nep-for New Economics Papers
on Forecasting
Issue of 2020‒11‒09
ten papers chosen by
Rob J Hyndman
Monash University

  1. A comparison of monthly global indicators for forecasting growth By Christiane Baumeister; Pierre Guérin
  2. Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models By Christian Glocker; Serguei Kaniovski
  3. Forecasting Economic Activity Using the Yield Curve: Quasi-Real-Time Applications for New Zealand, Australia and the US By Todd Henry; Peter C.B. Phillips
  4. Time-Varying Risk Aversion and Forecastability of the US Term Structure of Interest Rates By Elie Bouri; Rangan Gupta; Anandamayee Majumdar; Sowmya Subramaniam
  5. Time-varying Forecast Combination for High-Dimensional Data By Bin Chen; Kenwin Maung
  6. Forecasting Consumer Price Index Inflation in India: Vector Error Correction Mechanism Vs. Dynamic Factor Model Approach for Non-Stationary Time Series. By Bhattacharya, Rudrani; Kapoor, Mrigankshi
  7. Real-time forecasting of the Australian macroeconomy using flexible Bayesian VARs By Bo Zhang; Bao H. Nguyen
  8. Selective Attention in Exchange Rate Forecasting By Svatopluk Kapounek; Zuzana Kucerova; Evzen Kocenda
  9. The loss optimisation of loan recovery decision times using forecast cash flows By Arno Botha; Conrad Beyers; Pieter de Villiers
  10. High Dimensional Forecast Combinations Under Latent Structures By Zhentao Shi; Liangjun Su; Tian Xie

  1. By: Christiane Baumeister; Pierre Guérin
    Abstract: This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world GDP using mixed-frequency models. We find that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecast accuracy, while other monthly measures have more mixed success. This global economic conditions indicator contains valuable information also for assessing the current and future state of the economy for a set of individual countries and groups of countries. We use this indicator to track the evolution of the nowcasts for the US, the OECD area, and the world economy during the coronavirus pandemic and quantify the main factors driving the nowcasts.
    Keywords: MIDAS models, global economic conditions, world GDP growth, nowcasting, forecasting, mixed frequency
    JEL: C22 C52 E37
    Date: 2020–10
  2. By: Christian Glocker; Serguei Kaniovski
    Abstract: We propose a modelling approach involving a series of small-scale factor models. They are connected to each other within a cluster, whose linkages are derived from Granger-causality tests. GDP forecasts are established across the production, income and expenditure accounts within a disaggregated approach. This method merges the benefits of large-scale macroeconomic and small-scale factor models, rendering our Cluster of Dynamic Factor Models (CDFM) useful for model-consistent forecasting on a large scale. While the CDFM has a simple structure, its forecasts outperform those of a wide range of competing models and of professional forecasters. Moreover, the CDFM allows forecasters to introduce their own judgment and hence produce conditional forecasts.
    Keywords: Forecasting, Dynamic factor model, Granger causality, Structural modeling
    Date: 2020–10–27
  3. By: Todd Henry (University of Auckland); Peter C.B. Phillips (Cowles Foundation, Yale University)
    Abstract: Inversion of the yield curve has come to be viewed as a leading recession indicator. Unsurprisingly, some recent instances of inversion have attracted attention from economic commentators and policymakers about possible impending recessions. Using a variety of time series models and recent innovations in econometric method, this paper conducts quasi-real-time forecasting exercises to investigate whether the predictive capability of the yield curve extends to forecasting economic activity in general and whether removing the term premium component from yields affects forecast accuracy. The empirical ï¬ ndings for the US, Australia, and New Zealand show that forecast performance is not improved either by augmenting simplistic models with information from the yield curve or by making such a decomposition of yields. Results from similar research exercises in previous work in the literature are mixed. The results of the present analysis suggest possible explanations that reconcile these conflicting results.
    Keywords: Forecasting, Inversion, Recession indicator, Yield curve
    JEL: C53 E43
    Date: 2020–10
  4. By: Elie Bouri (Adnan Kassar School of Business, Lebanese American University, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Anandamayee Majumdar (Department of Physical Sciences, School of Engineering, Technology & Sciences, Independent University, Bangladesh, Dhaka 1229, Bangladesh); Sowmya Subramaniam (Indian Institute of Management Lucknow, Prabandh Nagar off Sitapur Road, Lucknow, Uttar Pradesh 226013, India)
    Abstract: In this paper, we analyse the forecasting ability of a time-varying metric of daily risk aversion for the entire term structure of interest rates of Treasury securities of the United States (US) as reflected by the three latent factors, level, slope and curvature. Using daily data covering the out-of-sample period 22nd June, 1988 to 3rd September, 2020 (given the in-sample period 30th May, 1986 to 21st June, 1988) within a quantiles-based framework, the results show statistically significant forecasting gains emanating from risk aversion for the tails of the conditional distributions of the level, slope and curvature factors at horizons of one-day, one-week, and one-month-ahead. Interestingly, a conditional mean-based model fails to detect any evidence of out-of-sample predictability. Our findings have important implications for academics, bond investors, and policymakers in their quest to better understand the evolution of future movement in US Treasury securities.
    Keywords: Yield Curve Factors, Risk Aversion, Out-of-Sample Forecasts
    JEL: C22 C53 E43 G12 G17
    Date: 2020–10
  5. By: Bin Chen; Kenwin Maung
    Abstract: In this paper, we propose a new nonparametric estimator of time-varying forecast combination weights. When the number of individual forecasts is small, we study the asymptotic properties of the local linear estimator. When the number of candidate forecasts exceeds or diverges with the sample size, we consider penalized local linear estimation with the group SCAD penalty. We show that the estimator exhibits the oracle property and correctly selects relevant forecasts with probability approaching one. Simulations indicate that the proposed estimators outperform existing combination schemes when structural changes exist. Two empirical studies on inflation forecasting and equity premium prediction highlight the merits of our approach relative to other popular methods.
    Date: 2020–10
  6. By: Bhattacharya, Rudrani (National Institute of Public Finance and Policy); Kapoor, Mrigankshi (Birla Institute of Technology and Science)
    Abstract: Short to medium term forecasting of inflation rate is important for economic decision making by economic agents and timely implementation of monetary policy. In this study, we develop two alternative forecasting models for Year-on-Year (YOY) inflation in Consumer Price Index (CPI) in India using a large number of macroeconomic indicators. The YOY CPI inflation and its predictive indicators are found to be non-stationary and cointegrated. To address this issue, we employ Vector Error Correction Model (VECM) and Dynamic Factor Model (DFM) modified for non-stationary time series to forecast CPI inflation. We find that in terms of Root Mean Square Error (RMSE), the VECM model performs marginally better than the DFM model. However, both models are found to have the same predictive accuracy using Diebold-Mariano test.
    Keywords: CPI Inflation ; India ; Forecasting ; Vector Error Correction Model ; Dynamic Factor Model
    JEL: C32 C53
    Date: 2020–10
  7. By: Bo Zhang; Bao H. Nguyen
    Abstract: This paper evaluates the real-time forecast performance of alternative Bayesian Vector Autoregressive (VAR) models for the Australian macroeconomy. To this end, we construct an updated vintage database and estimate a set of model specifications with different covariance structures. The results suggest that a large VAR model with 20 variables tends to outperform a small VAR model when forecasting GDP growth, CPI inflation and unemployment rate. We find consistent evidence that the models with more flexible error covariance structures forecast GDP growth and inflation better than the standard VAR, while the standard VAR does better than its counterparts for unemployment rate. The results are robust under alternative priors and when the data includes the early stage of the COVID-19 crisis.
    Keywords: Australia, real-time forecast, Non-Gaussian, Stochastic Volatility
    JEL: C11 C32 C53 C55
    Date: 2020–10
  8. By: Svatopluk Kapounek (Mendel University in Brno, Faculty of Business and Economics, Brno, Czech Republic); Zuzana Kucerova (Mendel University in Brno, Faculty of Business and Economics); Evzen Kocenda (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic; Institute of Information Theory and Automation, Czech Academy of Sciences, Prague, Czech Republic; CESifo, Munich, IOS, Regensburg)
    Abstract: We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market participants process only a limited amount of information. Our analysis includes more than 100,000 news articles relevant to the six most-traded foreign exchange currency pairs for the period of 1979–2016. We employ a dynamic model averaging approach to reduce model selection uncertainty and to identify time-varying probability to include regressors in our models. Our results show that smaller sizes models accounting for the presence of selective attention offer improved fitting and forecasting results. Specifically, we document a growing impact of foreign trade and monetary policy news on the euro/dollar exchange rate following the global financial crisis. Overall, our results point to the existence of selective attention in the case of most currency pairs.
    Keywords: exchange rate; selective attention; news; forecasting; dynamic model averaging
    JEL: F33 C11
    Date: 2020–10
  9. By: Arno Botha; Conrad Beyers; Pieter de Villiers
    Abstract: A theoretical method is empirically illustrated in finding the best time to forsake a loan such that the overall credit loss is minimised. This is predicated by forecasting the future cash flows of a loan portfolio up to the contractual term, as a remedy to the inherent right-censoring of real-world `incomplete' portfolios. Two techniques, a simple probabilistic model as well as an eight-state Markov chain, are used to forecast these cash flows independently. We train both techniques from different segments within residential mortgage data, provided by a large South African bank, as part of a comparative experimental framework. As a result, the recovery decision's implied timing is empirically illustrated as a multi-period optimisation problem across uncertain cash flows and competing costs. Using a delinquency measure as a central criterion, our procedure helps to find a loss-optimal threshold at which loan recovery should ideally occur for a given portfolio. Furthermore, both the portfolio's historical risk profile and forecasting thereof are shown to influence the timing of the recovery decision. This work can therefore facilitate the revision of relevant bank policies or strategies towards optimising the loan collections process, especially that of secured lending.
    Date: 2020–10
  10. By: Zhentao Shi; Liangjun Su; Tian Xie
    Abstract: This paper presents a novel high dimensional forecast combination estimator in the presence of many forecasts and potential latent group structures. The new algorithm, which we call $\ell_2$-relaxation, minimizes the squared $\ell_2$-norm of the weight vector subject to a relaxed version of the first-order conditions, instead of minimizing the mean squared forecast error as those standard optimal forecast combination procedures. A proper choice of the tuning parameter achieves bias and variance trade-off, and incorporates as special cases the simple average (equal-weight) strategy and the conventional optimal weighting scheme. When the variance-covariance (VC) matrix of the individual forecast errors exhibits latent group structures -- a block equicorrelation matrix plus a VC for idiosyncratic noises, $\ell_2$-relaxation delivers combined forecasts with roughly equal within-group weights. Asymptotic optimality of the new method is established by exploiting the duality between the sup-norm restriction and the high-dimensional sparse $\ell_1$-norm penalization. Excellent finite sample performance of our method is demonstrated in Monte Carlo simulations. Its wide applicability is highlighted in three real data examples concerning empirical applications of microeconomics, macroeconomics and finance.
    Date: 2020–10

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