nep-for New Economics Papers
on Forecasting
Issue of 2009‒07‒03
twenty papers chosen by
Rob J Hyndman
Monash University

  1. General correcting formula of forecasting? By Harin, Alexander
  2. Forecasting the World Economy in the Short-Term. By Audrone Jakaitiene; Stéphane Dées
  3. A nonparametric approach to forecasting realized volatility By Adam Clements; Ralf Becker
  4. Forecast accuracy and economic gains from Bayesian model averaging using time varying weight By Lennart Hoogerheide; Richard Kleijn; Francesco Ravazzolo; Herman K. van Dijk; Marno Verbeek
  5. Forecasting Productivity Using Information from Firm-Level Data By Eric J. Bartelsman; Zoltán Wolf
  6. Forecasting Levels of log Variables in Vector Autoregressions By Gunnar Bårdsen and Helmut Lütkepohl
  7. Ñ-STING: España Short Term INdicator of Growth By Maximo Camacho; Gabriel Perez-Quiros
  8. Forecasting Aggregated Time Series Variables: A Survey By Helmut Luetkepohl
  9. Exponential Smoothing and the Akaike Information Criterion By Ralph D. Snyder; J. Keith Ord
  10. Survey Data as Coicident or Leading Indicators By Cecilia Frale; Massimiliano Marcellino; Gian Luigi Mazzi; Tommaso Proietti
  11. Forecasting realized (co)variances with a block structure Wishart autoregressive model By Bonato, Matteo; Caporin, Massimiliano; Ranaldo, Angelo
  12. Forecasting volatility and spillovers in crude oil spot, forward and future markets By Chang, C-L.; McAleer, M.; Tansuchat, R.
  13. Google Econometrics and Unemployment Forecasting By Nikolaos Askitas; Klaus F. Zimmermann
  14. Financial Development and Velocity of Money in Bangladesh: A Vector Auto- Regression Analysis By Md. Akhtaruzzaman
  15. Analyzing Macroeconomic Forecastability By Ray C. Fair
  16. Strict and Flexible Inflation Forecast Targets: An Empirical Investigation By Glenn Otto; Graham Voss
  17. A Viral Branching Model for Predicting the Spread of Electronic Word-of-Mouth By Lans, R.J.A. van der; Bruggen, G.H. van; Eliashberg, J.; Wierenga, B.
  18. Nonmanipulable Bayesian Testing By Colin Stewart
  19. Predicting gold ores price By Kitov, Ivan
  20. Predicting the price index for jewelry and jewelry products: 2009 to 2016 By Kitov, Ivan

  1. By: Harin, Alexander
    Abstract: A general correcting formula of forecasting (as a framework for long-use and standardized forecasts) is proposed. The formula provides new forecasting resources and areas of application including economic forecasting.
    Keywords: forecasting; prediction; forecasting correction; planning;
    JEL: C53 E17 F17 H68 J11
    Date: 2009–06–15
  2. By: Audrone Jakaitiene (Institute of Mathematics and Informatics, Akademijos st. 4, LT-08663 Vilnius, Lithuania.); Stéphane Dées (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.)
    Abstract: Forecasting the world economy is a difficult task given the complex inter relationships within and across countries. This paper proposes a number of approaches to forecast short-term changes in selected world economic variables and aims, first, at ranking various forecasting methods in terms of forecast accuracy and, second, at checking whether methods forecasting directly aggregate variables (direct approaches) outperform methods based on the aggregation of country-speci.c forecasts (bottom-up approaches). Overall, all methods perform better than a simple benchmark for short horizons (up to three months ahead). Among the forecasting approaches used, factor models appear to perform the best. Moreover, direct approaches outperform bottom-up ones for real variables, but not for prices. Finally, when country-specific forecasts are adjusted to match direct forecasts at the aggregate levels (top-down approaches), the forecast accuracy is neither improved nor deteriorated (i.e. top-down and bottom-up approaches are broadly equivalent in terms of country-specific forecast accuracy). JEL Classification: C53, C32, E37, F17.
    Keywords: Factor models, Forecasts, Time series models.
    Date: 2009–06
  3. By: Adam Clements (QUT); Ralf Becker (Manchester)
    Abstract: A well developed literature exists in relation to modeling and forecasting asset return volatility. Much of this relate to the development of time series models of volatility. This paper proposes an alternative method for forecasting volatility that does not involve such a model. Under this approach a forecast is a weighted average of historical volatility. The greatest weight is given to periods that exhibit the most similar market conditions to the time at which the forecast is being formed. Weighting occurs by comparing short-term trends in volatility across time (as a measure of market conditions) by the application of a multivariate kernel scheme. It is found that at a 1 day forecast horizon, the proposed method produces forecasts that are significantly more accurate than competing approaches.
    Keywords: Volatility, forecasts, forecast evaluation, model confidence set, nonparametric
    JEL: C22 G00
    Date: 2009–05–12
  4. By: Lennart Hoogerheide; Richard Kleijn; Francesco Ravazzolo; Herman K. van Dijk; Marno Verbeek (Econometric and Tinbergen Institutes, Erasmus University Rotterdam; PGGM, Zeist; Norges Bank; Econometric and Tinbergen Institutes, Erasmus University Rotterdam; Rotterdam School of Management, Erasmus University Rotterdam)
    Abstract: Several Bayesian model combination schemes, including some novel approaches that simultaneously allow for parameter uncertainty, model uncertainty and robust time varying model weights, are compared in terms of forecast accuracy and economic gains using ¯nancial and macroeconomic time series. The results indicate that the proposed time varying model weight schemes outperform other combination schemes in terms of predictive and economic gains. In an empirical application using returns on the S&P 500 index, time varying model weights provide improved forecasts with substantial economic gains in an investment strategy including transaction costs. Another empirical example refers to forecasting US economic growth over the business cycle. It suggests that time varying combination schemes may be very useful in business cycle analysis and forecasting, as these may provide an early indicator for recessions.
    Keywords: Forecast combination, Bayesian model averaging, time varying model weights, portfolio optimization, business cycle
    Date: 2009–06–23
  5. By: Eric J. Bartelsman (VU University Amsterdam); Zoltán Wolf (VU University Amsterdam)
    Abstract: This paper contributes to the productivity literature by using results from firm-level productivity studies to improve forecasts of macro-level productivity growth. The paper employs current research methods on estimating firm-level productivity to build times-series components that capture the joint dynamics of the firm-level productivity and size distributions. The main question of the paper is to assess whether the micro-aggregated components of productivity---the so-called productivity decompositions---add useful information to improve the performance of macro-level productivity forecasts. The paper explores various specifications of decompositions and various forecasting experiments. The result from these horse-races is that micro-aggregated components improve simple aggregate total factor productivity forecasts. While the results are mixed for richer forecasting specifications, the paper shows, using Bayesian model averaging techniques (BMA), that the forecasts using micro-level information were always better than the macro alternative.
    Keywords: Economic growth; production function; total factor productivity; aggregation; firm-level data data; Bayesian analysis; forecasting
    JEL: C11 C14 C32 C33 D24 O12 O47
    Date: 2009–05–14
  6. By: Gunnar Bårdsen and Helmut Lütkepohl (Department of Economics, Norwegian University of Science and Technology)
    Abstract: Sometimes forecasts of the original variable are of interest al- though a variable appears in logarithms (logs) in a system of time series. In that case converting the forecast for the log of the variable to a naive forecast of the original variable by simply applying the exponential transformation is not optimal theoretically. A simple expression for the optimal forecast un- der normality assumptions is derived. Despite its theoretical advantages the optimal forecast is shown to be inferior to the naive forecast if speci¯cation and estimation uncertainty are taken into account. Hence, in practice using the exponential of the log forecast is preferable to using the optimal forecast.
    Date: 2009–06–16
  7. By: Maximo Camacho (Universidad de Murcia); Gabriel Perez-Quiros (Banco de España)
    Abstract: We develop a dynamic factor model to compute short term forecasts of the Spanish GDP growth in real time. With this model, we compute a business cycle index which works well as an indicator of the business cycle conditions in Spain. To examine its real time forecasting accuracy, we use real-time data vintages from 2008.02 through 2009.01. We conclude that the model exhibits good forecasting performance in anticipating the recent and sudden downturn.
    Keywords: Business Cycles, Output Growth, Time Series
    JEL: E32 C22 E27
    Date: 2009–06
  8. By: Helmut Luetkepohl
    Abstract: Aggregated times series variables can be forecasted in different ways. For example, they may be forecasted on the basis of the aggregate series or forecasts of disaggregated variables may be obtained first and then these forecasts may be aggregated. A number of forecasts are presented and compared. Classical theoretical results on the relative efficiencies of different forecasts are reviewed and some complications are discussed which invalidate the theoretical results. Contemporaneous as well as temporal aggregation are considered.
    Keywords: Autoregressive moving-average process, temporal aggregation, contemporaneous aggregation, vector autoregressive moving-average process
    JEL: C22 C32
    Date: 2009
  9. By: Ralph D. Snyder; J. Keith Ord
    Abstract: Using an innovations state space approach, it has been found that the Akaike information criterion (AIC) works slightly better, on average, than prediction validation on withheld data, for choosing between the various common methods of exponential smoothing for forecasting. There is, however, a puzzle. Should the count of the seed states be incorporated into the penalty term in the AIC formula? We examine arguments for and against this practice in an attempt to find an acceptable resolution of this question.
    Keywords: Exponential smoothing, forecasting, Akaike information criterion, innovations state space approach
    JEL: C22
    Date: 2009–06–11
  10. By: Cecilia Frale; Massimiliano Marcellino; Gian Luigi Mazzi; Tommaso Proietti
    Abstract: In this paper we propose a monthly measure for the euro area Gross Domestic Product (GDP) based on a small scale factor model for mixed frequency data, featuring two factors: the first is driven by hard data, whereas the second captures the contribution of survey variables as coincident indicators. Within this framework we evaluate both the in-sample contribution of the second survey-based factor, and the short term forecasting performance of the model in a pseudo-real time experiment. We find that the survey-based factor plays a significant role for two components of GDP: Industrial Value Added and Exports. Moreover, the two factor model outperforms in terms of out of sample forecasting accuracy the traditional autoregressive distributed lags (ADL) specifications and the single factor model, with few exceptions for Exports and in growth rates.
    Keywords: Survey data, Temporal Disaggregation. Multivariate State Space Models. Dynamic factor Models. Kalman filter and smoother. Chain-linking
    JEL: E32 E37 C53
    Date: 2009
  11. By: Bonato, Matteo (University of Zurich); Caporin, Massimiliano (University of Padova); Ranaldo, Angelo (Swiss National Bank)
    Abstract: In modelling and forecasting volatility, two main trade-offs emerge: mathematical tractability versus economic interpretation and accuracy versus speed. The authors attempt to reconcile, at least partially, both trade-offs. The former trade-off is crucial for many financial applications, including portfolio and risk management. The speed/accuracy trade-off is becoming more and more relevant in an environment of large portfolios, prolonged periods of high volatility (as in the current financial crisis), and the burgeoning phenomenon of algorithmic trading in which computer-based trading rules are automatically implemented. The increased availability of high-frequency data provides new tools for forecasting variances and covariances between assets. However, there is scant literature on forecasting more than one realised volatility. Following Gourieroux, Jasiak and Sufana (Journal of Econometrics, forthcoming), the authors propose a methodology to model and forecast realised covariances without any restriction on the parameters while maintaining economic interpretability. An empirical application based on variance forecasting and risk evaluation of a portfolio of two US treasury bills and two exchange rates is presented. The authors compare their model with several alternative specifications proposed in the literature. Empirical findings suggest that the model can be efficiently used in large portfolios.
    Keywords: Wishart process; realized volatility; Granger causality; volatility spillover; Value-at-Risk
    JEL: C13 C16 C22 C51 C53
    Date: 2009–06–24
  12. By: Chang, C-L.; McAleer, M.; Tansuchat, R. (Erasmus Econometric Institute)
    Abstract: Crude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at-Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia-Pacific), which are likely to be highly correlated. This paper analyses the volatility spillover effects across and within the four markets, using three multivariate GARCH models, namely the CCC, VARMA-GARCH and VARMA-AGARCH models. A rolling window approach is used to forecast the 1-day ahead conditional correlations. The paper presents evidence of volatility spillovers and asymmetric effects on the conditional variances for most pairs of series. In addition, the forecasted conditional correlations between pairs of crude oil returns have both positive and negative trends.
    Keywords: volatility spillovers;multivariate GARCH;conditional correlations;crude oil spot prices;spot returns;forward returns;futures returns
    Date: 2009–06–16
  13. By: Nikolaos Askitas; Klaus F. Zimmermann
    Abstract: The current economic crisis requires fast information to predict economic behavior early, which is difficult at times of structural changes. This paper suggests an innovative new method of using data on internet activity for that purpose. It demonstrates strong correlations between keyword searches and unemployment rates using monthly German data and exhibits a strong potential for the method used.
    Keywords: Google, internet, keyword search, search engine, unemployment, predictions, timeseries analysis
    JEL: C22 C82 E17 E24 E37
    Date: 2009
  14. By: Md. Akhtaruzzaman
    Abstract: The study uses co-integration and vector auto-regression (VAR) techniques to identify the determinants of income velocity of money (VM) in Bangladesh, covering both narrow and broad money. The study observes that financial development affects VM negatively. The VAR estimates show that two variables, real GDP growth and financial development, jointly account for around half of the variance of speed of VM for both M1 and M2. The results show that it is important for the monetary authorities to take into account both stages of economic and financial development in forecasting VM for designing effective monetary policy in Bangladesh.[PAU WP no.0806]
    Keywords: velocity of narrow and broad money; safe limit to monetary expansion; financial development; inflation expectation; Cambridge equation of exchange; money multiplier; rate of monetization; co-integration; unit root test; VAR; forecast error variance.
    Date: 2009
  15. By: Ray C. Fair (Cowles Foundation, Yale University)
    Abstract: This paper examines whether recessions and booms are forecastable under the assumption that equity prices, housing prices, import prices, exports, and random shocks are not. Each of the 214 eight-quarter periods within the overall 1954:1--2009:1 period is examined regarding predictions of output growth and inflation. The results for low output growth vary by recession -- there is no common pattern. Of the eight recessions, three are forecast well. For four of the five that are not, the main reason for each is not knowing: 1) the random shocks, 2) import prices and equity prices, 3) exports, and 4) exports and equity prices. For the fifth -- the last one -- all five components are large contributors, including housing prices: a perfect storm.
    Keywords: Macroeconomic forecasting, Recessions, Booms
    JEL: E17
    Date: 2009–06
  16. By: Glenn Otto (School of Economics, University of New South Wales); Graham Voss (Department of Economics, University of Victoria)
    Abstract: We examine whether models of inflation forecast targeting are consistent with the observed behaviour of the central banks of Australia, Canada, and the United States. The target criteria from these models restrict the conditionally expected paths of variables targeted by the central bank, in particular inflation and the output gap. We estimate various moment conditions, providing a description of monetary policy for each central bank under different maintained hypotheses. We then test whether these estimated conditions satisfy the predictions of models of optimal monetary policy. The overall objective is to examine the extent to which and the manner in which these central banks successfully balance inflation and output objectives over the near term. For all three countries, we obtain reasonable estimates for both the strict and flexible inflation forecast targeting models, though with some qualifications. Most notably, for Australia and the United States there are predictable deviations from forecasted targets, which is not consistent with models of inflation targeting. In contrast, the results for Canada lend considerable support to simple models of flexible inflation forecast targeting.
    Keywords: Monetary policy, inflation, inflation targeting, central banks
    JEL: E31 E58
    Date: 2009–06–16
  17. By: Lans, R.J.A. van der; Bruggen, G.H. van; Eliashberg, J.; Wierenga, B. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University)
    Abstract: In a viral marketing campaign an organization develops a marketing message, and stimulates customers to forward this message to their contacts. Despite its increasing popularity, there are no models yet that help marketers to predict how many customers a viral marketing campaign will reach, and how marketers can influence this process through marketing activities. This paper develops such a model using the theory of branching processes. The proposed Viral Branching Model allows customers to participate in a viral marketing campaign by 1) opening a seeding email from the organization, 2) opening a viral email from a friend, and 3) responding to other marketing activities such as banners and offline advertising. The model parameters are estimated using individual-level data that become available in large quantities already in the early stages of viral marketing campaigns. The Viral Branching Model is applied to an actual viral marketing campaign in which over 200,000 customers participated during a six-week period. The results show that the model quickly predicts the actual reach of the campaign. In addition, the model proves to be a valuable tool to evaluate alternative what-if scenarios.
    Keywords: branching processes;Markov processes;forecasting;online marketing;viral marketing;word of mouth
    Date: 2009–05–15
  18. By: Colin Stewart
    Abstract: This paper considers the problem of testing an expert who makes probabilistic forecasts about the outcomes of a stochastic process. I show that, under general conditions on the tester's prior, a likelihood test can distinguish informed from uninformed experts with high prior probability. The test rejects informed experts on data-generating processes where the tester quickly learns the true probabilities by updating her prior. However, the set of processes on which informed experts are rejected is topologically small. These results contrast sharply with many negative results in the literature.
    Keywords: Probability forecasts, testing, experts
    JEL: C44 D81 D83
    Date: 2009–06–24
  19. By: Kitov, Ivan
    Abstract: It was demonstrated that gold ores price can be predicted at a several year horizon. The prediction consists of three steps. First, we show that the difference between producer price index and the index for gold ores is characterized by the presence of sustainable mid-term trends. Second, the evolution of the difference is predicted at a five to ten-year horizon. Considering the PPI to be practically constant over the next decade, the above difference provides a direct prediction of the price index for gold ores.
    Keywords: gold ores; prediction; PPI
    JEL: G1 E3
    Date: 2009–06–23
  20. By: Kitov, Ivan
    Abstract: It was demonstrated that jewelry and jewelry products price can be predicted at a several year horizon. The prediction consists of three steps. First, we show that the difference between producer price index and the index for jewelry and jewelry products is characterized by the presence of sustainable mid-term trends. Second, the evolution of the difference is predicted at a five to ten-year horizon. Considering the PPI to be practically constant over the next decade, the above difference provides a direct prediction of the price index for jewelry and jewelry products.
    Keywords: jewelry and jewelry products; prediction; PPI
    JEL: G1 E3
    Date: 2009–06–23

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