nep-for New Economics Papers
on Forecasting
Issue of 2014‒04‒05
nine papers chosen by
Rob J Hyndman
Monash University

  1. Does the federal reserve staff still beat private forecasters? By El-Shagi, Makram; Giesen, Sebastian; Jung, Alexander
  2. Anchoring the yield curve using survey expectations By Altavilla, Carlo; Giacomini, Raffaella; Ragusa, Giuseppe
  3. 2007-2013: This is what the indicator told us ? Evaluating the performance of real-time nowcasts from a dynamic factor model By Muriel Nguiffo-Boyom
  4. Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance By Manabu Asai; Michael McAleer
  5. Theoretical guidelines for a partially informed forecast examiner By Tsyplakov, Alexander
  6. Matrix Box-Cox Models for Multivariate Realized Volatility By Weigand, Roland
  7. To Predict the Equity Market, Consult Economic Theory By Davide Pettenuzzo
  8. Oil price shocks and volatility do predict stock market regimes By Stavros Degiannakis; Timotheos Angelidis; George Filis
  9. Noncausal Bayesian Vector Autoregression By Markku Lanne; Jani Luoto

  1. By: El-Shagi, Makram; Giesen, Sebastian; Jung, Alexander
    Abstract: The aim of this paper is to assess whether the findings of Romer and Romer (2000) on the superiority of staff forecasts are still valid today. The paper uses both latest available econometric techniques as well as conventional tests. Several tests for forecast rationality show that a necessary condition for good forecast performance is satisfied both for Greenbook and private forecasts, as measured by the Survey of Professional Forecasters (SPF). Tests for forecast accuracy and the encompassing test confirm the superiority of Greenbook forecasts for inflation and output using an extended sample (1968 to 2006). The relative forecast performance is, however, not robust in the presence of large macroeconomic shocks such as the Great Moderation and oil price shocks. Other econometric tests show that a relative better forecast performance by staff is observed when there is increased uncertainty. Staff’s better knowledge about the Fed’s future interest rate path also plays an important role in this respect. JEL Classification: C53, E37, E52, E58
    Keywords: forecast performance, forecast rationality, forecast stability, greenbook forecasts, of professional forecasters, survey
    Date: 2014–02
  2. By: Altavilla, Carlo; Giacomini, Raffaella; Ragusa, Giuseppe
    Abstract: The dynamic behaviour of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations are accurate predictors of yields, but only for very short maturities. We argue that this is partly due to the ability of survey participants to incorporate information about the current state of the economy as well as forward-looking information such as that contained in monetary policy announcements. We show how the informational advantage of survey expectations about short yields can be exploited to improve the accuracy of yield curve forecasts given by a base model. We do so by employing a flexible projection method that anchors the model forecasts to the survey expectations in segments of the yield curve where the informational advantage exists and transmits the superior forecasting ability to all remaining yields. The method implicitly incorporates into yield curve forecasts any information that survey participants have access to, without the need to explicitly model it. We document that anchoring delivers large and significant gains in forecast accuracy for the whole yield curve, with improvements of up to 52% over the years 2000-2012 relative to the class of models that are widely adopted by financial and policy institutions for forecasting the term structure of interest rates. JEL Classification: G1, E4, C5
    Keywords: blue chip analysts survey, exponential tilting, forecast performance, macroeconomic factors, monetary policy forward guidance, term structure models
    Date: 2014–02
  3. By: Muriel Nguiffo-Boyom
    Abstract: In 2007, a new indicator of economic activity for Luxembourg was elaborated at the BcL. It was developed using a large dataset of about 100 economic and financial time series. The methodology was based on the generalized dynamic-factor models, and the model was estimated over the period from June 1995 to June 2007. Forecast performance was evaluated on several criteria (both in pseudo-real-time and using ex-post in-sample simulations) and results were satisfactory. They gave in particular clear evidence that the indicator provides better forecasts of GDP growth than a more standard approach that relies on past GDP values only. In this paper, we present results of the real-time use of the indicator from December 2007 onwards. Special attention is given to real-time forecasts of GDP growth and the real-time assessment of the economic situation that were made during the financial crisis. The root mean squared forecast error of the indicator-based GDP growth forecasts have decreased during the 2009-2011 ?revovery? period in comparison to the 2007-2009 period, which is an encouraging results. This paper also includes (real-time) forecasts that were produced until the end of April 2013. The mean squared errors appear to have on average decreased over the second half of this extended study period in comparison with the first half. Finally, the BcL indicator produced better forecasts on average than the benchmarks over this extended study.
    Keywords: Forecasting, factor model, large datasets, real time analysis
    JEL: C53 E17
    Date: 2014–03
  4. By: Manabu Asai; Michael McAleer (University of Canterbury)
    Abstract: Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory. Using the basic structure of the fMSV model, the authors extend the dynamic correlation MSV model, the conditional/stochastic Wishart autoregressive models, the matrix-exponential MSV model, and the Cholesky MSV model. Empirical results for 7 financial asset returns for US stock returns indicate that the new fMSV models outperform existing dynamic conditional correlation models for forecasting future covariances. Among the new fMSV models, the Cholesky MSV model with long memory and asymmetry shows stable and better forecasting performance for one-day, five-day and ten-day horizons in the periods before, during and after the global financial crisis.
    Keywords: Dimension reduction; Factor Model; Multivariate Stochastic Volatility; Leverage Effects; Long Memory; Realized Volatility
    JEL: C32 C53 C58 G17
    Date: 2014–03–17
  5. By: Tsyplakov, Alexander
    Abstract: The paper explores probability theory foundations behind evaluation of probabilistic forecasts. The emphasis is on a situation when the forecast examiner possesses only partially the information which was available and was used to produce a forecast. We argue that in such a situation forecasts should be judged by their conditional auto-calibration. Necessary and sufficient conditions of auto-calibration are discussed and expressed in the form of testable moment conditions. The paper also analyzes relationships between forecast calibration and forecast efficiency.
    Keywords: probabilistic forecast; forecast calibration; moment condition; probability integral transform; orthogonality condition; scoring rule; forecast encompassing
    JEL: C52 C53
    Date: 2014–04–02
  6. By: Weigand, Roland
    Abstract: We propose flexible models for multivariate realized volatility dynamics which involve generalizations of the Box-Cox transform to the matrix case. The matrix Box-Cox model of realized covariances (MBC-RCov) is based on transformations of the covariance matrix eigenvalues, while for the Box-Cox dynamic correlation (BC-DC) specification the variances are transformed individually and modeled jointly with the correlations. We estimate transformation parameters by a new multivariate semiparametric estimator and discuss bias-corrected point and density forecasting by simulation. The methods are applied to stock market data where excellent in-sample and out-of-sample performance is found.
    Keywords: Realized covariance matrix; dynamic correlation; semiparametric estimation; density forecasting
    JEL: C14 C32 C51 C53 C58
    Date: 2014–03
  7. By: Davide Pettenuzzo (International Business School, Brandeis University)
    Abstract: Despite more than half a century of research on forecasting stock market returns, most predictive models perform quite poorly when they are put to the test of actually predicting equity returns. In fact, many authors, including Bossaerts and Hillion (1999), Brennan and Xia (2005), and Welch and Goyal (2008) suggest that equity returns cannot be predicted at all. This brief proposes a simple yet very effective solution to improve the quality of stock return predictions by taking economic theory into account.
    Keywords: Economic constraints; Sharpe ratio, Equity premium predictions; Bayesian analysis
    JEL: C11 C22 G11 G12
    Date: 2013
  8. By: Stavros Degiannakis (Bank of Greece); Timotheos Angelidis (University of Peloponnese); George Filis (Bournemouth University)
    Abstract: The paper investigates whether oil price shocks and oil price volatility provide predictive information for the state of the US stock market returns and volatility. The disaggregation of oil price shocks according to their origin allows us to assess whether they contain incremental forecasting power on the state of the stock market returns and volatility, a case that does not hold for the oil price returns. Overall, the results suggest that oil price returns and volatility possess the power to forecast the state of stock market returns and volatility. The full effects of oil price returns, though, can only be revealed when the oil price shocks are disentangled and as such we claim that the oil price shocks have an incremental power in forecasting the state of the stock market. The findings are important for stock market forecasters and investors dealing with stock and derivatives markets.
    Keywords: Decomposition of shocks; oil price shocks; oil price volatility;regime switching;stock market volatility; US stock market
    JEL: C13 C32 C58 G10 Q40
    Date: 2013–12
  9. By: Markku Lanne (University of Helsinki and CREATES); Jani Luoto (University of Helsinki)
    Abstract: We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution as a by-product. We apply the methods to postwar quarterly U.S. inflation and GDP growth series. The noncausal VAR model turns out to be superior in terms of both in-sample fit and out-of-sample forecasting performance over its conventional causal counterpart. In addition, we find GDP growth to have predictive power for the future distribution of inflation over and above the own history of inflation, but not vice versa. This may be interpreted as evidence against the new Keynesian model that implies Granger causality from inflation to GDP growth, provided GDP growth is a reasonable proxy of the marginal cost.
    Keywords: Noncausal time series, non-Gaussian time series, Bayesian analysis, New Keynesian model
    JEL: C11 C32 E31
    Date: 2014–05–24

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