nep-for New Economics Papers
on Forecasting
Issue of 2015‒08‒19
ten papers chosen by
Rob J Hyndman
Monash University

  1. Information use in supply chain forecasting By Fildes, Robert; Goodwin, Paul; Onkal, Dilek
  2. Evaluating UK point and density forecasts from an estimated DSGE model: the role of off-model information over the financial crisis By Fawcett, Nicholas; Koerber, Lena; Masolo, Riccardo; Waldron, Matthew
  3. How Good Are U.S. Government Forecasts of the Federal Debt? By Andrew Martinez
  4. Quantile forecasts of in‡ation under model uncertainty By Dimitris Korobilis.
  5. Forecasts from Reduced-form Models under the Zero-Lower-Bound Constraint By Pasaogullari, Mehmet
  6. Does Cash Flow Predict Returns? By Paresh K Narayan; Joakim Westerlund
  7. Model Predictive Control Strategy to Forecast Employability in Earth Sciences By E Courtial; C Garrouste
  8. A State-Space Estimation of the Lee-Carter Mortality Model and Implications for Annuity Pricing By Man Chung Fung; Gareth W. Peters; Pavel V. Shevchenko
  9. (Mis-)Predicted Subjective Well-Being Following Life Events By Odermatt, Reto; Stutzer, Alois
  10. Institutional Investors, Annual Reports, Textual Analysis and Stock Returns: Evidence from SEC EDGAR 10-K and 13-F Forms By Chouliaras, Andreas

  1. By: Fildes, Robert; Goodwin, Paul; Onkal, Dilek
    Abstract: Demand forecasting to support supply chain planning is a critical activity, recognized as pivotal in manufacturing and retailing operations where information is shared across functional areas to produce final detailed forecasts. The approach generally encountered is that a baseline statistical forecast is examined in the light of shared information from sales, marketing and logistics and the statistical forecast may then be modified to take these various pieces of information into account. This experimental study explores forecasters’ use of available information when they are faced with the task of adjusting a baseline forecast for a number of retail stock keeping units to take into account a forthcoming promotion. Forecasting demand in advance of promotions carries a particular significance given their intensive supply chain repercussions and financial impact. Both statistical and qualitative information was provided through a forecasting support system typical of those found in practice. Our results show participants responding to the quantity of information made available, though with decreasing scale effects. In addition, various statistical cues (which are themselves extraneous) were illustrated to be particularly important, including the size and timing of the last observed promotion. Overall, participants appeared to use a compensatory strategy when combining information that had either positive or negative implications for the success of the promotions. However, there was a consistent bias towards underestimating the effect of the promotions. These observed biases have important implications for the design of organizational sales and operations planning processes and the forecasting support systems that such processes rely on.
    Keywords: Demand planning; Sales and Operations Planning; Behavioural operations; Forecasting support systems; Promotional planning; Information effects.
    JEL: D81 M11 M15
    Date: 2015–05
  2. By: Fawcett, Nicholas (Bank of England); Koerber, Lena (Bank of England); Masolo, Riccardo (Bank of England); Waldron, Matthew (Bank of England)
    Abstract: This paper investigates the real-time forecast performance of the Bank of England’s main DSGE model, COMPASS, before, during and after the financial crisis with reference to statistical and judgemental benchmarks. A general finding is that COMPASS’s relative forecast performance improves as the forecast horizon is extended (as does that of the Statistical Suite of forecasting models). The performance of forecasts from all three sources deteriorates substantially following the financial crisis. The deterioration is particularly marked for the DSGE model’s GDP forecasts. One possible explanation for that, and a key difference between DSGE models and judgemental forecasts, is that judgemental forecasts are implicitly conditioned on a broader information set, including faster-moving indicators that may be particularly informative when the state of the economy is evolving rapidly, as in periods of financial distress. Consistent with that interpretation, GDP forecasts from a version of the DSGE model augmented to include a survey measure of short-term GDP growth expectations are competitive with the judgemental forecasts at all horizons in the post-crisis period. More generally, a key theme of the paper is that both the type of off-model information and the method used to apply it are key determinants of DSGE model forecast accuracy.
    Keywords: DSGE models; forecasting; financial crisis.
    JEL: C53 E12 E17
    Date: 2015–07–31
  3. By: Andrew Martinez
    Abstract: This paper compares annual one-year-ahead and five-year-ahead forecasts from government agencies for the U.S. gross federal debt and deficit from 1984 to 2013.  Other studies have compared two of these agencies' forecasts, but not for debt.  The current paper finds that the forecast from the Analysis of the President's Budget performs best across both horizons but does not encompass the other forecasts.  Instead, each of the forecasts lacks information included by the other agencies and therefore a combination of all three outperforms any forecast individually.
    Keywords: Evaluating Forecasts, Government Forecasting, Macroeconomic Forecasts, Forecast Encompassing, Deficit
    JEL: C53 H68
    Date: 2014–10–20
  4. By: Dimitris Korobilis.
    Abstract: Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for di§erent predictors to a§ect di§erent quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future ináation by providing superior predictive densities compared to mean regression models with and without BMA.
    Keywords: Bayesian model averaging; quantile regression; ináation forecasts; fan charts
    JEL: C11 C22 C52
    Date: 2015–04
  5. By: Pasaogullari, Mehmet (Federal Reserve Bank of Cleveland)
    Abstract: In this paper, I consider forecasting from a reduced-form VAR under the zero lower bound (ZLB) for the short-term nominal interest rate. I develop a method that a) computes the exact moments for the first n + 1 periods when n previous periods are tracked and b) approximates moments for the periods beyond n + 1 period using techniques for truncated normal distributions and approximations a la Kim (1994). I show that the algorithm produces satisfactory results for VAR systems with moderate to high persistence even when only one previous period is tracked. For very persistent VAR systems, however, tracking more periods is needed in order to obtain reliable approximations. I also show that the method is suitable for affine term-structure modeling, where the underlying state vector includes the short-term interest rate as in Taylor rules with inertia.
    Keywords: monetary policy; forecasting from VARs; zero lower bound; normal mixtures
    JEL: C53 E42 E43 E47
    Date: 2015–08–05
  6. By: Paresh K Narayan (Deakin University); Joakim Westerlund (Deakin University)
    Abstract: In this paper, we propose the hypothesis that cash flow and cash flow volatility predict returns. We categorize firms listed on the New York Stock Exchange into sectors, and apply tests for both in-sample and out-of-sample predictability. While we find strong evidence that cash flow volatility predicts returns for all sectors, the evidence obtained when using cash flow as a predictor is relatively weak. Estimated profits and utility gains also suggest that it is cash flow volatility that is more relevant as a source of information than cash flow.
    Keywords: Cash Flow Volatility; Returns; Predictability; Panel Data; Sectors
    JEL: C12 C22
  7. By: E Courtial (Institut Prisme de l'Université d'Orléan - Institut Prisme de l'Université d'Orléan - Université de Franche-Comté); C Garrouste (LEO - Laboratoire d'économie d'Orleans - CNRS - UO - Université d'Orléans)
    Abstract: Energy prices and environmental policies influence more than ever employment trends across the world. The purpose of this paper is to develop a control strategy to enhance the employability of French graduates in a field that is both a key driver and a significant target of these new trends, namely Earth Sciences. The aim is to provide French universities with a predictive tool to adjust efficiently their skills' supply capacity with the demand forecasts at the European level. This task is treated as a tracking problem from the viewpoint of the control theory. The reference trajectory is obtained via a labour market forecasting model. For the first time, an econometric model and a predictive control strategy are combined. Simulations illustrate the feasibility and potentials of the proposed approach.
    Date: 2014–08–24
  8. By: Man Chung Fung; Gareth W. Peters; Pavel V. Shevchenko
    Abstract: In this article we investigate a state-space representation of the Lee-Carter model which is a benchmark stochastic mortality model for forecasting age-specific death rates. Existing relevant literature focuses mainly on mortality forecasting or pricing of longevity derivatives, while the full implications and methods of using the state-space representation of the Lee-Carter model in pricing retirement income products is yet to be examined. The main contribution of this article is twofold. First, we provide a rigorous and detailed derivation of the posterior distributions of the parameters and the latent process of the Lee-Carter model via Gibbs sampling. Our assumption for priors is slightly more general than the current literature in this area. Moreover, we suggest a new form of identification constraint not yet utilised in the actuarial literature that proves to be a more convenient approach for estimating the model under the state-space framework. Second, by exploiting the posterior distribution of the latent process and parameters, we examine the pricing range of annuities, taking into account the stochastic nature of the dynamics of the mortality rates. In this way we aim to capture the impact of longevity risk on the pricing of annuities. The outcome of our study demonstrates that an annuity price can be more than 4% under-valued when different assumptions are made on determining the survival curve constructed from the distribution of the forecasted death rates. Given that a typical annuity portfolio consists of a large number of policies with maturities which span decades, we conclude that the impact of longevity risk on the accurate pricing of annuities is a significant issue to be further researched. In addition, we find that mis-pricing is increasingly more pronounced for older ages as well as for annuity policies having a longer maturity.
    Date: 2015–08
  9. By: Odermatt, Reto (University of Basel); Stutzer, Alois (University of Basel)
    Abstract: The correct prediction of how alternative states of the world affect our lives is a cornerstone of economics. We study how accurate people are in predicting their future well-being when facing major life events. Based on individual panel data, we compare people's forecast of their life satisfaction in five years' time to their actual realisations later on. This is done after the individuals experience widowhood, marriage, unemployment or disability. We find systematic prediction errors that are at least partly driven by unforeseen adaptation.
    Keywords: adaptation, life satisfaction, life events, projection-bias, subjective well-being, utility prediction, unemployment
    JEL: D03 D12 D60 I31
    Date: 2015–08
  10. By: Chouliaras, Andreas
    Abstract: I analyze 18510 SEC EDGAR Form 10-K (annual reports), for NASDAQ, NYSE and AMEX (NYSE MKT) stocks, from 1999 until 2015, along with 176565 SEC EDGAR Form 13-F (quarterly reports of institutional investors holdings). I find that (i) 10-K pessimism negatively affects stock holdings after the filing (ii) institutions do not appear to have forecasting power as to how pessimistic the annual report will be, as they do not adjust their holdings in the pessimistic stocks before the 10-K filing takes place, (iii) an increase in the number of institutional investors that hold a stock leads to an increase in stock prices after the 10-K filing (iv) institutions increase their positions in stocks that had positive returns one (1) to twelve (12) months before the 10-K filing.
    Keywords: SEC, EDGAR, Form 13-F, Form 10-K, Textual Analysis, NYSE, NASDAQ, AMEX (NYSE MKT)
    JEL: G10 G14 G23
    Date: 2015–07–31

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