nep-for New Economics Papers
on Forecasting
Issue of 2013‒09‒26
nine papers chosen by
Rob J Hyndman
Monash University

  1. Survey Data and Short-Term Forecasts of Swedish GDP Growth By Österholm, Pär
  2. Exchange Rate Predictability By Rossi, Barbara
  3. An empirical study of predicting car type choice in Sweden using cross-validation and feature-selection By Habibi, Shiva; Sundberg, Marcus; Karlström, Anders
  4. A comparative study on forecasting polyester chips prices for 15 days, using different hybrid intelligent systems By Mojtaba Sedigh Fazli; Jean-Fabrice Lebraty
  5. Economic Cycles and Expected Stock Returns By Beber, Alessandro; Brandt, Michael; Luisi, Maurizio
  6. GPM6 - The Global Projection Model with 6 Regions By Ioan Carabenciov; Charles Freedman; Roberto Garcia-Saltos; Douglas Laxton; Ondra Kamenik; Petar Manchev
  7. Grand canonical minority game as a sign predictor By Karol Wawrzyniak; Wojciech Wi\'slicki
  8. Statistical inference of co-movements of stocks during a financial crisis By Takero Ibuki; Shunsuke Higano; Sei Suzuki; Jun-ichi Inoue; Anirban Chakraborti
  9. Characterizing financial crisis by means of the three states random field Ising model By Mitsuaki Murota; Jun-ichi Inoue

  1. By: Österholm, Pär (National Institute of Economic Research)
    Abstract: In this paper, the author evaluates forecasting models for Swedish GDP growth which make use of data from Sweden´s most important business survey, the Economic Tendency Survey. Employing nine years of quarterly real-time data, an out-of-sample forecast exercise is conducted. Results indicate that the survey data have informational value that can be used to improve forecasts, thereby confirming the empirical relevance of survey data for GDP forecasters.
    Keywords: Out-of-sample forecasts; Real-time data
    JEL: E22 E27
    Date: 2013–09–13
  2. By: Rossi, Barbara
    Abstract: The main goal of this article is to provide an answer to the question: "Does anything forecast exchange rates, and if so, which variables?". It is well known that exchange rate fluctuations are very difficult to predict using economic models, and that a random walk forecasts exchange rates better than any economic model (the Meese and Rogoff puzzle). However, the recent literature has identified a series of fundamentals/methodologies that claim to have resolved the puzzle. This article provides a critical review of the recent literature on exchange rate forecasting and illustrates the new methodologies and fundamentals that have been recently proposed in an up-to-date, thorough empirical analysis. Overall, our analysis of the literature and the data suggests that the answer to the question: "Are exchange rates predictable?" is, "It depends" on the choice of predictor, forecast horizon, sample period, model, and forecast evaluation method. Predictability is most apparent when one or more of the following hold: the predictors are Taylor rule or net foreign assets, the model is linear, and a small number of parameters are estimated. The toughest benchmark is the random walk without drift.
    Keywords: Exchange Rates; Forecast Evaluation; Forecasting; Instability
    JEL: C5 F3
    Date: 2013–07
  3. By: Habibi, Shiva (KTH); Sundberg, Marcus (KTH); Karlström, Anders (KTH)
    Abstract: In this paper we analyze the prediction problem and focus on building a multinomial logit model (MNL) to predict accurately, the market shares of new cars in the Swedish car fleet in the short-term future. Also, we investigate whether or not different prediction questions lead to different 'best' models specifications. Most of the studies in the field, take an inference-driven approach to select best models to estimate relevant parameters and project the results to the future, whereas we do take a prediction-driven approach. We use feature (variable) selection and cross-validation algorithms to improve predictive performance of models. These methods have been extensively used in other fields such as marketing but are scarce studies employing them in the choice modeling field. Additionally, we introduce four different prediction questions or loss-functions: overall prediction (log-likelihood), brand market share, ethanol (E85)/brand market share, and total share of ethanol cars and the predicted results of these models are compared. The results show that 'best' models prediction depend different prediction questions to answer. Also, they indicate that log-likelihood does not perform accurately when the objective is to predict a sub-section of population such as total share of E85 cars.
    Keywords: Hold-out sample; Out of sample prediction; Feature selection; Cross validation; Model selection; Car type choice; Discrete choice modeling; Clean vehicles
    JEL: R41
    Date: 2013–09–16
  4. By: Mojtaba Sedigh Fazli (Centre de Recherche Magellan - Université Jean Moulin - Lyon III : EA3713); Jean-Fabrice Lebraty (Centre de Recherche Magellan - Université Jean Moulin - Lyon III : EA3713)
    Abstract: Forecasting in a risky situation is a very important function for managers to assist in decision making. One of the fluctuated markets in stock exchange market is chemical market. In this research the target item for prediction is PET (Poly Ethylene Terephthalate) which is the raw material for textile industries and its very sensitive on oil prices and the demand and supply ratio. The main idea is coming through NORN model which was presented by T. Lee and James N.K. Liu in 2001. In this article after modifying the NORN model, a model has been proposed and real data are applied to this new model (we named it AHIS which stands for Adaptive Hybrid Intelligent System). Finally three different types of simulation have been conducted and compared together, which show that hybrid model which is supporting both Fuzzy Systems and Neural Networks concepts, satisfied the research question considerably. In normal situation the model forecasts a relevant trend and can be used as a DSS for a manager.
    Keywords: Efficient Market Hypothesis; Financial Forecasting; Chemicals; Artificial Intelligence; Artificial Neural Networks; Decision Support System; Locally Linear Model Tree; Hybrid Neuro Fuzzy Model.
    Date: 2013–08
  5. By: Beber, Alessandro; Brandt, Michael; Luisi, Maurizio
    Abstract: We construct daily real-time indices capturing the public information on realized and anticipated economic activity. The one-month change in realized fundamentals predicts US stock returns across horizons with strongest results between a month and a quarter. The information in anticipated fundamentals that is orthogonal to the realized data predicts returns even more strongly particularly at longer horizons up to two quarters. Splitting the sample into times of high versus low uncertainty, as measured by the cross-sectional dispersion of economist forecasts, we show that the predictability is largely concentrated in high-uncertainty times. Finally, extending the analysis internationally, we find similar results that are curiously much stronger when US data are used as predictors than global composites or local data.
    Keywords: macroeconomic uncertainty; state of the economy; stock market predictability
    JEL: G12
    Date: 2013–06
  6. By: Ioan Carabenciov; Charles Freedman; Roberto Garcia-Saltos; Douglas Laxton; Ondra Kamenik; Petar Manchev
    Abstract: This is the sixth of a series of papers that are being written as part of a project to estimate a small quarterly Global Projection Model (GPM). The GPM project is designed to improve the toolkit to which economists have access for studying both own-country and cross-country linkages. In this paper, we add three more regions and make a number of other changes to a previously estimated small quarterly projection model of the US, euro area, and Japanese economies. The model is estimated with Bayesian techniques, which provide a very efficient way of imposing restrictions to produce both plausible dynamics and sensible forecasting properties.
    Keywords: Forecasting models;Real effective exchange rates;Interest rates;External shocks;Unemployment;Demand;Spillovers;World Economic Outlook;Macroeconomic Modeling, Bayesian Estimation, Monetary Policy
    Date: 2013–04–10
  7. By: Karol Wawrzyniak; Wojciech Wi\'slicki
    Abstract: In this paper the extended model of Minority game (MG), incorporating variable number of agents and therefore called Grand Canonical, is used for prediction. We proved that the best MG-based predictor is constituted by a tremendously degenerated system, when only one agent is involved. The prediction is the most efficient if the agent is equipped with all strategies from the Full Strategy Space. Each of these filters is evaluated and, in each step, the best one is chosen. Despite the casual simplicity of the method its usefulness is invaluable in many cases including real problems. The significant power of the method lies in its ability to fast adaptation if \lambda-GCMG modification is used. The success rate of prediction is sensitive to the properly set memory length. We considered the feasibility of prediction for the Minority and Majority games. These two games are driven by different dynamics when self-generated time series are considered. Both dynamics tend to be the same when a feedback effect is removed and an exogenous signal is applied.
    Date: 2013–09
  8. By: Takero Ibuki; Shunsuke Higano; Sei Suzuki; Jun-ichi Inoue; Anirban Chakraborti
    Abstract: In order to figure out and to forecast the emergence phenomena of social systems, we propose several probabilistic models for the analysis of financial markets, especially around a crisis. We first attempt to visualize the collective behaviour of markets during a financial crisis through cross-correlations between typical Japanese daily stocks by making use of multi- dimensional scaling. We find that all the two-dimensional points (stocks) shrink into a single small region when a economic crisis takes place. By using the properties of cross-correlations in financial markets especially during a crisis, we next propose a theoretical framework to predict several time-series simultaneously. Our model system is basically described by a variant of the multi-layered Ising model with random fields as non-stationary time series. Hyper-parameters appearing in the probabilistic model are estimated by means of minimizing the 'cumulative error' in the past market history. The justification and validity of our approaches are numerically examined for several empirical data sets.
    Date: 2013–09
  9. By: Mitsuaki Murota; Jun-ichi Inoue
    Abstract: We propose a formula of time-series prediction by means of three states random field Ising model (RFIM). At the economic crisis due to disasters or international disputes, the stock price suddenly drops. The macroscopic phenomena should be explained from the corresponding microscopic view point because there are existing a huge number of active traders behind the crushes. Hence, here we attempt to model the artificial financial market in which each trader $i$ can choose his/her decision among `buying', `selling' or `staying (taking a wait-and-see attitude)', each of which corresponds to a realization of the three state Ising spin, namely, $S_{i}=+1$, -1 and $S_{i}=0$, respectively. The decision making of traders is given by the Gibbs-Boltzmann distribution with the energy function. The energy function contains three distinct terms, namely, the ferromagnetic two-body interaction term (endogenous information), random field term as external information (exogenous news), and chemical potential term which controls the number of traders who are watching the market calmly at the instance. We specify the details of the model system from the past financial market data to determine the conjugate hyper-parameters and draw each parameter flow as a function of time-step. Especially we will examine to what extent one can characterize the crisis by means of a brand-new order parameter --- `turnover' --- which is defined as the number of active traders who post their decisions $S_{i}=1,-1$, instead of $S_{i}=0$.
    Date: 2013–09

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