nep-for New Economics Papers
on Forecasting
Issue of 2013‒10‒02
thirteen papers chosen by
Rob J Hyndman
Monash University

  1. Evaluating point and density forecasts of DSGE models By Wolters, Maik H.
  2. An Early Warning System for Inflation in the Philippines Using Markov-Switching and Logistic Regression Models By Cruz , Christopher John; Mapa, Dennis
  3. Does Banque de France control inflation and unemployment? By Kitov, Ivan; KItov, Oleg
  4. Using Common Features to Understand the Behavior of Metal-Commodity Prices and Forecast them at Different Horizons By Issler, João Victor; Rodrigues, Claudia; Burjack, Rafael
  5. Testing for the Systemically Important Financial Institutions: a Conditional Approach By Sessi Tokpavi
  6. Market Index and Stock Price Direction Prediction using Machine Learning Techniques: An empirical study on the KOSPI and HSI By Yanshan Wang; In-Chan Choi
  7. A univariate analysis: Short-term forecasts of container throughput in the port of Antwerp By Rashed, Yasmine; Meersman, Hilde; Van de Voorde, Eddy; Vanelslander, Thierry
  8. Measuring return and volatility spillovers in euro area financial markets By Dimitrios P. Louzis
  9. Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling By Serena Ng; Jonathan H. Wright
  10. On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks By Ledenyov, Dimitri O.; Ledenyov, Viktor O.
  11. The impact of lead time forecasting on the bullwhip effect By Zbigniew Michna; Peter Nielsen
  12. The Rome Metropolitan Area’s population: recent demographic evolution and forecast to 2024 La popolazione dell’area metropolitana di Roma. Evoluzione demografica e previsioni al 2024 By Oliviero Casacchia; Massimiliano Crisci
  13. A stochastic generalized Nash-Cournot model for the northwestern European natural gas markets: The S-GaMMES model By Ibrahim Abada; Pierre-André Jouvet

  1. By: Wolters, Maik H.
    Abstract: This paper investigates the accuracy of forecasts from four DSGE models for inflation, output growth and the federal funds rate using a real-time dataset synchronized with the Fed's Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as accurate as the Greenbook projections for output growth and the federal funds rate. Only for inflation the model forecasts are dominated by the Greenbook projections. A comparison with forecasts from Bayesian VARs shows that the economic structure of the DSGE models which is useful for the interpretation of forecasts does not lower the accuracy of forecasts. Combining forecasts of several DSGE models increases precision in comparison to individual model forecasts. Comparing density forecasts with the actual distribution of observations shows that DSGE models overestimate uncertainty around point forecasts. --
    Keywords: DSGE models,Bayesian VAR,forecasting,model uncertainty,forecast combination,density forecasts,real-time data,Greenbook
    JEL: C53 E31 E32 E37
    Date: 2013
  2. By: Cruz , Christopher John; Mapa, Dennis
    Abstract: With the adoption of the Bangko Sentral ng Pilipinas (BSP) of the Inflation Targeting (IT) framework in 2002, average inflation went down in the past decade from historical average. However, the BSP’s inflation targets were breached several times since 2002. Against this backdrop, this paper develops an early warning system (EWS) model for predicting the occurrence of high inflation in the Philippines. Episodes of high and low inflation were identified using Markov-switching models. Using the outcomes of regime classification, logistic regression models are then estimated with the objective of quantifying the possibility of the occurrence of high inflation episodes. Empirical results show that the proposed EWS model has some potential as a complementary tool in the BSP’s monetary policy formulation based on the in-sample and out-of sample forecasting performance.
    Keywords: Inflation Targeting, Markov Switching Models, Early Warning System
    JEL: C5 C52 E37
    Date: 2013
  3. By: Kitov, Ivan; KItov, Oleg
    Abstract: We re-estimate statistical properties and predictive power of a set of Phillips curves, which are expressed as linear and lagged relationships between the rates of inflation, unemployment, and change in labour force. For France, several relationships were estimated eight years ago. The change rate of labour force was used as a driving force of inflation and unemployment within the Phillips curve framework. Following the original problem formulation by Fisher and Phillips, the set of nested models starts with a simplistic version without autoregressive terms and one lagged term of explanatory variable. The lag is determined empirically together with all coefficients. The model is estimated using the Boundary Element Method (BEM) with the least squares method applied to the integral solutions of the differential equations. All models include one structural break might be associated with revisions to definitions and measurement procedures in the 1980s and 1990s as well as with the change in monetary policy in 1994-1995. For the GDP deflator, our original model provided a root mean squared forecast error (RMSFE) of 1.0% per year at a four-year horizon for the period between 1971 and 2004. The same RMSFE is estimated with eight new readings obtained since 2004. The rate of CPI inflation is predicted with RMSFE=1.5% per year. For the naive (no change) forecast, RMSFE at the same time horizon is 2.95% and 3.3% per year, respectively. Our model outperforms the naive one by a factor of 2 to 3. The relationships for inflation were successfully tested for cointegration. We have formally estimated several vector error correction (VEC) models for two measures of inflation. In the VAR representation, these VECMs are similar to the Phillips curves. At a four year horizon, the estimated VECMs provide significant statistical improvements on the results obtained by the BEM: RMSFE=0.8% per year for the GDP deflator and ~1.2% per year for CPI. For a two year horizon, the VECMs improve RMSFEs by a factor of 2, with the smallest RMSFE=0.5% per year for the GDP deflator. This study has validated the reliability and accuracy of the linear and lagged relationships between inflation, unemployment, and the change in labour force between 1970 and 2012.
    Keywords: monetary policy, inflation, unemployment, labour force, Phillips curve, measurement error, forecasting, cointegration, France
    JEL: C32 E31 E6 J21 J64
    Date: 2013–09–27
  4. By: Issler, João Victor; Rodrigues, Claudia; Burjack, Rafael
    Abstract: The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual frequencies. Data consists of metal-commodity prices at a monthly and quarterly frequencies from 1957 to 2012, extracted from the IFS, and annual data, provided from 1900-2010 by the U.S. Geological Survey (USGS). We also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009). We investigate short- and long-run comovement by applying the techniques and the tests proposed in the common-feature literature. One of the main contributions of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding out-of-sample forecasts, our main contribution is to show the benefits of forecast-combination techniques, which outperform individual-model forecasts -- including the random-walk model. We use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates and functional forms to forecast the returns and prices of metal commodities. Using a large number of models (N large) and a large number of time periods (T large), we apply the techniques put forth by the common-feature literature on forecast combinations. Empirically, we show that models incorporating (short-run) common-cycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation.
    Date: 2013–09–26
  5. By: Sessi Tokpavi
    Abstract: We introduce in this paper a testing approach that allows checking whether two financial institutions are systemically equivalent, with systemic risk measured by CoVaR (Adrian and Brunnermeier, 2011). The test compares the difference in CoVaR forecasts for two financial institutions via a suitable loss function that has an economic content. Our testing approach differs from those in the literature in the sense that it is conditional, and helps evaluating in a forward-looking manner, the extent to which statistically significant differences in CoVaR forecasts can be attributed to lag values of market state variables. Moreover, the test can be used to identify systemically important financial institutions (SIFIs). Extensive Monte Carlo simulations show that the test has desirable small sample properties. With an application on a sample including 70 large U.S. financial institutions, our conditional test using market state variables such as VIX and various yield spreads, reveals more (resp. less) heterogeneity in the systemic profiles of these institutions compared to its unconditional version, in crisis (resp. non-crisis) period. It also emerges that the systemic ranking provided by our testing approach is a good forecast of a financial institution's sensitivity to a crisis. This is in contrast to the ranking obtained directly using CoVaR forecasts which has less predictive power because of estimation uncertainty.
    Keywords: Systemic Risk, SIFIs, CoVaR, Estimation Uncertainty, Conditional Predictive Ability Test.
    JEL: G32 C53 C58
    Date: 2013
  6. By: Yanshan Wang; In-Chan Choi
    Abstract: The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. In this paper, we propose an empirical study on the Korean and Hong Kong stock market with an integrated machine learning framework that employs Principal Component Analysis (PCA) and Support Vector Machine (SVM). We try to predict the upward or downward direction of stock market index and stock price. In the proposed framework, PCA, as a feature selection method, identifies principal components in the stock market movement and SVM, as a classifier for future stock market movement, processes them along with other economic factors in training and forecasting. We present the results of an extensive empirical study of the proposed method on the Korean composite stock price index (KOSPI) and Hangseng index (HSI), as well as the individual constituents included in the indices. In our experiment, ten years data (from January 1st, 2002 to January 1st, 2012) are collected and schemed by rolling windows to predict one-day-ahead directions. The experimental results show notably high hit ratios in predicting the movements of the individual constituents in the KOSPI and HSI. The results also varify the \textit{co-movement} effect between the Korean (Hong Kong) stock market and the American stock market.
    Date: 2013–09
  7. By: Rashed, Yasmine; Meersman, Hilde; Van de Voorde, Eddy; Vanelslander, Thierry
    Abstract: The relation between transportation and economic activity is complex and interrelated. From this complexity arises the difficulty of forecasting the port throughput, which plays an essential part in planning the port operations, not only for the port stakeholders but also for the development of hinterland activities and connectivity network. The aim of the univariate method used in this paper is to estimate short-term forecasting and to provide initial insight of the stochastic process for further research in the multiple regression analyses. The SARIMA model is found to be quite appropriate for the container throughput since seasonality exists in the time series. The advantage of the univariate method is that it is independent of other variables, provide a generic and a simple model that can be updated frequently and the model can be applied to other ports.
    Date: 2013–09
  8. By: Dimitrios P. Louzis (Bank of Greece)
    Abstract: This study examines the return (price) and volatility spillovers among the money, stock, foreign exchange and bond markets of the euro area, utilizing the forecast-error variance decomposition framework of a generalized VAR model proposed by Diebold and Yilmaz (2012) [Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 23, 57-66]. Our empirical results, based on a data set covering a twelve-year period (2000-2012), suggest a high level of total return and volatility spillover effects throughout the sample, indicating that, on average, more than the 50% of the forecast-error variance of the respective VAR model is explained by spillover effects. Moreover, the stock market is identified as the main transmitter of both return and volatility spillovers even during the current sovereign debt crisis. With the exception of the period 2011-2012, bonds of the periphery countries under financial support mechanisms are receivers of return spillovers, whereas, they transmit volatility spillovers to other markets diachronically. Finally, we identify the key role of money market in volatility transmission in the euro area during the outbreak of the global financial crisis.
    Keywords: Asset markets; Spillovers; Vector Autoregressive; Euro area; Financial Crisis.
    JEL: G01 G10 G20 C53
    Date: 2013–03
  9. By: Serena Ng; Jonathan H. Wright
    Abstract: This paper provides a survey of business cycle facts, updated to take account of recent data. Emphasis is given to the Great Recession which was unlike most other post-war recessions in the US in being driven by deleveraging and financial market factors. We document how recessions with financial market origins are different from those driven by supply or monetary policy shocks. This helps explain why economic models and predictors that work well at some times do poorly at other times. We discuss challenges for forecasters and empirical researchers in light of the updated business cycle facts.
    JEL: C22 C32 E32 E37
    Date: 2013–09
  10. By: Ledenyov, Dimitri O.; Ledenyov, Viktor O.
    Abstract: The central banks introduce and implement the monetary and financial stabilities policies, going from the accurate estimations of national macro-financial indicators such as the Gross Domestic Product (GDP). Analyzing the dependence of the GDP on the time, the central banks accurately estimate the missing observations in the financial time series with the application of different interpolation models, based on the various filtering algorithms. The Stratonovich – Kalman – Bucy filtering algorithm in the state space interpolation model is used with the purpose to interpolate the real GDP by the US Federal Reserve and other central banks. We overviewed the Stratonovich – Kalman – Bucy filtering algorithm theory and its numerous applications. We describe the technique of the accurate characterization of the economic and financial time series with application of state space methods with the Stratonovich – Kalman - Bucy filtering algorithm, focusing on the estimation of Gross Domestic Product by the Swiss National Bank. Applying the integrative thinking principles, we developed the software program and performed the computer modeling, using the Stratonovich – Kalman – Bucy filtering algorithm for the accurate characterization of the Australian GDP, German GDP and the USA GDP in the frames of the state-space model in Matlab. We also used the Hodrick-Prescott filter to estimate the corresponding output gaps in Australia, Germany and the USA. We found that the Australia, Germany on one side and the USA on other side have the different business cycles. We believe that the central banks can use our special software program with the aim to greatly improve the national macroeconomic indicators forecast by making the accurate characterization of the financial time-series with the application of the state-space models, based on the Stratonovich – Kalman – Bucy filtering algorithm.
    Keywords: Wiener filtering theory, Stratonovich optimal non-linear filtering theory, Stratonovich – Kalman – Bucy filtering algorithm, state space interpolation technique, financial time-series, nonlinearities, stochastic volatility; Markov switching, Bayesian estimation. Gaussian distribution, econophysics, econometrics, central bank, integrative thinking.
    JEL: C4 C46 C5 C51 C52 C53 C58 C6 E5 E58
    Date: 2013–09–27
  11. By: Zbigniew Michna; Peter Nielsen
    Abstract: In this article we quantify the bullwhip effect (the variance amplification in replenishment orders) when demands and lead times are predicted in a simple two-stage supply chain with one supplier and one retailer. In recent research the impact of stochastic order lead time on the bullwhip effect is investigated, but the effect of needing to predict / estimate the lead time is not considered in the supply chain models. Under uncertainty conditions it is necessary to estimate the lead time for a member of the supply chain to place an order. We find a new cause of the bullwhip effect in the form of lead time forecasting and we give an exact form of the bullwhip effect measure (the ratio of variances) when demands and lead times are predicted by moving averages. In the bullwhip effect measure we discover two terms amplifying the effect which are the result of lead time estimation
    Date: 2013–09
  12. By: Oliviero Casacchia; Massimiliano Crisci
    Abstract: This working paper presents the results of a research project financed by the Province of Rome and conducted by the CNR IRPPS and the Department of Statistical Sciences, Sapienza University of Rome. During the last decade international migration and urban spread have been the main demographic processes in the Rome metropolitan area (RMA). On these population trends was based a range of hypotheses referred to the future demographic dynamics in the period 2009-24. Population forecasts have been conducted applying two multiregional models, stochastic and deterministic, on five concentric subareas dividing the territory of Rome (core, urban periphery and three rings of metropolitan peripheries). According to the scenarios, the population of the RMA is going to increase and to get older. Moreover, if urban policies don’t change, the demographic weight of the city of Rome is going to decrease further. Il working paper presenta i risultati di un progetto di ricerca finanziato dalla Provincia di Roma e condotto dall’IRPPS-CNR e dal Dipartimento di Scienze Statistiche della Sapienza Università di Roma. Nel corso dell’ultimo decennio le immigrazioni straniere e i trasferimenti residenziali dal core urbano alle periferie hanno rappresentato gli eventi demografici più rilevanti nell’area metropolitana di Roma. Di queste tendenze si è tenuto conto nel definire le ipotesi alla base degli scenari evolutivi della popolazione dal 2009 al 2024. Le previsioni sono state condotte applicando due modelli previsivi multiregionali, di tipo stocastico e deterministico, sulle cinque sub-aree concentriche nelle quali è stata suddivisa l’area romana. Gli scenari concordano nel prevedere un incremento e un invecchiamento della popolazione dell’area, oltre ad un’ulteriore diminuzione del peso demografico della città di Roma.
    Keywords: Population forecast; Rome; Demographic dynamics; Stochastic method; Italian Metropolitan areas Previsioni demografiche; Roma; Dinamiche demografiche; Metodo stocastico; Aree metropolitane italiane
  13. By: Ibrahim Abada; Pierre-André Jouvet
    Abstract: This article presents a stochastic dynamic Generalized Nash-Cournot model to describe the evolution of the natural gas markets. The major gas chain players are depicted including: producers, consumers, storage, and pipeline operators, as well as intermediate local traders. Our economic structure description takes into account market power and the demand representation captures the possible fuel substitution that can be made between oil, coal, and natural gas in the overall fossil energy consumption. The demand is made random because of the oil price fluctuations and we take into account long-term contracts in an endogenous way. The model is applied to represent the European natural gas market and to forecast, until 2035, after a calibration process, patterns of consumption, prices, production, and long-term contract prices and volumes. In terms of policy implications, we show how the perception of the oil price’s uncertainty modifies the gas long-term contract volumes in Europe between the producers and the midstreamers. Finally, we define the value, gain and loss of the stochastic solution adapted to our model and calculate them for each market actor.
    Keywords: Energy markets modeling, Game theory, Generalized Nash-Cournot equilibria, Quasi-Variational Inequality, Equilibrium problems, Stochastic programming
    JEL: C61 C73 D24 D43 L13 Q41
    Date: 2013

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