nep-for New Economics Papers
on Forecasting
Issue of 2009‒04‒05
three papers chosen by
Rob J Hyndman
Monash University

  1. Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation? By Manzan, Sebastiano; Zerom, Dawit
  2. Evolution of Subjective Hurricane Risk Perceptions: A Bayesian Approach By David Kelly; David Letson; Forest Nelson; David S. Nolan; Daniel Solis
  3. Unemployment and inflation in Western Europe: solution by the boundary element method By Kitov, Ivan; Kitov, Oleg

  1. By: Manzan, Sebastiano; Zerom, Dawit
    Abstract: 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.
    Keywords: Conditional quantiles; Distribution; Inflation; Predictability; Phillips curve; Combining
    JEL: C53 E31 E52 C22
    Date: 2009–01–30
  2. By: David Kelly (Department of Economics, University of Miami); David Letson (Rosenstiel School of Marine and Atmospheric Science, University of Miami); Forest Nelson (Department of Economics, Henry B. Tippie College of Business Administration, University of Iowa); David S. Nolan (Rosenstiel School of Marine and Atmospheric Science, University of Miami); Daniel Solis (Rosenstiel School of Marine and Atmospheric Science, University of Miami)
    Abstract: This paper studies how individuals update subjective risk perceptions in response to hurricane track forecast information, using a unique data set from an event market, the Hurricane Futures Market (HFM). We derive a theoretical Bayesian framework which predicts how traders update their perceptions of the probability of a hurricane making landfall in a certain range of coastline. Our results suggest that traders behave in a way consistent with Bayesian updating but this behavior is based on the perceived quality of the information received.
    Keywords: risk perceptions, learning, Bayesian learning, event markets, prediction markets, favorite-longshot bias, hurricanes
    JEL: D83 C53 G14 C9
    Date: 2009–02–27
  3. By: Kitov, Ivan; Kitov, Oleg
    Abstract: Using an analog of the boundary element method in engineering and science, we analyze and model unemployment rate in Austria, Italy, the Netherlands, Sweden, Switzerland, and the United States as a function of inflation and the change in labor force. Originally, the model linking unemployment to inflation and labor force was developed and successfully tested for Austria, Canada, France, Germany, Japan, and the United States. Autoregressive properties of neither of these variables are used to predict their evolution. In this sense, the model is a self-consistent and completely deterministic one without any stochastic component (external shocks) except that associated with measurement errors and changes in measurement units. Nevertheless, the model explains between ~65% and ~95% of the variability in unemployment and inflation. For Italy, the rate of unemployment is predicted at a time horizon of nine (!) years with pseudo out-of-sample root-mean-square forecasting error of 0.55% for the period between 1973 and 2006. One can expect that the unemployment will be growing since 2008 and will reach ~11.4% [±0.6 %] near 2012. After 2012, unemployment in Italy will start to descend.
    Keywords: unemployment; inflation; labor force; boundary integral method; prediction; Western Europe
    JEL: E31 E24 J21 J64 J11
    Date: 2009–03–29

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