nep-for New Economics Papers
on Forecasting
Issue of 2016‒08‒28
seven papers chosen by
Rob J Hyndman
Monash University

  1. Assessing Point Forecast Accuracy by Stochastic Error Distance By Francis X. Diebold; Minchul Shin
  2. Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns By Rasmus T. Varneskov; Pierre Perron
  3. Forecasting the Brazilian Yield Curve Using Forward-Looking Variables By Fausto Vieira; Fernando Chague; Marcelo Fernandes
  4. Forecasting in the presence of in and out of sample breaks By Jiawen Xu; Pierre Perron
  5. Quantile Dependence between Stock Markets and its Application in Volatility Forecasting By Heejoon Han
  6. QMM - A Quarterly Macroeconomic Model of the Icelandic Economy By Ásgeir Daníelsson; Bjarni G. Einarsson; Magnús F. Guðmundsson; Svava J. Haraldsdóttir; Thórarinn G. Pétursson; Signý Sigmundardóttir; Jósef Sigurðarson; Rósa Sveinsdóttir
  7. Improved Tests for Forecast Comparisons in the Presence of Instabilities By Luis Filipe Martins; Pierre Perron

  1. By: Francis X. Diebold; Minchul Shin
    Abstract: We propose point forecast accuracy measures based directly on distance of the forecast-error c.d.f. from the unit step function at 0 ("stochastic error distance," or SED). We provide a precise characterization of the relationship between SED and standard predictive loss functions, and we show that all such loss functions can be written as weighted SED's. The leading case is absolute-error loss. Among other things, this suggests shifting attention away from conditional-mean forecasts and toward conditional-median forecasts.
    JEL: C52 C53
    Date: 2016–08
  2. By: Rasmus T. Varneskov (Aarhus University and CREATES); Pierre Perron (Boston University)
    Abstract: We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean- and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in high-frequency measures of volatility whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes, and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons.
    Keywords: Forecasting, Kalman Filter, Long Memory Processes, State Space Modeling, Stochastic Volatility, Structural Change
    JEL: C13 C22 C53
    Date: 2015–09–08
  3. By: Fausto Vieira (Fundação Getulio Vargas (FGV)); Fernando Chague (University of São Paulo); Marcelo Fernandes (Queen Mary University of London and FGV)
    Abstract: This paper proposes a forecasting model that combines a factor augmented VAR (FAVAR) methodology with the Nelson and Siegel (NS) parametrization of the yield curve to predict the Brazilian term structure of interest rates. Importantly, we extract the principal components for the FAVAR from a large data set containing forward-looking macroeconomic and financial variables. Our forecasting model significantly improves the predicting accuracy of extant models in the literature, particularly at short-term horizons. For instance, the mean absolute forecast errors are 15-40% lower than the random walk benchmark on predictions at the three month horizon. The out-of-sample analysis shows that including forward-looking indicators is the key to improve the predictive ability of the model.
    Keywords: Bonds, Factor-augmented VAR, Forecasting, term structure, Yield curve
    JEL: E58 C38 E47
    Date: 2016–08
  4. By: Jiawen Xu (Shanghai University of Finance and Economics); Pierre Perron (Boston University)
    Abstract: We present a frequentist-based approach to forecast time series in the presence of in-sample and out-of-sample breaks in the parameters of the forecasting model. We first model the parameters as following a random level shift process, with the occurrence of a shift governed by a Bernoulli process. In order to have a structure so that changes in the parameters be forecastable, we introduce two modifications. The Örst models the probability of shifts according to some covariates that can be forecasted. The second incorporates a built-in mean reversion mechanism to the time path of the parameters. Similar modifications can also be made to model changes in the variance of the error process. Our full model can be cast into a non-linear nonGaussian state space framework. To estimate it, we use particle filtering and a Monte Carlo expectation maximization algorithm. Simulation results show that the algorithm delivers accurate in-sample estimates, in particular the filtered estimates of the time path of the parameters follow closely their true variations. We provide a number of empirical applications and compare the forecasting performance of our approach with a variety of alternative methods. These show that substantial gains in forecasting accuracy are obtained.
    Keywords: instabilities; structural change; forecasting; random level shifts; particle filter
    JEL: C22 C53
    Date: 2015–09–20
  5. By: Heejoon Han
    Abstract: This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. We use the cross-quantilogram, which is a correlation statistic of quantile hit processes. The detailed dependence between stock markets depends on specific quantile ranges and this dependence is generally asymmetric; the negative spillover effect is stronger than the positive spillover effect and there exists strong directional predictability from the US market to the UK, Germany, France and Japan markets. Second, we consider a simple quantile-augmented volatility model that accommodates the quantile dependence and directional predictability between the US market and these other markets. The quantile-augmented volatility model provides superior in-sample and out-of-sample volatility forecasts.
    Date: 2016–08
  6. By: Ásgeir Daníelsson; Bjarni G. Einarsson; Magnús F. Guðmundsson; Svava J. Haraldsdóttir; Thórarinn G. Pétursson; Signý Sigmundardóttir; Jósef Sigurðarson; Rósa Sveinsdóttir
    Abstract: This Handbook contains an updated version of the Quarterly Macroeconomic Model of the Central Bank of Iceland (qmm). qmm and the underlying quarterly database have been under construction since 2001 at the Research and Forecasting Division of the Economics and Monetary Policy Department at the Bank and was ?rst implemented in the forecasting round for the Monetary Bulletin 2006/1 in March 2006. qmm is used by the Bank for forecasting and various policy simulations and therefore plays a key role as an organisational framework for viewing the medium-term future when formulating monetary policy at the Bank. This paper is mainly focused on the short and medium-term properties of qmm. Steady state properties of the model are documented in a paper by Daníelsson (2009).
    Date: 2015–12
  7. By: Luis Filipe Martins (Lisbon University Institute); Pierre Perron (Boston University)
    Abstract: Of interest is comparing the out-of-sample forecasting performance of two competing models in the presence of possible instabilities. To that effect, we suggest using simple structural change tests, sup-Wald and UDmax as proposed by Andrews (1993) and Bai and Perron (1998), for changes in the mean of the loss-differences. Giacomini and Rossi (2010) proposed a áuctuations test and a one-time reversal test also applied to the loss-differences. When properly constructed to account for potential serial correlation under the null hypothesis to have a pivotal limit distribution, it is shown that their tests have undesirable power properties, power that can be low and non-increasing as the alternative gets further from the null hypothesis. The good power properties they reported is simply an artifact of imposing a priori that the loss differentials are serially uncorrelated and using the simple sample variance to scale the tests. On the contrary, our statistics are shown to have higher monotonic power, especially the UDmax version. We use their empirical examples to show the practical relevance of the issues raised.
    Keywords: non-monotonic power, structural change, forecasts, long-run variance
    JEL: C22 C53
    Date: 2015–10–06

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