reported in earlier studies. In particular, our results show the price elasticity ranges between −0.2, at low prices, and −0.5, at high prices, suggesting that households might respond differently to price changes depending on the level of price. In addition, we estimate separately the model to households that buy only regular gasoline and those that buy also midgrade/premium gasoline. The results show that the price elasticities for these groups are increasing in price and that regular households are more price sensitive compared to non-regular.

Are Professional Forecasters Bayesian

I evaluate whether expectations of professional forecasters are consistent with the property of Bayesian learning that the expected uncertainty of a fixed target forecast should decline with the horizon. I obtain a measure of individual uncertainty from the density forecasts of the Survey of Professional Forecasters (SPF) and the ECB-SPF and use it to test the prediction of the learning model. Empirically, I find that the prediction is often violated, in particular when forecasters experience unexpected news in the most recent data release, and following quarters in which they produce narrow forecasts. In addition, I find significant heterogeneity in the updating behavior of forecasters in response to changes in these variables.