Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?

Much of the US inflation forecasting literature deals with examining the ability of macroeconomic indicators to predict the mean of future inflation, and the overwhelming evidence suggests that the macroeconomic indicators provide little or no predictability. In this paper, we expand the scope of inflation predictability and explore whether macroeconomic indicators are useful in predicting the distribution of future inflation. To incorporate macroeconomic indicators into the prediction of the conditional distribution of future inflation, we introduce a semi-parametric approach using conditional quantiles. The approach offers more flexibility in capturing the possible role of macroeconomic indicators in predicting the different parts of the future inflation distribution. Using monthly data on US inflation, we find that unemployment rate, housing starts, and the term spread provide significant out-of-sample predictability for the distribution of core inflation. Importantly, this result is obtained for a forecast evaluation period that we intentionally chose to be after 1984, when current research shows that macroeconomic indicators do not add much to the predictability of the future mean inflation. This paper discusses various findings using forecast intervals and forecast densities, and highlights the unique insights that the distribution approach offers, which otherwise would be ignored if we relied only on mean forecasts.Conditional quantiles; Distribution; Inflation; Predictability; Phillips curve; Combining

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.semiparametric methods; partially linear additive model; gasoline demand

Does liquidity in the FX market depend on volatility?

We re-examine the relationship between exchange rates and order flow as proposed by Evans and Lyons (2002). Compared to their linear specification, we find that the response of exchange rates to order flow may depend on market historical volatility. If market historical volatility is high, a given order seems to have a lower price impact than in calmer periods. Overall, our simple threshold mechanism has the power to produce higher correlation coefficients.exchange rate dynamics

Forecasting Loan Default in Europe with Machine Learning

We use a dataset of 12 million residential mortgages to investigate the loan default behavior in several European countries. We model the default occurrence as a function of borrower characteristics, loan-specific variables, and local economic conditions. We compare the performance of a set of machine learning algorithms relative to the logistic regression, finding that they perform significantly better in providing predictions. The most important variables in explaining loan default are the interest rate and the local economic characteristics. The existence of relevant geographical heterogeneity in the variable importance points at the need for regionally tailored risk-assessment policies in Europe

Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?

Much of the US inflation forecasting literature deals with examining the ability of macroeconomic indicators to predict the mean of future inflation, and the overwhelming evidence suggests that the macroeconomic indicators provide little or no predictability. In this paper, we expand the scope of inflation predictability and explore whether macroeconomic indicators are useful in predicting the distribution of future inflation. To incorporate macroeconomic indicators into the prediction of the conditional distribution of future inflation, we introduce a semi-parametric approach using conditional quantiles. The approach offers more flexibility in capturing the possible role of macroeconomic indicators in predicting the different parts of the future inflation distribution. Using monthly data on US inflation, we find that unemployment rate, housing starts, and the term spread provide significant out-of-sample predictability for the distribution of core inflation. Importantly, this result is obtained for a forecast evaluation period that we intentionally chose to be after 1984, when current research shows that macroeconomic indicators do not add much to the predictability of the future mean inflation. This paper discusses various findings using forecast intervals and forecast densities, and highlights the unique insights that the distribution approach offers, which otherwise would be ignored if we relied only on mean forecasts

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

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 Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?

Much of the US inflation forecasting literature deals with examining the ability of macroeconomic indicators to predict the mean of future inflation, and the overwhelming evidence suggests that the macroeconomic indicators provide little or no predictability. In this paper, we expand the scope of inflation predictability and explore whether macroeconomic indicators are useful in predicting the distribution of future inflation. To incorporate macroeconomic indicators into the prediction of the conditional distribution of future inflation, we introduce a semi-parametric approach using conditional quantiles. The approach offers more flexibility in capturing the possible role of macroeconomic indicators in predicting the different parts of the future inflation distribution. Using monthly data on US inflation, we find that unemployment rate, housing starts, and the term spread provide significant out-of-sample predictability for the distribution of core inflation. Importantly, this result is obtained for a forecast evaluation period that we intentionally chose to be after 1984, when current research shows that macroeconomic indicators do not add much to the predictability of the future mean inflation. This paper discusses various findings using forecast intervals and forecast densities, and highlights the unique insights that the distribution approach offers, which otherwise would be ignored if we relied only on mean forecasts

Data Science Technologies in Economics and Finance: A Gentle Walk-In

AbstractThis chapter is an introduction to the use of data science technologies in the fields of economics and finance. The recent explosion in computation and information technology in the past decade has made available vast amounts of data in various domains, which has been referred to as Big Data. In economics and finance, in particular, tapping into these data brings research and business closer together, as data generated in ordinary economic activity can be used towards effective and personalized models. In this context, the recent use of data science technologies for economics and finance provides mutual benefits to both scientists and professionals, improving forecasting and nowcasting for several kinds of applications. This chapter introduces the subject through underlying technical challenges such as data handling and protection, modeling, integration, and interpretation. It also outlines some of the common issues in economic modeling with data science technologies and surveys the relevant big data management and analytics solutions, motivating the use of data science methods in economics and finance

CAS: Centre for advanced studies

An introduction to the Centre for Advanced Studies.JRC.A.5-Scientific Developmen