nep-for New Economics Papers
on Forecasting
Issue of 2009‒11‒14
fifteen papers chosen by
Rob J Hyndman
Monash University

  1. On economic evaluation of directional forecasts By Oliver Blaskowitz; Helmut Herwartz
  2. Predicting unemployment in short samples with internet job search query data By Francesco, D'Amuri
  3. Does Accounting for Spatial Effects Help Forecasting the Growth of Chinese Provinces? By Eric Girardin; Konstantin A. Kholodilin
  4. Nonlinearity, Nonstationarity, and Spurious Forecasts By Marmer, Vadim
  5. Forecasting Inflation Using Dynamic Model Averaging By Gary Koop; Dimitris Korobilis
  6. Demand Forecasting in the Early Stage of the Technology's Life Cycle Using Bayesian update By Chul-Yong Lee; Jongsu LEE
  7. Nowcasting Euro Area Economic Activity in Real-Time: The Role of Confidence Indicators By Domenico Giannone; Lucrezia Reichlin; Saverio Simonelli
  8. A Forecasting Model Incorporating Replacement Purchase: Mobile Handsets in South Korea¡¯s Market By Jongsu Lee; Chul-Yong
  9. Real-time datasets really do make a difference: definitional change, data release, and forecasting By Andres Fernandez; Norman R. Swanson
  10. Density forecasting of the Dow Jones share index By Öller, L-E; Stockhammar, P
  11. On Loss Functions and Ranking Forecasting Performances of Multivariate Volatility Models By Sébastien Laurent; Jeroen V.K. Rombouts; Francesco Violante
  12. Retail Forecast Holiday 2009 By O'Brien, Meghan
  13. Inflation Volatility and Forecast Accuracy By Jamie Hall; Jarkko Jääskelä
  14. Predictive density construction and accuracy testing with multiple possibly misspecified diffusion models By Valentina Corradi; Norman R. Swanson
  15. Modelling Realized Covariances By Xin Jin; John M Maheu

  1. By: Oliver Blaskowitz; Helmut Herwartz
    Abstract: It is commonly accepted that information is helpful if it can be exploited to improve a decision mak- ing process. In economics, decisions are often based on forecasts of up{ or downward movements of the variable of interest. We point out that directional forecasts can provide a useful framework to assess the economic forecast value when loss functions (or success measures) are properly formu- lated to account for realized signs and realized magnitudes of directional movements. We discuss a general approach to evaluate (directional) forecasts which is simple to implement, robust to outlying or unreasonable forecasts and which provides an economically interpretable loss/success functional framework. As such, the measure of directional forecast value is a readily available alternative to the commonly used squared error loss criterion.
    Keywords: Directional forecasts, directional forecast value, forecast evaluation, economic forecast value, mean squared forecast error, mean absolute forecast error
    JEL: C52 E17 E27 E37 E47 F17 F37 F47
    Date: 2009–10
  2. By: Francesco, D'Amuri
    Abstract: This article tests the power of a novel indicator based on job search related web queries in predicting quarterly unemployment rates in short samples. Augmenting standard time series specifications with this indicator definitely improves out-of-sample forecasting performance at nearly all in-sample interval lengths and forecast horizons, both when compared with models estimated on the same or on a much longer time series interval.
    Keywords: Google econometrics; Forecast comparison; Keyword search; Unemployment; Time series models.
    JEL: C53 E27 J60 J64 C22
    Date: 2009–10–30
  3. By: Eric Girardin; Konstantin A. Kholodilin
    Abstract: In this paper, we make multi-step forecasts of the annual growth rates of the real GRP for each of the 31 Chinese provinces simultaneously. Beside the usual panel data models, we use panel models that explicitly account for spatial dependence between the GRP growth rates. In addition, the possibility of spatial effects being different for different groups of provinces (Interior and Coast) is allowed. We find that both pooling and accounting for spatial effects helps substantially improve the forecast performance compared to the benchmark models estimated for each of the provinces separately. It was also shown that effect of accounting for spatial dependence is even more pronounced at longer forecasting horizons (the forecast accuracy gain as measured by the root mean squared forecast error is about 8% at 1-year horizon and exceeds 25% at 13- and 14-year horizon).
    Keywords: Chinese provinces, forecasting, dynamic panel model, spatial autocorrelation, group-specific spatial dependence
    JEL: C21 C23 C53
    Date: 2009
  4. By: Marmer, Vadim
    Abstract: Implications of nonlinearity, nonstationarity and misspecification are considered from a forecasting perspective. Our model allows for small departures from the martingale difference sequence hypothesis by including a nonlinear component, formulated as a general, integrable transformation of the I(1) predictor. We assume that the true generating mechanism is unknown to the econometrician and he is therefore forced to use some approximating functions. It is shown that in this framework the linear regression techniques lead to spurious forecasts. Improvements of the forecast accuracy are possible with properly chosen nonlinear transformations of the predictor. The paper derives the limiting distribution of the forecasts' MSE. In the case of square integrable approximants, it depends on the Lâ‚‚-distance between the nonlinear component and approximating function. Optimal forecasts are available for a given class of approximants.
    Keywords: Forecasting; integrated time series; misspecified models; nonlinear transformations; stock returns
    Date: 2009–11–03
  5. By: Gary Koop (Department of Economics, University of Strathclyde and RCEA); Dimitris Korobilis (Department of Economics, University of Strathclyde and RCEA)
    Abstract: There is a large literature on forecasting inflation using the generalized Phillips curve (i.e. using forecasting models where inflation depends on past inflation, the unemployment rate and other predictors). The present paper extends this literature through the use of econometric methods which incorporate dynamic model averaging. These not only allow for coefficients to change over time (i.e. the marginal effect of a predictor for inflation can change), but also allows for the entire forecasting model to change over time (i.e. different sets of predictors can be relevant at different points in time). In an empirical exercise involving quarterly US inflation, we fi…nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark approaches (e.g. random walk or recursive OLS forecasts) and more sophisticated approaches such as those using time varying coefficient models.
    Keywords: Option Pricing; Modular Neural Networks; Non-parametric Methods
    JEL: E31 E37 C11 C53
    Date: 2009–01
  6. By: Chul-Yong Lee; Jongsu LEE (Technology Management, Economics and Policy Program(TEMEP), Seoul National University)
    Abstract: Forecasting demand for new technology for which few historical data observations are available is difficult but essential to successful marketing. The current study suggests an alternative forecasting methodology based on a hazard rate model using stated and revealed preferences. In estimating the hazard rate, information is derived initially through conjoint analysis based on a consumer survey and then updated using Bayes¡¯ theorem with available market data. Based on the results of the empirical analysis, the model described here can significantly improve demand forecasting for newly introduced technologies.
    Keywords: demand forecasting, conjoint analysis, Bayesian update, telematics service
    Date: 2009–04
  7. By: Domenico Giannone (ECARES, Université Libre de Bruxelles and CEPR); Lucrezia Reichlin (London Business School and CEPR); Saverio Simonelli (Università di Napoli Federico II, EUI and CSEF)
    Abstract: This paper assesses the role of surveys for the early estimates of GDP in the euro area in a model-based automated procedures which exploits the timeliness of their release. The analysis is conducted using both an historical evaluation and a real time case study on the current conjuncture.
    Keywords: Forecasting; factor model; real time data; large data sets; survey
    JEL: E52 C33 C53
    Date: 2009–11–06
  8. By: Jongsu Lee; Chul-Yong (Technology Management, Economics and Policy Program(TEMEP), Seoul National University)
    Abstract: The paper introduces a replacement forecasting model that operates at the brand level and overcomes limitations of existing models. The model (1) consists of a diffusion model and a time series model; (2) separately identifies the diffusion of first-time purchases and that of replacement purchases; (3) reflects brands¡¯ competitive factors affecting product diffusion; and (4) characterizes consumers¡¯ different replacement cycles.The model is applied to South Korea¡¯s mobile handset market. The model performs well in terms of its fit and forecasting when compared with other forecasting models incorporating replacement and repeat purchases. The usefulness of the model stems from its ability to describe complicated environments and its flexibility in including multiple factors that drive diffusion in the regression analysis.
    Keywords: Replacement, Diffusion model, Mobile handset market
    Date: 2009–04
  9. By: Andres Fernandez; Norman R. Swanson
    Abstract: In this paper, the authors empirically assess the extent to which early release inefficiency and definitional change affect prediction precision. In particular, they carry out a series of ex-ante prediction experiments in order to examine: the marginal predictive content of the revision process, the trade-offs associated with predicting different releases of a variable, the importance of particular forms of definitional change, which the authors call "definitional breaks," and the rationality of early releases of economic variables. An important feature of our rationality tests is that they are based solely on the examination of ex-ante predictions, rather than being based on in-sample regression analysis, as are many tests in the extant literature. Their findings point to the importance of making real-time datasets available to forecasters, as the revision process has marginal predictive content, and because predictive accuracy increases when multiple releases of data are used when specifying and estimating prediction models. The authors also present new evidence that early releases of money are rational, whereas prices and output are irrational. Moreover, they find that regardless of which release of our price variable one specifies as the "target" variable to be predicted, using only "first release" data in model estimation and prediction construction yields mean square forecast error (MSFE) "best" predictions. On the other hand, models estimated and implemented using "latest available release" data are MSFE-best for predicting all releases of money. The authors argue that these contradictory findings are due to the relevance of definitional breaks in the data generating processes of the variables that they examine. In an empirical analysis, they examine the real-time predictive content of money for income, and they find that vector autoregressions with money do not perform significantly worse than autoregressions, when predicting output during the last 20 years.
    Keywords: Economic forecasting ; Econometrics
    Date: 2009
  10. By: Öller, L-E; Stockhammar, P
    Abstract: The distribution of differences in logarithms of the Dow Jones share index is compared to the normal (N), normal mixture (NM) and a weighted sum of a normal and an Assymetric Laplace distribution (NAL). It is found that the NAL fits best. We came to this result by studying samples with high, medium and low volatility, thus circumventing strong heteroscedasticity in the entire series. The NAL distribution also fitted economic growth, thus revealing a new analogy between financial data and real growth.
    Keywords: Density forecasting; heteroscedasticity; mixed Normal- Asymmetric Laplace distribution; Method of Moments estimation; connection with economic growth.
    JEL: C20
    Date: 2009
  11. By: Sébastien Laurent; Jeroen V.K. Rombouts; Francesco Violante
    Abstract: A large number of parameterizations have been proposed to model conditional variance dynamics in a multivariate framework. However, little is known about the ranking of multivariate volatility models in terms of their forecasting ability. The ranking of multivariate volatility models is inherently problematic because it requires the use of a proxy for the unobservable volatility matrix and this substitution may severely affect the ranking. We address this issue by investigating the properties of the ranking with respect to alternative statistical loss functions used to evaluate model performances. We provide conditions on the functional form of the loss function that ensure the proxy-based ranking to be consistent for the true one – i.e., the ranking that would be obtained if the true variance matrix was observable. We identify a large set of loss functions that yield a consistent ranking. In a simulation study, we sample data from a continuous time multivariate diffusion process and compare the ordering delivered by both consistent and inconsistent loss functions. We further discuss the sensitivity of the ranking to the quality of the proxy and the degree of similarity between models. An application to three foreign exchange rates, where we compare the forecasting performance of 16 multivariate GARCH specifications, is provided.
    Keywords: Volatility, multivariate GARCH, Matrix norm, Loss function, Model confidence set
    JEL: C10 C32 C51 C52 C53 G10
    Date: 2009
  12. By: O'Brien, Meghan
    Abstract: Despite pronouncements that the recession ended in the third quarter of 2009, the prospects for the holiday retail season remain bleak. This report describes the factors that will continue to suppress retail sales for the holiday season and beyond and forecasts how different retail categories will fare this holiday season.
    Date: 2009–10–30
  13. By: Jamie Hall (Reserve Bank of Australia); Jarkko Jääskelä (Reserve Bank of Australia)
    Abstract: This paper examines the statistical properties of inflation in a sample of inflation-targeting and non-inflation-targeting countries. First, it analyses the time-varying volatility of a measure of the persistent component of inflation. Based on this measure, inflation-targeting countries (Australia, Canada, New Zealand, Sweden and the United Kingdom) have experienced a relatively more pronounced fall in the volatility of inflation than non-inflation-targeting countries (Austria, France, Germany, Japan and the United States). But it is hard to say whether inflation is more volatile in inflation-targeting or non-inflation-targeting countries. Second, it analyses whether inflation became easier to forecast after the introduction of inflation targeting. It finds that inflation became easier to forecast in both inflation-targeting and non-inflation-targeting countries; the improvement was greater for the former group but forecast errors remain smaller for the latter group.
    Keywords: inflation; time series econometrics
    JEL: C53 E37
    Date: 2009–10
  14. By: Valentina Corradi; Norman R. Swanson
    Abstract: This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, the authors first outline a simple simulation-based framework for constructing predictive densities for one-factor and stochastic volatility models. Then, they construct accuracy assessment tests that are in the spirit of Diebold and Mariano (1995) and White (2000). In order to establish the asymptotic properties of their tests, the authors also develop a recursive variant of the nonparametric simulated maximum likelihood estimator of Fermanian and Salanié (2004). In an empirical illustration, the predictive densities from several models of the one-month federal funds rates are compared.
    Keywords: Econometric models - Evaluation ; Stochastic analysis
    Date: 2009
  15. By: Xin Jin; John M Maheu
    Abstract: This paper proposes a new dynamic model of realized covariance (RCOV) matrices based on recent work in time-varying Wishart distributions. The specifications can be linked to returns for a joint multivariate model of returns and covariance dynamics that is both easy to estimate and forecast. Realized covariance matrices are constructed for 5 stocks using high-frequency intraday prices based on positive semi-definite realized kernel estimates. We extend the model to capture the strong persistence properties in RCOV. Out-of-sample performance based on statistical and economic metrics show the importance of this. We discuss which features of the model are necessary to provide improvements over a traditional multivariate GARCH model that only uses daily returns.
    Keywords: eigenvalues, dynamic conditional correlation, predictive likelihoods, MCMC
    JEL: C11 C32 C53
    Date: 2009–11–10

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