nep-for New Economics Papers
on Forecasting
Issue of 2018‒10‒15
seven papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting with Dynamic Panel Data Models By Laura Liu; Hyungsik Roger Moon; Frank Schorfheide
  2. Prediction Regions for Interval-valued Time Series By Gloria Gonzalez-Rivera; Yun Luo; Esther Ruiz
  3. Predictable biases in macroeconomic forecasts and their impact across asset classes By Félix, Luiz; Kräussl, Roman; Stork, Philip
  4. Taking the Cochrane-Piazzesi Term Structure Model Out of Sample: More Data, Additional Currencies, and FX Implications By Robert J. Hodrick; Tuomas Tomunen
  5. Optimal Asset Allocation with Multivariate Bayesian Dynamic Linear Models By Carlos Carvalho; Jared D. Fisher; Davide Pettenuzzo
  6. “A novel measure of consensus for Likert scales” By Oscar Claveria
  7. Efficient generation of time series with diverse and controllable characteristics By Yanfei Kang; Rob J Hyndman; Feng Li

  1. By: Laura Liu; Hyungsik Roger Moon; Frank Schorfheide
    Abstract: This paper considers the problem of forecasting a collection of short time series using cross sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross-sectional information to transform the unit-specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a non-parametric kernel estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated-random-effects distribution as known (ratio-optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions.
    JEL: C11 C14 C23 C53 G21
    Date: 2018–09
  2. By: Gloria Gonzalez-Rivera (Department of Economics, University of California Riverside); Yun Luo (University of California, Riverside); Esther Ruiz (Universidad Carlos III de Madrid)
    Abstract: We approximate probabilistic forecasts for interval-valued time series by offering alternative approaches to construct bivariate prediction regions of the interval center and range (or lower/upper bounds). We estimate a bivariate system of the center/log-range, which may not be normally distributed. Implementing analytical or bootstrap methods, we directly transform prediction regions for center/log-range into those for center/range and upper/lower bounds systems. We propose new metrics to evaluate the regions performance. Monte Carlo simulations show bootstrap methods being preferred even in Gaussian systems. For daily SP500 low/high return intervals, we build joint conditional prediction regions of the return level and return volatility.
    Keywords: Bootstrap, Constrained Regression, Coverage Rates, Logarithmic Transformation, QML estimation
    JEL: C01 C22 C53
    Date: 2018–10
  3. By: Félix, Luiz; Kräussl, Roman; Stork, Philip
    Abstract: This paper investigates how biases in macroeconomic forecasts are associated with economic surprises and market responses across asset classes around US data announcements. We find that the skewness of the distribution of economic forecasts is a strong predictor of economic surprises, suggesting that forecasters behave strategically (rational bias) and possess private information. Our results also show that consensus forecasts of US macroeconomic releases embed anchoring. Under these conditions, both economic surprises and the returns of assets that are sensitive to macroeconomic conditions are predictable. Our findings indicate that local equities and bond markets are more predictable than foreign markets, currencies and commodities. Economic surprises are found to link to asset returns very distinctively through the stages of the economic cycle, whereas they strongly depend on economic releases being inflation- or growth-related. Yet, when forecasters fail to correctly forecast the direction of economic surprises, regret becomes a relevant cognitive bias to explain asset price responses. We find that the behavioral and rational biases encountered in US economic forecasting also exists in Continental Europe, the United Kingdom and Japan, albeit, to a lesser extent.
    Keywords: anchoring,rational bias,economic surprises,predictability,stocks,bonds,currencies,commodities,machine learning
    JEL: G14 F47 E44
    Date: 2018
  4. By: Robert J. Hodrick; Tuomas Tomunen
    Abstract: We examine the Cochrane and Piazzesi (2005, 2008) model in several out-of-sample analyzes. The model's one-factor forecasting structure characterizes the term structures of additional currencies in samples ending in 2003. In post-2003 data one-factor structures again characterize each currency's term structure, but we reject equality of the coefficients across the two samples. We derive some implications of the model for the predictability of cross-currency investments, but we find little support for these predictions in either pre-2004 or post-2003 data. The model fails to beat historical average returns in recursive out-or-sample forecasting of excess rates of return for bonds and currencies.
    JEL: G12 G15
    Date: 2018–09
  5. By: Carlos Carvalho (University of Texas at Austin); Jared D. Fisher (University of Texas at Austin); Davide Pettenuzzo (Brandeis University, Department of Economics)
    Abstract: We introduce a simulation-free method to model and forecast multiple asset returns and employ it to investigate the optimal ensemble of features to include when jointly predicting monthly stock and bond excess returns. Our approach builds on the Bayesian Dynamic Linear Models of West and Harrison (1997), and it can objectively determine, through a fully automated procedure, both the optimal set of regressors to include in the predictive system and the degree to which the model coefficients, volatilities, and covariances should vary over time. When applied to a portfolio of five stock and bond returns, we find that our method leads to large forecast gains, both in statistical and economic terms. In particular, we find that relative to a standard no-predictability benchmark, the optimal combination of predictors, stochastic volatility, and time-varying covariances increases the annualized certainty equivalent returns of a leverage-constrained power utility investor by more than 500 basis points.
    Keywords: Optimal asset allocation, Bayesian econometrics, Dynamic Linear models
    JEL: C11 C22 G11 G12
    Date: 2018–09
  6. By: Oscar Claveria (AQR-IREA AQR-IREA, University of Barcelona (UB). Tel. +34-934021825; Fax. +34-934021821. Department of Econometrics, Statistics and Applied Economics, University of Barcelona, Diagonal 690, 08034 Barcelona, Spain.)
    Abstract: In this study we present a metric of consensus for Likert-type scales. The measure gives the level of agreement as the percentage of consensus among respondents. The proposed framework allows to design a positional indicator that gives the degree of agreement for each item independently of the number of reply options. In order to assess the performance of the proposed metric of consensus, in an iterated one-period ahead forecasting experiment we test whether the inclusion of the degree of agreement in consumers’ expectations regarding the evolution of unemployment improves out-of-sample forecast accuracy in eight European countries. We find that this is the case in five countries (Finland, France, Ireland, Italy and Spain). These results show that the degree of agreement in consumers’ expectations contains useful information to predict unemployment rates and underline the usefulness of consensus-based metrics to track the evolution of economic variables.
    Keywords: Likert scales; consensus; geometry; economic tendency surveys; consumer expectations; unemployment. JEL classification:C14; C51; C52; C53; D12; E24.
    Date: 2018–09
  7. By: Yanfei Kang; Rob J Hyndman; Feng Li
    Abstract: The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires a diverse collection of time series data to enable reliable comparisons against alternative approaches. We propose the use of mixture autoregressive (MAR) models to generate collections of time series with diverse features. We simulate sets of time series using MAR models and investigate the diversity and coverage of the simulated time series in a feature space. An efficient method is also proposed for generating new time series with controllable features by tuning the parameters of the MAR models. The simulated data based on our method can be used as evaluation tool for tasks such as time series classification and forecasting.
    Keywords: time series features, time series generation, mixture autoregressive models
    Date: 2018

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