nep-for New Economics Papers
on Forecasting
Issue of 2014‒11‒17
six papers chosen by
Rob J Hyndman
Monash University

  1. On the Sources of Uncertainty in Exchange Rate Predictability By Byrne, Joseph P; Korobilis, Dimitris; Ribeiro, Pinho J
  2. The mortgage spread as a predictor of real-time economic activity By Hännikäinen, Jari
  3. Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data By Guérin, Pierre; Leiva-Leon, Danilo
  4. Improving Density Forecasts and Value-at-Risk Estimates by Combining Densities By Anne Opschoor; Dick van Dijk; Michel van der Wel
  5. Do US Macroeconomic Forecasters Exaggerate Their Differences? By Michael P. Clements
  6. Forecasting Medium and Large Datasets with Vector Autoregressive Moving Average (VARMA) Models By Gustavo Fruet Dias; George Kapetanios

  1. By: Byrne, Joseph P; Korobilis, Dimitris; Ribeiro, Pinho J
    Abstract: We analyse the role of time-variation in coefficients and other sources of uncertainty in exchange rate forecasting regressions. Our techniques incorporate the notion that the relevant set of predictors and their corresponding weights, change over time. We find that predictive models which allow for sudden, rather than smooth, changes in coefficients significantly beat the random walk benchmark in out-of-sample forecasting exercise. Using an innovative variance decomposition scheme, we identify uncertainty in coefficients estimation and uncertainty about the precise degree of coefficients' variability, as the main factors hindering models' forecasting performance. The uncertainty regarding the choice of the predictor is small.
    Keywords: Instabilities; Exchange Rate Forecasting; Time-Varying Parameter Models; Bayesian Model Averaging; Forecast Combination; Financial Condition Indexes; Bootstrap
    JEL: C53 C58 E44 F37 G01
    Date: 2014–09–26
  2. By: Hännikäinen, Jari
    Abstract: We analyze the predictive content of the mortgage spread for U.S. economic activity. We find that the spread contains predictive power for real GDP and industrial production. Furthermore, it outperforms the term spread and Gilchrist– Zakrajsek spread in a real-time forecasting exercise. However, the predictive ability of the mortgage spread varies over time.
    Keywords: mortgage spread, forecasting, real-time data
    JEL: C53 E37 E44
    Date: 2014–09–05
  3. By: Guérin, Pierre; Leiva-Leon, Danilo
    Abstract: This paper estimates and forecasts U.S. business cycle turning points with state-level data. The probabilities of recession are obtained from univariate and multivariate regime-switching models based on a pairwise combination of national and state-level data. We use two classes of combination schemes to summarize the information from these models: Bayesian Model Averaging and Dynamic Model Averaging. In addition, we suggest the use of combination schemes based on the past predictive ability of a given model to estimate regimes. Both simulation and empirical exercises underline the utility of such combination schemes. Moreover, our best specification provides timely updates of the U.S. business cycles. In particular, the estimated turning points from this specification largely precede the announcements of business cycle turning points from the NBER business cycle dating committee, and compare favorably with competing models.
    Keywords: Markov-switching; Nowcasting; Forecasting; Business Cycles; Forecast combination.
    JEL: C53 E32 E37
    Date: 2014–10–17
  4. By: Anne Opschoor (VU University Amsterdam); Dick van Dijk (Erasmus University Rotterdam); Michel van der Wel (Erasmus University Rotterdam)
    Abstract: We investigate the added value of combining density forecasts for asset return prediction in a specific region of support. We develop a new technique that takes into account model uncertainty by assigning weights to individual predictive densities using a scoring rule based on the censored likelihood. We apply this approach in the context of recently developed univariate volatility models (including HEAVY and Realized GARCH models), using daily returns from the S&P 500, DJIA, FTSE and Nikkei stock market indexes from 2000 until 2013. The results show that combined density forecasts based on the censored likelihood scoring rule significantly outperform pooling based on the log scoring rule and individual density forecasts. The same result, albeit less strong, holds when compared to combined density forecasts based on equal weights. In addition, VaR estimates improve a t the short horizon, in particular when compared to estimates based on equal weights or to the VaR estimates of the individual models.
    Keywords: Density forecast evaluation, Volatility modeling, Censored likelihood, Value-at-Risk
    JEL: C53 C58 G17
    Date: 2014–07–21
  5. By: Michael P. Clements (ICMA Centre, Henley Business School, University of Reading)
    Abstract: Application of the Bernhardt, Campello and Kutsoati (2006) test of herding to the calendar-year annual output growth and inflation forecasts suggests forecasters tend to exaggerate their differences, except at the shortest horizon when they tend to herd. We consider whether these types of behaviour can help to explain the puzzle that professional forecasters sometimes make point predictions and histogram forecasts which are mutually inconsistent.
    Keywords: Macro-forecasting, imitative behaviour, histogram forecasts, point predictions.
    Date: 2014–09
  6. By: Gustavo Fruet Dias (Aarhus University and CREATES); George Kapetanios (Queen Mary University of London)
    Abstract: We address the issue of modelling and forecasting macroeconomic variables using medium and large datasets, by adopting VARMA models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (IOLS) estimator. We establish the consistency and asymptotic distribution of the estimator for strong and weak VARMA(p,q) models. Monte Carlo results show that IOLS is consistent and feasible for large systems, outperforming the MLE and other linear regression based efficient estimators under alternative scenarios. Our empirical application shows that VARMA models outperform the AR(1), VAR(p) and factor models, considering different model dimensions.
    Keywords: VARMA, weak VARMA, weak ARMA, Forecasting, Large datasets, Iterative ordinary least squares (IOLS) estimator, Asymptotic contraction mapping
    JEL: C13 C32 C53 C63 E0
    Date: 2014–10–23

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