nep-for New Economics Papers
on Forecasting
Issue of 2019‒12‒09
six papers chosen by
Rob J Hyndman
Monash University

  1. Elucidate Structure in Intermittent Demand Series By Nikolaos Kourentzes; George Athanasopoulos
  2. On The Evaluation Of Binary Event Probability Predictions In Electricity Price Forecasting By Arne Vogler; Florian Ziel
  3. Comparing Forecasts of Extremely Large Conditional Covariance Matrices By Moura, Guilherme V.; Ruiz, Esther; Santos, André A. P.
  4. An Alternative Method of Forecasting Divorce Rates Based on the Parametric Sickle Model By Wolfinger, Nicholas H.
  5. Predicting interest rates in real-time By Alberto Caruso; Laura Coroneo
  6. Modeling UK Mortgage Demand Using Online Searches By Jaroslav Pavlicek; Ladislav Kristoufek

  1. By: Nikolaos Kourentzes; George Athanasopoulos
    Abstract: Intermittent demand forecasting has been widely researched in the context of spare parts management. However, it is becoming increasingly relevant to many other areas, such as retailing, where at the very disaggregate level time series may be highly intermittent, but at more aggregate levels are likely to exhibit trends and seasonal patterns. The vast majority of intermittent demand forecasting methods are inappropriate of producing forecasts with such features. We propose using temporal hierarchies to produce forecasts that demonstrate these traits at the various aggregation levels, effectively informing the resulting intermittent forecasts of these patterns that are identifiable only at higher levels. We conduct an empirical evaluation on real data and demonstrate statistically significant gains for both point and quantile forecasts.
    Keywords: forecasting, temporal aggregation, temporal hierarchies, forecast combination, forecast reconciliation
    Date: 2019
  2. By: Arne Vogler; Florian Ziel (Chair for Management Sciences and Energy Economics, University of Duisburg-Essen (Campus Essen))
    Abstract: In this paper we present an evaluation framework for predictions of binary events in probabilistic electricity price forecasting. It employs the MSE-equivalent QPS together with the DM test and allows for further insights about deficiencies of the considered models. Additionally, techniques from the field of classification are considered, which extend our framework and are particularly suited for the evaluation of predictions of rare events. We consider binary events with direct applicability to a generator’s daily decision making such as profitability of a pumped-hydro storage plant and evaluate the respective forecasts statistically. We show that the task of forecast evaluation can be simplified from assessing a multivariate distribution over prices to assessing a univariate distribution over a binary outcome, fully characterized by a single probability.
    Keywords: Probabilistic Forecasting, Binary Predictions, Classification, Electricity Price Forecasting
    JEL: C53 C38 Q47
  3. By: Moura, Guilherme V.; Ruiz, Esther; Santos, André A. P.
    Abstract: Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenvironments involving even thousands of time series since most of the available models sufferfrom the curse of dimensionality. In this paper, we challenge some popular multivariate GARCH(MGARCH) and Stochastic Volatility (MSV) models by fitting them to forecast the conditionalcovariance matrices of financial portfolios with dimension up to 1000 assets observed daily over a30-year time span. The time evolution of the conditional variances and covariances estimated bythe different models is compared and evaluated in the context of a portfolio selection exercise. Weconclude that, in a realistic context in which transaction costs are taken into account, modelling thecovariance matrices as latent Wishart processes delivers more stable optimal portfolio compositionsand, consequently, higher Sharpe ratios.
    Keywords: Stochastic Volatility; Risk-Adjusted Return; Portfolio Turnover; Minimum-Variance Portfolio; Garch; Covariance Forecasting
    JEL: G17 C53
    Date: 2019–11–30
  4. By: Wolfinger, Nicholas H.
    Abstract: Demographers routinely predict that between 40 and 50 percent of new marriages will end in divorce. Based on life tables, these forecasts entail strong assumptions that current marriages will behave in the future like others did in the past. I use data from the 1995 June Marriage and Fertility Supplement of the Current Population Survey to test an alternative method of forecasting divorce rates: predictions based on the parametric sickle model of marital instability. The sickle model corresponds almost perfectly to completed marriage cohorts (30 year marriages unlikely to ever dissolve), but offered implausibly low divorce rate forecasts for newer marriages. It is therefore unlikely to be useful for forecasting divorce rates.
    Date: 2018–08–29
  5. By: Alberto Caruso; Laura Coroneo
    Abstract: We analyse the predictive ability of real-time macroeconomic information for the yield curve of interest rates. We specify a mixed-frequency macro-yields model in real-time that incorporates interest rate surveys and that treats macroeconomic factors as unobservable components. Results indicate that real-time macroeconomic information is helpful to predict interest rates, and that data revisions drive a superior predictive ability of revised macro data over real-time macro data. Moreover, we find that incorporating interest rate surveys in the model can significantly improve its predictive ability.
    Keywords: Government Bonds; Dynamic Factor Models; Real-time Forecasting; Mixed-frequencies.
    JEL: C33 C53 E43 E44 G12
    Date: 2019–11
  6. By: Jaroslav Pavlicek (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic); Ladislav Kristoufek (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic)
    Abstract: The internet has become the primary source of information for most of the population in modern economies, and as such, it provides an enormous amount of readily available data. Among these are the data on the internet search queries, which have been shown to improve forecasting models for various economic and financial series. In the aftermath of the global financial crisis, modeling and forecasting mortgage demand and subsequent approvals have become a central issue in the banking sector as well as for governments and regulators. Here, we provide new insights into the dynamics of the UK mortgage market, specifically the demand for mortgages measured by new mortgage approvals, and whether or how models of this market can be improved by incorporating the online searches of potential mortgage applicants. Because online searches are expected to be one of the last steps before a customer’s actual application for a large share of the population, intuitive utility is an appealing approach. We compare two baseline models – an autoregressive model and a structural model with relevant macroeconomic variables – with their extensions utilizing online searches on Google. We find that the extended models better explain the number of new mortgage approvals and markedly improve their nowcasting and forecasting performance.
    Keywords: Mortgage, online data, Google Trends, forecasting
    JEL: C22 C52 C53 C82 E27 E51
    Date: 2019–07

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