nep-for New Economics Papers
on Forecasting
Issue of 2016‒09‒18
four papers chosen by
Rob J Hyndman
Monash University

  1. Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility By Francis X. Diebold; Frank Schorfheide; Minchul Shin
  2. Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks By Zeineb Affes; Rania Hentati-Kaffel
  3. Forecasting US GNP Growth: The Role of Uncertainty By Mawuli Segnon; Rangan Gupta; Stelios Bekiros; Mark E. Wohar
  4. Testing the Predictability of Consumption Growth: Evidence from China By Liping Gao; Hyeongwoo Kim

  1. By: Francis X. Diebold; Frank Schorfheide; Minchul Shin
    Abstract: Recent work has analyzed the forecasting performance of standard dynamic stochastic general equilibrium (DSGE) models, but little attention has been given to DSGE models that incorporate nonlinearities in exogenous driving processes. Against that background, we explore whether incorporating stochastic volatility improves DSGE forecasts (point, interval, and density). We examine real-time forecast accuracy for key macroeconomic variables including output growth, inflation, and the policy rate. We find that incorporating stochastic volatility in DSGE models of macroeconomic fundamentals markedly improves their density forecasts, just as incorporating stochastic volatility in models of financial asset returns improves their density forecasts.
    JEL: E17
    Date: 2016–09
  2. By: Zeineb Affes (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Rania Hentati-Kaffel (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)
    Abstract: In this paper, we compare the performance of two non-parametric methods of classification, Regression Trees (CART) and the newly Multivariate Adaptive Regression Splines (MARS) models, in forecasting bankruptcy. Models are implemented on a large universe of US banks over a complete market cycle and running under a K-Fold Cross validation. A hybrid model which combines K-means clustering and MARS is tested as well. Our findings highlight that i) Either in training or testing sample, MARS provides, in average, better correct classification rate than CART model, ii) Hybrid approach significantly enhances the classification accuracy rate for both the training and the testing samples, iii) MARS prediction underperforms when the misclassification rate is adopted as a criteria, iv) Results proves that Non-parametric models are more suitable for bank failure prediction than the corresponding Logit model.
    Keywords: Bankruptcy prediction,MARS,CART,K-means,Early-Warning System
    Date: 2016–03
  3. By: Mawuli Segnon (Department of Economics, University of Münster, Germany); Rangan Gupta (Department of Economics, University of Pretoria, South Africa); Stelios Bekiros (Department of Economics, European University Institute, Florence, Italy); Mark E. Wohar (Department of Economics, University of Nebraska, Omaha, USA and School of Business and Economics, Loughborough University, UK)
    Abstract: There are a large number of models developed in the literature to analyse and forecast the changes in output dynamics. The objective of this paper is to compare the forecasting ability of uni- and bivariate models in terms of forecasting U.S. GNP growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as the superior predictive ability (SPA) and equal accuracy tests, we evaluate the forecasting performance of our models over the quarterly period of 1919:2-2014:4, given an in-sample of 1900:1 1919:1. We find that the economic policy uncertainty index should be improving the accuracy of U.S. GNP growth forecasts in the bivariate models. While we find that the Markov switching time varying parameter VAR (MS-TVP-VAR) models in most cases cannot be outperformed its competitive models according to the root mean squared error (RMSE), the density forecast measure reveals that the Bayesian VAR (BVAR) model with stochastic volatility in most cases is the model that produces more accurate forecasts. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rate.
    Keywords: Forecast comparison, vector autoregressive models, US GNP, Economic Policy Uncertainty
    JEL: C22 C32 E32 E37
    Date: 2016–09
  4. By: Liping Gao; Hyeongwoo Kim
    Abstract: Using time series macroeconomic data, Chow (1985, 2010, 2011) reported indirect empirical evidence that implies the validity of the permanent income hypothesis in China. We revisit this issue by evaluating direct measures of the predictability of consumption growth in China during the post-economic reform regime (1978-2009). We also implement and report similar analysis for the postwar US data for comparison. Our in-sample analysis provides strong evidence against the PIH for both countries. Out-of-sample forecast exercises show that consumption changes are highly predictable, which sharply contrasts the implications of empirical findings by Chow (1985, 2010, 2011).
    Keywords: Permanent Income Hypothesis; Consumption; Out-of-Sample Forecast; Diebold-Mariano-West Statistic
    JEL: E21 E27
    Date: 2016–09

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