nep-for New Economics Papers
on Forecasting
Issue of 2016‒11‒27
six papers chosen by
Rob J Hyndman
Monash University

  1. Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks By Jari Hännikäinen
  2. Economic Forecasting in Theory and Practice : An Interview with David F. Hendry By Neil R. Ericsson
  3. Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments By Mikhail Anufriev; Cars Hommes; Tomasz Makarewicz
  4. "Forecasting the Forecasts of Others:" Expectational Heterogeneity and Aggregate Dynamics By Antulio N. Bomfim
  5. Value-at-Risk Prediction in R with the GAS Package By David Ardia; Kris Boudt; Leopoldo Catania
  6. The Usefulness of the Median CPI in Bayesian VARs Used for Macroeconomic Forecasting and Policy By Meyer, Brent; Zaman, Saeed

  1. By: Jari Hännikäinen (School of Management, University of Tampere)
    Abstract: In this paper, we analyze the forecasting performance of a set of widely used window selection methods in the presence of data revisions and recent structural breaks. Our Monte Carlo and empirical results for U.S. real GDP and inflation show that the expanding window estimator often yields the most accurate forecasts after a recent break. It performs well regardless of whether the revisions are news or noise, or whether we forecast first-release or final values. We find that the differences in the forecasting accuracy are large in practice, especially when we forecast inflation after the break of the early 1980s.
    Keywords: Recent structural break, choice of estimation window, forecasting, real-time data
    JEL: C22 C53 C82
    Date: 2016–11
  2. By: Neil R. Ericsson
    Abstract: David Hendry has made major contributions to many areas of economic forecasting. He has developed a taxonomy of forecast errors and a theory of unpredictability that have yielded valuable insights into the nature of forecasting. He has also provided new perspectives on many existing forecast techniques, including mean square forecast errors, add factors, leading indicators, pooling of forecasts, and multi-step estimation. In addition, David has developed new forecast tools, such as forecast encompassing; and he has improved existing ones, such as nowcasting and robustification to breaks. This interview for the International Journal of Forecasting explores David Hendry’s research on forecasting.
    Keywords: Encompassing ; Equilibrium correction models ; Error correction ; Evaluation ; Exogeneity ; Forecasting ; Modeling ; Nowcasting ; Parameter constancy ; Robustification ; Structural breaks
    JEL: C53
    Date: 2016–11
  3. By: Mikhail Anufriev (Economics Discipline Group, University of Technology, Sydney); Cars Hommes (CeNDEF, University of Amsterdam); Tomasz Makarewicz (CeNDEF, University of Amsterdam)
    Abstract: We study a model in which individual agents use simple linear first order price forecasting rules, adapting them to the complex evolving market environment with a smart Genetic Algorithm optimization procedure. The novelties are: (1) a parsimonious experimental foundation of individual forecasting behaviour; (2) an explanation of individual and aggregate behavior in four different experimental settings, (3) improved one-period and 50-period ahead forecasting of lab experiments, and (4) a characterization of the mean, median and empirical distribution of forecasting heuristics. The median of the distribution of GA forecasting heuristics can be used in designing or validating simple Heuristic Switching Model.
    Keywords: Expectation Formation; Learning to Forecast Experiment; Genetic Algorithm Model of Individual Learning
    JEL: C53 C63 C91 D03 D83 D84
    Date: 2015–07–13
  4. By: Antulio N. Bomfim
    Abstract: I construct a dynamic general equilibrium model where agents differ in the way they form expectations. Sophisticated agents form model-consistent expectations. Rule-of-thumb agents' expectations are based on an intuitive forecasting rule. All agents solve standard dynamic optimization problems and face strategic complementarity in production. Extending the work of Haltiwanger and Waldman (1989), I show that even a minority of rule-of-thumb forecasters can have a significant effect on the aggregate properties of the economy. For instance, as agents try to forecast each others' behavior they effectively strengthen the internal propagation mechanism of the economy. I solve the model by assuming a hierarchical information structure similar to the one in Townsend's (1983) model of informationally dispersed markets. The quantitative results are obtained by calibrating the model and running a battery of sensitivity tests on key parameters. The analysis highlights the role of strategic complementarity in the heterogeneous expectations literature and precisely quantify many qualitative claims about the aggregate implications of expectational heterogeneity.
    Keywords: Business cycles ; expectatations ; strategic complementarity ; bounded rationality
  5. By: David Ardia; Kris Boudt; Leopoldo Catania
    Abstract: GAS models have been recently proposed in time-series econometrics as valuable tools for signal extraction and prediction. This paper details how financial risk managers can use GAS models for Value-at-Risk (VaR) prediction using the novel GAS package for R. Details and code snippets for prediction, comparison and backtesting with GAS models are presented. An empirical application considering Dow Jones Index constituents investigates the VaR forecasting performance of GAS models.
    Date: 2016–11
  6. By: Meyer, Brent (Federal Reserve Bank of Atlanta); Zaman, Saeed (Federal Reserve Bank of Cleveland)
    Abstract: In this paper we investigate the forecasting performance of the median Consumer Price Index (CPI) in a variety of Bayesian vector autoregressions (BVARs) that are often used for monetary policy. Until now, the use of trimmed-mean price statistics in forecasting inflation has often been relegated to simple univariate or Phillips curve approaches, thus limiting their usefulness in applications that require consistent forecasts of multiple macro variables. We find that inclusion of an extreme trimmed-mean measure—the median CPI—improves the forecasts of both core and headline inflation (CPI and personal consumption expenditures) across our set of monthly and quarterly BVARs. Although the inflation forecasting improvements are perhaps not surprising given the current literature on core inflation statistics, we also find that inclusion of the median CPI improves the forecasting accuracy of the central bank's primary instrument for monetary policy: the federal funds rate. We conclude with a few illustrative exercises that highlight the usefulness of using the median CPI.
    Keywords: inflation forecasting; trimmed-mean estimators; Bayesian vector autoregression; conditional forecasting
    JEL: C11 E31 E37 E52
    Date: 2016–11–01

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