nep-for New Economics Papers
on Forecasting
Issue of 2018‒03‒19
six papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting European Economic Policy Uncertainty By Stavros Degiannakis; George Filis
  2. The Impact of Big Data on Firm Performance: An Empirical Investigation By Patrick Bajari; Victor Chernozhukov; Ali Hortaçsu; Junichi Suzuki
  3. Term structure and real-time learning By Pablo Aguilar; Jesús Vázquez
  4. The perils of approximating fixed-horizon inflation forecasts with fixed-event forecasts By James Yetman
  5. Is the US Phillips Curve Stable? Evidence from Bayesian VARs By Karlsson, Sune; Österholm, Pär
  6. Forecasting dynamically asymmetric fluctuations of the U.S. business cycle By Emilio Zanetti Chini

  1. By: Stavros Degiannakis (Department of Economics and Regional Development, Panteion University of Social and Political Sciences); George Filis (Department of Accounting, Finance and Economics, Bournemouth University)
    Abstract: Forecasting the economic policy uncertainty in Europe is of paramount importance given the on-going debt crisis and the Brexit vote. This paper evaluates monthly out-of-sample economic policy uncertainty index forecasts and examines whether ultra-high frequency information from asset market volatilities and global economic policy uncertainty can improve the forecasts relatively to the no-change forecast. The results show that the global economic policy uncertainty provides the highest predictive gains, followed by the European and US stock market volatilities. The results hold true even when we consider the directional accuracy.
    Keywords: Economic policy uncertainty; forecasting; financial markets; commodities markets; HAR; ultra-high frequency information
    JEL: C22 C53 E60 E66 G10
    Date: 2018–03
  2. By: Patrick Bajari; Victor Chernozhukov; Ali Hortaçsu; Junichi Suzuki
    Abstract: In academic and policy circles, there has been considerable interest in the impact of “big data” on firm performance. We examine the question of how the amount of data impacts the accuracy of Machine Learned models of weekly retail product forecasts using a proprietary data set obtained from Amazon. We examine the accuracy of forecasts in two relevant dimensions: the number of products (N), and the number of time periods for which a product is available for sale (T). Theory suggests diminishing returns to larger N and T, with relative forecast errors diminishing at rate 1/√N+1/√T. Empirical results indicate gains in forecast improvement in the T dimension; as more and more data is available for a particular product, demand forecasts for that product improve over time, though with diminishing returns to scale. In contrast, we find an essentially flat N effect across the various lines of merchandise: with a few exceptions, expansion in the number of retail products within a category does not appear associated with increases in forecast performance. We do find that the firm’s overall forecast performance, controlling for N and T effects across product lines, has improved over time, suggesting gradual improvements in forecasting from the introduction of new models and improved technology.
    JEL: C53 L81
    Date: 2018–02
  3. By: Pablo Aguilar (Banco de España); Jesús Vázquez (Universidad del País Vasco (UPV/EHU))
    Abstract: This paper introduces the term structure of interest rates into a medium-scale DSGE model. This extension results in a multi-period forecasting model that is estimated under both adaptive learning and rational expectations. Term structure information enables us to characterize agents’ expectations in real time, which addresses an imperfect information issue mostly neglected in the adaptive learning literature. Relative to the rational expectations version, our estimated DSGE model under adaptive learning largely improves the model fit to the data, which include not just macroeconomic data but also the yield curve and the consumption growth and inflation forecasts reported in the Survey of Professional Forecasters. Moreover, the estimation results show that most endogenous sources of aggregate persistence are dramatically undercut when adaptive learning based on multi-period forecasting is incorporated through the term structure of interest rates.
    Keywords: real-time adaptive learning, term spread, multi-period forecasting, short-versus long-sighted agents, SPF forecasts, medium-scale DSGE model
    JEL: C53 D84 E30 E44
    Date: 2018–01
  4. By: James Yetman
    Abstract: A common practice in studies using inflation forecasts is to approximate fixed-horizon forecasts with fixed-event ones. Here we show that this may be problematic. In a panel of US inflation forecast data that allows us to compare the two, the approximation results in a mean absolute approximation error of around 0.2-0.3 percentage points (around 10% of the level of inflation), and statistically significant differences in both the variances and persistence of the approximate inflation forecasts relative to the actual forecasts. To reduce these problems, we propose an adjustment to the approximation, consistent with a model where longer-horizon forecasts are more heavily "anchored", while shorter-horizon forecasts more closely reflect current inflation levels.
    Keywords: fixed-event forecasts, fixed-horizon forecasts, inflation expectations
    JEL: C43 E31
    Date: 2018–02
  5. By: Karlsson, Sune (Örebro University School of Business); Österholm, Pär (Örebro University School of Business)
    Abstract: Inflation did not fall as much as many economists expected as the Great Recession hit the US economy. One explanation suggested for this phenomenon is that the Phillips curve has become flatter. In this paper we investigate the stability of the US Phillips curve, employing Bayesian VARs to quarterly data from 1990Q1 to 2017Q3. We estimate bivariate models for PCE inflation and the unemployment rate under a number of different assumptions concerning the dynamics and covariance matrix. Specifically, we assess the importance of time-varying parameters and stochastic volatility. Using new tools for model selection, we find support for both time-varying parameters and stochastic volatility. Interpreting the Phillips curve as the inflation equation of our Bayesian VAR, we conclude that the US Phillips curve has been unstable. Our results also indicate that the Phillips curve may have been somewhat flatter between 2005 and 2013 than in the decade preceding that period. However, while the dynamic relations of the model appear to be subject to time variation, we note that the effect of a shock to the unemployment rate on inflation is not fundamentally different over time. Finally, a conditional forecasting exercise suggests that as far as the models are concerned, inflation may not have been unexpectedly high around the Great Recession.
    Keywords: Time-varying parameters; Stochastic volatility; Model selection; Inflation; Unemployment
    JEL: C11 C32 C52 E37
    Date: 2018–03–05
  6. By: Emilio Zanetti Chini (Department of Economics and Management, University of Pavia)
    Abstract: The Generalized Smooth Transition Auto-Regression (GSTAR) parametrizes the joint asymmetry in the duration and length of cycles in macroeconomic time series by using particular generalizations of the logistic function. The symmetric smooth transition and linear auto-regressions are peculiar cases of the new parametrization. A test for the null hypothesis of dynamic symmetry is discussed. Two case studies indicate that dynamic asymmetry is a key feature of the U.S. economy. Our model beats its competitors in point forecasting, but this superiority becomes less evident in density forecasting and in uncertain forecasting environments.
    Keywords: Density forecasts, Econometric modelling, Evaluating forecasts, Generalized logistic, Industrial production, Nonlinear time series, Point forecasts, Statistical tests, Unemployment.
    JEL: C22 C51 C52
    Date: 2018–03

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