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

A Multi-Step Forecast Density

This paper makes two contribution to the literature on density forecasts. First, we propose a novel bootstrap approach to estimate forecasting densities based on nonparametric techniques. The method is based on the Markov Bootstrap that is suitable to resample dependent data. The combination of nonparametric and bootstrap methods delivers density forecasts that are flexible in capturing markovian dependence (linear and nonlinear) occurring in any moment of the distribution. Second, we improve the testing approach to evaluate density forecasts by considering a set of tests for dynamical misspecification such as autocorrelation, heteroskedasticity and neglected nonlinearity. The approach is useful because rejections of the tests give insights into ways to improve the forecasting model. By Monte Carlo simulations we show that the proposed evaluation strategy has much higher power to detect misspecification of the density forecasts compared to previous analysis. The proposed nonparametric-bootstrap forecasting method exhibits the ability to capture correctly the dynamics of linear and nonlinear time series models. We also investigate the performance at higher orders and propose methods to deal with the \u201ccurse of dimensionality\u201d. Finally, we empirically investigate the relevance of the method in out-of-sample forecasting the density of 3 business cycles variables for the US: real GDP, the Coincident Indicator and Industrial Production. The results indicate that the method gives reliable density forecasts for all variables and performs better compared to parametric forecasting methods.

Testing for Nonlinear Structure and Chaos in Economic Time Series: A Comment

This short paper is a comment on ``Testing for Nonlinear Structure and Chaos in Economic Time Series'' by Catherine Kyrtsou and Apostolos Serletis. We summarize their main results and discuss some of their conclusions concerning the role of outliers and noisy chaos. In particular, we include some new simulations to investigate whether economic time series may be characterized by low dimensional noisy chaos.

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

Behavioral Heterogeneity in Stock Prices

We estimate a dynamic asset pricing model characterized by heterogeneous boundedly rational agents. The fundamental value of the risky asset is publicly available to all agents, but they have different beliefs about the persistence of deviations of stock prices from the fundamental benchmark. An evolutionary selection mechanism based on relative past profits governs the dynamics of the fractions and switching of agents between different beliefs or forecasting strategies. A strategy attracts more agents if it performed relatively well in the recent past compared to other strategies. We estimate the model to annual US stock price data from 1871 until 2003. The estimation results support the existence of two expectation regimes, and a bootstrap F-test rejects linearity in favor of our nonlinear two-type heterogeneous agent model. One regime can be characterized as a fundamentalists regime, because agents believe in mean reversion of stock prices toward the benchmark fundamental value. The second regime can be characterized as a chartist, trend following regime because agents expect the deviations from the fundamental to trend. The fractions of agents using the fundamentalists and trend following forecasting rules show substantial time variation and switching between predictors. The model offers an explanation for the recent stock prices run-up. Before the 90s the trend following regime was active only occasionally. However, in the late 90s the trend following regime persisted and created an extraordinary deviation of stock prices from the fundamentals. Recently, the activation of the mean reversion regime has contributed to drive stock prices back closer to their fundamental valuation.

Nonlinear Mean Reversion in Stock Prices

We investigate evidence for nonlinear mean reversion in yearly S\&P500 data from 1871 until 2001. We find that up to 1990 there is significant evidence of nonlinear mean reversion. In particular, stock prices are characterized by a persistent process close to the fundamental value. However, when prices deviate significantly a mean reverting regime is activated and prices adjust to fundamental values. Instead, the stock price run-up of the late 90s exacerbated the persistence of the deviations and there is no evidence for a mean reverting regime that drives prices back to fundamentals.nonlinear time series, mean reversion

Multi-criteria energy and daylighting optimization for an office with fixed and moveable shading devices

This paper presents an optimization approach to design an external fixed shading device protecting an energy efficient office from high sun loads. The developed methodology takes into account heating, cooling and energy required for lighting appliances, along with the interaction with an internal moveable venetian blind for direct sunlight protection. The optimization process considers whole-year simulations performed with different software codes, specifically ESP-r for energy calculation and DAYSIM\uae for daylighting analysis, while the modeFRONTIER\uae tool synchronizes the simulations and drives the optimization for searching optimal solutions. The fixed shading device is a flat panel positioned parallel to the window and inclined by its horizontal axis and the optimization variables change the size, inclination and position of the device with respect to the building fa\ue7ade. Two exposures are considered, south and south-west, and the optimized results are reported as a Pareto front highlighting the performance of different solutions, comparing the energy and daylighting performance of the offic