International Journal of Forecasting

Volume 34, n° 4, October-November 2018

  • DRESS Korbinian, LESSMANN Stefannn, VON METTENHEIM Hans-Jörg - Residual value forecasting using asymmetric cost functions, pp. 551-565
  • MOISAN Stella, HERRERA Rodrigo, CLEMENTS Adam - A dynamic multiple equation approach for forecasting PMpollution in Santiago, Chile, pp. 566-581
  • GILLARD Jonathan, USEVICH Ko, pp. 582-597
  • CAMACHO Maximoo, PEREZ-QUIROS Gabrielos, PONCHELA Pilar - Markov-switching dynamic factor models in real time, pp. 598-611
  • BEMMAOR Albert C., ZHENG Li - The diffusion of mobile social networking: Further study, pp. 612-621
  • HERRERA Ana María, HU Liang, PASTOR Daniel - Forecasting crude oil price volatility, pp. 622-635
  • GUIDOLIN Massimon, THORNTON Daniel L. - Predictions of short-term rates and the expectations hypothesis, pp. 636-664
  • WANG Jue, ATHANASOPOULOS George, HYNDMAN Rob J., WANG Shouyang - Crude oil price forecasting based on internet concern using an extreme learning machin, pp. 665-677
  • FORGIONE Antonio Fabio, MIGLIARDO Carlo - Forecasting distress in cooperative banks: The role of asset quality, pp. 678-695
  • KHAN Urmee, LIELI Robert P. - Information flow between prediction markets, polls and media: Evidence from the 2008 presidential primaries, pp. 696-710
  • CHINI Emilio Zanetti - Forecasting dynamically asymmetric fluctuations of the U.S. business cycle, pp. 711-732
  • ARDIA David, BLUTEAU Keven, BOUDT Kris, CATANIA Leopoldo - Forecasting risk with Markov-switching GARCH models:A large-scale performance study, pp. 733-747
  • DANTAS Tiago Mendes, OLIVEIRA Fernando Luiz Cyrino - Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing, pp. 748-761
  • THOREY J., CHAUSSIN C., MALLET V. - Ensemble forecast of photovoltaic power with online CRPS learnin, pp. 762-773
  • FORONI Claudia, GUÉRIN Pierre, MARCELLINO Massimiliano - Using low frequency information for predicting high frequency variables, pp. 774-787
  • BERGE Travis J. - Understanding survey-based inflation expectations, pp. 788-801
  • MAKRIDAKIS Spyros, SPILIOTIS Evangelos, ASSIMAKOPOULOS Vassilios - The M4 Competition: Results, findings, conclusion and way forward, pp. 802-808
  • KOLASA Marcin, RUBASZEK Michał - Does the foreign sector help forecast domestic variables in DSGE models? pp. 809-821
  • SEAMAN Brian - Considerations of a retail forecasting practitioner, pp. 822-829
  • KOLASSA Stephan - Commentary on Retail Forecasting, pp. 830-831
  • BOYLAN John E. - Commentary on Retail Forecasting, pp. 832-834
  • MAKRIDAKIS Spyros, ASSIMAKOPOULOS Vassilios, SPILIOTIS Evangelos - Objectivity, reproducibility and replicability in forecasting research, pp. 835-838

 (résumés du n° 4/2018)

Volume 34, n° 3, July-September 2018

  • MENG Xiaochun, TAYLOR James W. - An approximate long-memory range-based approach for value at risk estimation, pp. 377-388
  • SMITH Michael Stanley, MANEESOONTHORN Worapree - Inversion copulas from nonlinear state space models with an application to inflation forecasting, pp. 389-407
  • SMEEKES Stephan, WIJLER Etienne - Macroeconomic forecasting using penalized regression methods, pp. 408-430
  • MOHLER George, CARTER Jeremy, RAJE Rajeev - Improving social harm indices with a modulated Hawkes process, pp. 431-439
  • GOGAS Periklis, PAPADIMITRIOU Theophilos, AGRAPETIDOU Anna - Forecasting bank failures and stress testing: A machine learning approach, pp. 440-455
  • LI Feng, KANG Yanfei - Improving forecasting performance using covariate-dependent copula models, pp. 456-476
  • BARAN Sándor, LERCH Sebastian - Combining predictive distributions for the statistical post-processing of ensemble forecasts, pp. 477-496
  • SAHAMKHADAM Maziar, STEPHAN Andreas, ÖSTERMARK Ralf - Portfolio optimization based on GARCH-EVT-Copula forecasting models, pp. 497-506
  • LU Emiao, HANDL Julia, XU Dong-ling - Determining analogies based on the integration of multiple information sources, pp. 507-528
  • DAVID Mathieu, LUIS Mazorra Aguiar, LAURET Philippe - Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous dat, pp. 527-547

Volume 34, n° 2, April-June 2018

  • BERK K., HOFFMANN A., MÜLLER A. - Probabilistic forecasting of industrial electricity load with regime switching behavior, pp. 147-162
  • DE BAETS Shari, HARVEY Nigel - Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support, pp. 163-180
  • CLEMENTS Michael P. - Are macroeconomic density forecasts informative? pp. 181-198
  • STIERS Dieter, DASSONNEVILLE Ruth - Affect versus cognition: Wishful thinking on election day: An analysis using exit poll data from Belgium, pp. 199-215
  • MEIJERING Jurian Vincent, TOBI Hilde - The effects of feeding back experts’ own initial ratings in Delphi studies: A randomized trial, pp. 216-224
  • HARCHAOUI Tarek M., JANSSEN Robert V. - How can big data enhance the timeliness of official statistics?: The case of the U.S. consumer price index, pp. 225-234
  • LEITER Debra, MURR Andreas, RAMÍREZ Ericka Rascón, STEGMAIER Mary - Social networks and citizen election forecasting: The more friends the better, pp. 235-248
  • ANTUNES António, BONFIM Diana, MONTEIRO Nuno, RODRIGUES Paulo M.M. - Forecasting banking crises with dynamic panel probit models, pp. 249-275
  • BEKIERMAN Jeremias, MANNER Hans - Forecasting realized variance measures using time-varying coefficient models, pp. 276-287
  • NIBBERING Didier, PAAP Richard, VAN DER WEL Michel - What do professional forecasters actually predict? pp. 288-311
  • KEITH Ord, ROBERT Fildes, NIKOLAOS Kourentzes - Book Review: Principles of business forecasting, 2nd ed., pp. 312-313
  • YOUNG Peter C. - Data-based mechanistic modelling and forecasting globally averaged surface temperature, pp. 314-335
  • ALLEN P. Geoffrey - Commentary on “Data-based mechanistic modelling and forecasting globally averaged surface temperature”, pp. 336-338

Special Section: Forecasting with big data

  • KIM Hyun Hak, SWANSON Norman R. - Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods, pp. 339-354
  • TOBBACK Ellen, NAUDTS Hans, DAELEMANS Walter,DE FORTUNY Enric Junqué, MARTENS David - Belgian economic policy uncertainty index: Improvement through text mining, pp. 355-365
  • GALBRAITH John W., TKACZ Greg - Nowcasting with payments system data, pp. 366-376

(résumés du n° 2/2018)

Volume 34, n° 1, January-March 2018

  • SNUDDEN Stephen - Targeted growth rates for long-horizon crude oil price forecasts, pp. 1-16
  • PEETERS Thomas - Testing the Wisdom of Crowds in the field: Transfermarkt valuations and international soccer results, pp. 17-29
  • FAN-OSUALA Onochie, ZANTEDESCHI Daniel, JANK Wolfgang - Using past contribution patterns to forecast fundraising outcomes in crowdfunding, pp. 30-44
  • DE ALMEIDA Daniel, HOTTA Luiz K., RUIZ Esther - MGARCH models: Trade-off between feasibility and flexibility, pp. 45-63
  • CHAN Felix, PAUWELS Laurent L. - Some theoretical results on forecast combinations, pp. 64-74
  • HORTA Eduardo, ZIEGELMANN Flavio - Dynamics of financial returns densities: A functional approach applied to the Bovespa intraday index, pp. 75-88
  • LUO Jian, HONG Tao, FANG Shu-Cherng - Benchmarking robustness of load forecasting models under data integrity attacks, pp. 89-104
  • KNÜPPEL Malte - Forecast-error-based estimation of forecast uncertainty when the horizon is increased, pp. 105-116
  • KOURENTZES Nikolaos - Demand Forecasting for Managers, Stephan Kolassa, Enno Siemsen, pp. 117-118
  • HENDRY David F. - Deciding between alternative approaches in macroeconomics, pp. 119-135
  • PROIETTI Tommaso - Discussion of the paper “Deciding between alternative approaches in macroeconomics”, pp. 136-138
  • PEREZ-QUIROS Gabriel - Discussion of the paper “Deciding between alternative approaches in macroeconomics”, pp. 139-141
  • HENDRY David F. - Response to the Discussants of ‘Deciding between alternative approaches in macroeconomics’, pp. 142-146

(résumés du n° 1/2018)

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