nep-for New Economics Papers
on Forecasting
Issue of 2013‒01‒19
nine papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts By Jos Jansen; Xiaowen Jin; Jasper de Winter
  2. Can Tightness in the Housing Market Help Predict Subsequent Home Price Appreciation? Evidence from the U.S. and the Netherlands By Paul E. Carrillo; Erik Robert De Wit; William D. Larson
  3. Forecasting extreme electricity spot prices By Volodymyr Korniichuk
  4. Has the Basel Accord Improved Risk Management During the Global Financial Crisis? By Michael McAleer; Juan-Ángel Jiménez-Martín; Teodosio Pérez-Amaral
  5. Survival prediction based on compound covariate under cox proportional hazard models By Emura, Takeshi; Chen, Yi-Hau; Chen, Hsuan-Yu
  6. Economic forecasts and sovereign yields* By António Afonso; Ana Sofia Nunes
  7. Strategic Asset Allocation for Central Bank’s Management of Foreign Reserves: A new approach By Zhang, Zhichao; Chau, Frankie; Xie, Li
  8. Dynamics of episodic transient correlations in currency exchange rate returns and their predictability By Milan \v{Z}ukovi\v{c}
  9. Bayesian inference and data cloning in population projection matrices By J. de la Horra Navarro; J. Miguel Marín; M. T. Rodríguez Bernal

  1. By: Jos Jansen; Xiaowen Jin; Jasper de Winter
    Abstract: We conduct a systematic comparison of the short-term forecasting abilities of eleven statistical models and professional analysts in a pseudo-real time setting, using a large set of monthly indicators. Our analysis covers the euro area and its five largest countries over the years 1996-2011. We find that summarizing the available monthly information in a few factors is a more promising forecasting strategy than averaging a large number of indicator-based forecasts. The dynamic and static factor model outperform other models, especially during the crisis period. Judgmental forecasts by professional analysts often embody valuable information that could be used to enhance forecasts derived from purely mechanical procedures.
    Keywords: nowcasting; professional forecasters; factor model; judgment; forecasting
    JEL: E52 C53 C33
    Date: 2012–12
  2. By: Paul E. Carrillo (Department of Economics/Institute for International Economic Policy, George Washington University); Erik Robert De Wit (University of Amsterdam); William D. Larson (George Washington University)
    Abstract: This paper assesses the predictive power of variables that measure market tightness, such as seller's bargaining power and sale probabilities, on future home prices. Theoretical insights from a stylized search-and-matching model illustrate that such indicators can be associated with subsequent home price appreciation. The empirical analysis employs data on all residential units offered for sale through a real estate broker in the Netherlands and a large suburb in the Washington, DC area. Individual records are used to construct a quarterly home price index, an index that measures seller's bargaining power, and (quality adjusted) home sale probabilities. Using conventional time-series models we show that current sale probabilities and bargaining power can significantly reduce home price appreciation forecast errors.
    Keywords: Forecasting, Home prices, Bargaining power, Time on the market, Information asymmetries
    JEL: R30 C53
    Date: 2012–08
  3. By: Volodymyr Korniichuk
    Abstract: We propose a model for forecasting extreme electricity prices in real time (high frequency) settings. The unique feature of our model is its ability to forecast electricity price exceedances over very high thresholds, where only a few (if any) observations are available. The model can also be applied for simulating times of occurrence and magnitudes of the extreme prices. We employ a copula with a changing dependence parameter for capturing serial dependence in the extreme prices and the censored GPD for modelling their marginal distributions. For modelling times of the extreme price occurrences we propose an approach based on a negative binomial distribution. The model is applied to electricity spot prices from Australia's national electricity market.
    Keywords: electricity spot prices, copula, GPD, negative binomial distribution
    JEL: C53 C51 C32
    Date: 2012–12–27
  4. By: Michael McAleer (Erasmus University Rotterdam); Juan-Ángel Jiménez-Martín (Complutense University of Madrid); Teodosio Pérez-Amaral (Complutense University of Madrid)
    Abstract: The Basel II Accord requires that banks and other Authorized Deposit-taking Institutions (ADIs) communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models to measure Value-at-Risk (VaR). The risk estimates of these models are used to determine capital requirements and associated capital costs of ADIs, depending in part on the number of previous violations, whereby realised losses exceed the estimated VaR. In this paper we define risk management in terms of choosing from a variety of risk models, and discuss the selection of optimal risk models. A new approach to model selection for predicting VaR is proposed, consisting of combining alternative risk models, and we compare conservative and aggressive strategies for choosing between VaR models. We then examine how different risk management strategies performed during the 2008-09 global financial crisis. These issues are illustrated using Standard and Poor’s 500 Composite Index.
    Keywords: Value-at-Risk (VaR); daily capital charges; violation penalties; optimizing strategy; risk forecasts; aggressive or conservative risk management strategies; Basel Accord; global financial crisis
    JEL: G32 G11 G17 C53 C22
    Date: 2013–01–08
  5. By: Emura, Takeshi; Chen, Yi-Hau; Chen, Hsuan-Yu
    Abstract: Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models. We demonstrate via simulations and real data analysis that the compound covariate method generally competes well with ridge regression and Lasso methods, both already well-studied methods for predicting survival outcomes with a large number of covariates. Furthermore, we develop a refinement of the compound covariate method by incorporating likelihood information from multivariate Cox models. The new proposal is an adaptive method that borrows information contained in both the univariate and multivariate Cox regression estimators. We show that the new proposal has a theoretical justification from a statistical large sample theory and is naturally interpreted as a shrinkage-type estimator, a popular class of estimators in statistical literature. Two datasets, the primary biliary cirrhosis of the liver data and the non-small-cell lung cancer data, are used for illustration. The proposed method is implemented in R package “compound.Cox” available in CRAN at
    Keywords: Cox proportional hazard model; Prediction; Survival analysis
    JEL: C13 C14 C34 C24 C4
    Date: 2012
  6. By: António Afonso; Ana Sofia Nunes
    Abstract: The European Commission releases twice a year economic forecasts for some macro and fiscal variables (GDP growth rate, inflation, budget balance, among others). In our research we will try to understand if the corrections made to these forecasts have an impact in sovereign yields. We will perform an econometric analysis in a panel of 15 EU countries (Austria, Belgium, Germany, Denmark, Spain, Finland, France, United Kingdom, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal and Sweden), covering the period from 1999:1 until 2012:1, and after we analyse each country individually, on the basis of a SUR analysis. We find that corrections in the EC’s forecasts do impinge on the 10-year sovereign bond yields, particularly corrections in fiscal variables, but this impact is different across countries, being more pronounced in countries with less favourable economic conditions.
    Keywords: macro forecasts, fiscal forecasts, sovereign yields.
    JEL: C23 E44 H68
    Date: 2013–01
  7. By: Zhang, Zhichao; Chau, Frankie; Xie, Li
    Abstract: This paper proposes a new approach to strategic asset allocation for central banks’ management of foreign reserves. This eclectic approach combines the behavioural portfolio management in the framework of mean-variance mental accounting (MVMA) with the improvements on asset return forecast offered by the Black-Litterman (B-L) model, proving particularly suitable for the reserve management policy with multiple objectives. The B-L model is embedded into the MVMA framework to obtain both the equilibrium and the B-L returns as our improved forecasts, formulating forward-looking investment strategies. The approach is applied to the case of China to derive optimal asset allocation for the Chinese central bank.
    Keywords: Reserve Management; Strategic Asset Allocation; Mental Accounting; Black-Litterman model; China’s Foreign Reserves
    JEL: G11 E58 C61 G0 C11
    Date: 2012–12–21
  8. By: Milan \v{Z}ukovi\v{c}
    Abstract: We study the dynamics of the linear and non-linear serial dependencies in financial time series in a rolling window framework. In particular, we focus on the detection of episodes of statistically significant two- and three-point correlations in the returns of several leading currency exchange rates that could offer some potential for their predictability. We employ a rolling window approach in order to capture the correlation dynamics for different window lengths and analyze the distributions of periods with statistically significant correlations. We find that for sufficiently large window lengths these distributions fit well to power-law behavior. We also measure the predictability itself by a hit rate, i.e. the rate of consistency between the signs of the actual returns and their predictions, obtained from a simple correlation-based predictor. It is found that during these relatively brief periods the returns are predictable to a certain degree and the predictability depends on the selection of the window length.
    Date: 2013–01
  9. By: J. de la Horra Navarro; J. Miguel Marín; M. T. Rodríguez Bernal
    Abstract: Discrete time models are used in Ecology for describing the evolution of an agestructured population. Usually, they are considered from a deterministic viewpoint but, in practice, this is not very realistic. The statistical model we propose in this article is a reasonable model for the case in which the evolution of the population is described by means of a projection matrix. In this statistical model, fertility rates and survival rates are unknown parameters and they are estimated by using a Bayesian approach. Usual Bayesian and data cloning methods (based on Bayesian methodology) are applied to real data from the population of the Steller sea lions located in the Alaska coast since 1978 to 2004. The estimates obtained from these methods show a good behavior when they are compared to the actual values
    Keywords: Population projection matrices, Data cloning, Age-structured population, Leslie matrix, Bayesian MCMC algorithm
    Date: 2013–01

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