International Journal of Forecasting

Volume 36, n° 4, October-December 2020

• Akgun, O., Pirotte, A., Urga, G. - Forecasting using heterogeneous panels with cross-sectional dependence, pp. 1211–1227.
• Arai, N. - Investigating the inefficiency of the CBO’s budgetary projections, pp. 1290–1300.
• Aromi, J.D. - Linking words in economic discourse: Implications for macroeconomic forecasts, pp. 1517–1530.
• Bespalova, O. - GDP forecasts: Informational asymmetry of the SPF and FOMC minutes, pp. 1531–1540.
• Bruzda, J. - Demand forecasting under fill rate constraints—The case of re-order points, pp. 1342–1361.
• Bunker, K. - A two-stage model to forecast elections in new democracies, pp. 1407–1419.
• Casey, E. - Do macroeconomic forecasters use macroeconomics to forecast? International Journal of Forecasting 36, 1439–1453.
• Catania, L., Proietti, T. - Forecasting volatility with time-varying leverage and volatility of volatility effects, pp. 1301–1317.
• Clements, M.P., Fritsche, U. - Text-based data and forecasting: Editor’s introduction, pp. 1476–1477.
• Clements, M.P., Reade, J.J. - Forecasting and forecast narratives: The Bank of England Inflation Reports, pp. 1488–1500.
• Colombo, E., Pelagatti, M. - Statistical learning and exchange rate forecasting, pp. 1260–1289.
• De Caigny, A., Coussement, K., De Bock, K.W., Lessmann, S. - Incorporating textual information in customer churn prediction models based on a convolutional neural network, pp. 1563–1578.
• Gür Ali, Ö., Gürlek, R. - Automatic Interpretable Retail forecasting with promotional scenarios, pp. 1389–1406.
• Huber, J., Stuckenschmidt, H. - Daily retail demand forecasting using machine learning with emphasis on calendric special days, pp. 1420–1438.
• Jones, J.T., Sinclair, T.M., Stekler, H.O. - A textual analysis of Bank of England growth forecasts, pp. 1478–1487.
• Kovalchik, S. - Extension of the Elo rating system to margin of victory, pp. 1329–1341.
• Le, T.H. - Forecasting value at risk and expected shortfall with mixed data sampling, pp. 1362–1379.
• Li, Y., Bu, H., Li, J., Wu, J. - The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning, pp. 1541–1562.
• Muniain, P., Ziel, F. - Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices, pp. 1193–1210.
• Peña, D. - Agustín Maravall: An interview with the International Journal of Forecasting, pp. 1241–1251.
• Rambaccussing, D., Kwiatkowski, A. - Forecasting with news sentiment: Evidence with UK newspapers, pp. 1501–1516.
• Strohsal, T., Wolf, E. - Data revisions to German national accounts: Are initial releases good nowcasts? International Journal of Forecasting 36, 1252–1259.
• Taleb, N.N. - On the statistical differences between binary forecasts and real-world payoffs, pp. 1228–1240.
• Taylor, J.W. - A strategic predictive distribution for tests of probabilistic calibration, pp. 1380–1388.
• Zhang, B., Chan, J.C.C., Cross, J.L. - Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts, pp. 1318–1328.
• Zhang, Y., Ma, F., Liao, Y. - Forecasting global equity market volatilities, pp. 1454–1475.

(résumés du n°4/2020)

Volume 36, n° 3, July-September 2020

With Special Section: Forecasting with Big Data in Real Time
Guest Editors: Claudio Antonini and George Monokroussos

• Amendola A., Braione M., Candila V., Storti G. - A Model Confidence Set approach to the combination of multivariate volatility forecasts, pp. 873‑891.
• Antonini, C., & Monokroussos, G. - Editorial : Forecasting with Big Data in Real Time, pp. 1114‑1115.
• Asai M., Gupta R., McAleer M. - Forecasting volatility and co-volatility of crude oil and gold futures : Effects of leverage, jumps, spillovers, and geopolitical risks, pp. 933‑948.
• Auerbach J., Wan P. - Forecasting the urban skyline with extreme value theory, pp. 814‑828.
• Buansing T. S. T., Golan A., Ullah A. - An information-theoretic approach for forecasting interval-valued SP500 daily returns, pp. 800‑813.
• Carstensen K., Heinrich M., Reif M., Wolters M. H. - Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model : An application to the German business cycle, pp. 829‑850.
• Cerina, R., & Duch, R. - Measuring public opinion via digital footprints, pp. 987‑1002.
• Chernis T., Cheung C., Velasco G. - A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth, pp. 851‑872.
• Cross, J. L., Hou, C., & Poon, A. - Macroeconomic forecasting with large Bayesian VARs : Global-local priors and the illusion of sparsity, pp. 899‑915.
• de Carvalho M., Martos G. - Brexit : Tracking and disentangling the sentiment towards leaving the EU, pp. 1128‑1137.
• Demetrescu M., Golosnoy V., Titova A. - Bias corrections for exponentially transformed forecasts : Are they worth the effort?, pp. 761‑780.
• Dendramis Y., Tzavalis E., Varthalitis P., Athanasiou E. - Predicting default risk under asymmetric binary link functions, pp. 1039‑1056.
• Gianfreda A., Ravazzolo F., Rossini L. - Comparing the forecasting performances of linear models for electricity prices with high RES penetration, pp. 974‑986.
• Giovannelli A., Pericoli F. M. - Are GDP forecasts optimal? Evidence on European countries, pp. 963‑973.
• Hwang R.-C., Chu C.-K., Yu K. - Predicting LGD distributions with mixed continuous and discrete ordinal outcomes, pp. 1003‑1022.
• Jennings W., Lewis-Beck M., Wlezien C. - Election forecasting : Too far out?, pp. 949‑962.
• Jobst R., Kellner R., Rösch D. - Bayesian loss given default estimation for European sovereign bonds, pp. 1073‑1091.
• Lazar E., Xue X. - Forecasting risk measures using intraday data in a generalized autoregressive score framework, pp. 1057‑1072.
• Lima L. R., Meng F., Godeiro L. - Quantile forecasting with mixed-frequency data, pp. 1149‑1162.
• Luo J., Chen L. - Realized volatility forecast with the Bayesian random compressed multivariate HAR model, pp. 781‑799.
• Ma, S., & Fildes, R. - Forecasting third-party mobile payments with implications for customer flow prediction, pp. 739‑760.
• Monokroussos G., Zhao Y. - Nowcasting in real time using popularity priors, pp. 1173‑1180.
• Nadeau R., Lewis-Beck M. S. - Election forecasts : Cracking the Danish case, pp. 892‑898.
• Niesert R. F., Oorschot J. A., Veldhuisen C. P., Brons K., Lange R.-J. - Can Google search data help predict macroeconomic series?, pp. 1163‑1172.
• Petropoulos A., Siakoulis V., Stavroulakis E., Vlachogiannakis N. E. - Predicting bank insolvencies using machine learning techniques, pp. 1092‑1113.
• Rice G., Wirjanto T., Zhao Y. - Forecasting value at risk with intra-day return curves, pp. 1023‑1038.
• Safikhani A., Kamga C., Mudigonda S., Faghih S. S., Moghimi B. - Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models, pp. 1138‑1148.
• Salinas D., Flunkert V., Gasthaus J., Januschowski T. - DeepAR : Probabilistic forecasting with autoregressive recurrent networks, pp. 1181‑1191.
• Vaughan G. - Efficient big data model selection with applications to fraud detection, pp. 1116‑1127.
• Wheatcroft E. - A profitable model for predicting the over/under market in football, pp. 916‑932.

Volume 36, n° 2, April–June 2020 

• Alonzo B., Tankov P., Drobinski P., Plougonven R. - Probabilistic wind forecasting up to three months ahead using ensemble predictions for geopotential height, pp. 515 530.
• Aparicio D., Bertolotto M. I. - Forecasting inflation with online prices, pp. 232 247.
• Audrino F., Sigrist F., Ballinari D. - The impact of sentiment and attention measures on stock market volatility, pp. 334 357.
• Berry L. R., Helman P., West M. - Probabilistic forecasting of heterogeneous consumer transaction–sales time series, pp. 552 569.
• Chou R. Y., Yen T.-J., Yen Y.-M. - Macroeconomic forecasting using approximate factor models with outliers, pp. 267 291.
• Deschamps B., Ioannidis C., Ka K. - High-frequency credit spread information and macroeconomic forecast revision, pp. 358 372.
• Diks C., Fang H. - Comparing density forecasts in a risk management context, pp. 531 551.
• Fronzetti Colladon A. - Forecasting election results by studying brand importance in online news, pp. 414 427.
• Gerlach R., Wang C. - Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures, pp. 489 506.
• Glas A. - Five dimensions of the uncertainty–disagreement linkage, pp. 607 627. 
  
• González Ordiano J. Á., Gröll L., Mikut R., Hagenmeyer V. - Probabilistic energy forecasting using the nearest neighbors quantile filter and quantile regression, pp. 310 323.
• Horváth L., Liu Z., Rice G.,Wang S. - A functional time series analysis of forward curves derived from commodity futures, pp. 646 665.
• Huang Y.-F., Startz R. - Improved recession dating using stock market volatility, pp. 507 514.
• Zajita M., Kajita S. - Crime prediction by data-driven Green’s function method, pp. 480 488.
• Kaposty F., Kriebel J., Löderbusch M. - Predicting loss given default in leasing: A closer look at models and variable selection, pp. 248 266.
• Karamaziotis P. I., Raptis A., Nikolopoulos K., Litsiou K., Assimakopoulos V. - An empirical investigation of water consumption forecasting methods, pp. 588 606.
• Lauderdale B. E., Bailey D., Blumenau J., Rivers D. - Model-based pre-election polling for national and sub-national outcomes in the US and UK, pp. 399 413.
• Li X., Zakamulin V. - The term structure of volatility predictability, pp. 723 737.
• Lyócsa Š., Todorova N. - Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?, pp. 628 645.
• Maheu J. M., Song Y., Yang Q. - Oil price shocks and economic growth: The volatility link, pp. 570 587.
• Marcjasz, G., Uniejewski, B., & Weron, R. - Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?, pp. 466 479.
• Moreno-Carbonell S., Sánchez-Úbeda E. F., Muñoz A. - Rethinking weather station selection for electric load forecasting using genetic algorithms, pp. 695 712.
• Paroissien, E. - Forecasting bulk prices of Bordeaux wines using leading indicators, pp. 292 309.
• Ramírez-Hassan A., Montoya-Blandón S. - Forecasting from others’ experience: Bayesian estimation of the generalized Bass model, pp. 442 465.
• Sobhani M., Hong T., Martin C. - Temperature anomaly detection for electric load forecasting, pp. 324 333.
• Tallman E. W., Zaman S. - Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy, pp. 373 398.
• Taylor, J. W. - Forecast combinations for value at risk and expected shortfall, pp. 428 441.
• Wang L., Ma F., Liu J., Yang L. - Forecasting stock price volatility: New evidence from the GARCH-MIDAS model, pp. 684 694.
• Wang Y., Liu L., Wu C. - Forecasting commodity prices out-of-sample: Can technical indicators help?, pp. 666 683.
• Wunderlich F., Memmert D. - Are betting returns a useful measure of accuracy in (sports) forecasting?, pp. 713 722.

Volume 36, n° 1, January-March 2020

Special Issue: M4 Competition

• Agathangelou P., Trihinas D., Katakis I. - Correlation analysis of forecasting methods: The case of the M4 competition, pp. 212–216.
• Atiya A.F. - Why does forecast combination work so well, pp. 197–200.
• Barker J. - Machine learning in M4: What makes a good unstructured model, pp. 150–155.
• Bontempi G. - Comments on M4 competition, pp. 201–202.
• Darin S.G., Stellwagen E. - Forecasting the M4 competition weekly data: Forecast Pro’s winning approach, pp. 135–141.
• Doornik J.A., Castle J.L., Hendry D.F. - Card forecasts for M4, pp. 129–134.
• Fildes R. - Learning from forecasting competitions, pp. 186–188.
• Fiorucci J.A., Louzada F. - GROEC: Combination method via Generalized Rolling Origin Evaluation, pp. 105–109.
• Fry C., Brundage M. - The M4 forecasting competition – A practitioner’s view, pp. 156–160.
• Gilliland M. - The value added by machine learning approaches in forecasting, pp. 161–166.
• Goodwin P. - Performance measurement in the M4 Competition: Possible future research, pp. 189–190.
• Grushka-Cockayne Y., Jose V.R.R. - Combining prediction intervals in the M4 competition, pp. 178–185.
• Hong T. - Forecasting with high frequency data: M4 competition and beyond, pp. 191–194.
• Hyndman R.J. - A brief history of forecasting competitions, pp. 7–14.
• Ingel A., Shahroudi N., Kängsepp M., Tättar A., Komisarenko V., Kull M. - Correlated daily time series and forecasting in the M4 competition, pp. 121–128.
• Jaganathan S., Prakash P.K.S. - A combination-based forecasting method for the M4-competition, pp. 98–104.
• Januschowski T., Gasthaus J., Wang Y., Salinas D., Flunkert V., Bohlke-Schneider M., Callot L. - Criteria for classifying forecasting methods, pp. 167–177.
• Kolassa S. - Why the “best” point forecast depends on the error or accuracy measure, pp. 208–211.
• Lichtendahl K.C., Winkler R.L. - Why do some combinations perform better than others, pp. 142–149.
• Makridakis S., Hyndman R.J., Petropoulos F. - Forecasting in social settings: The state of the art, pp. 15–28.
• Makridakis S., Petropoulos F. - The M4 competition: Conclusions, pp. 224–227.
• Makridakis S., Spiliotis E., Assimakopoulos V. - Predicting/hypothesizing the findings of the M4 Competition, pp. 29–36.
• Makridakis S., Spiliotis E., Assimakopoulos, V. - Responses to discussions and commentaries, pp. 217–223.
• Makridakis S., Spiliotis E., Assimakopoulos V. - The M4 Competition: 100,000 time series and 61 forecasting methods, pp. 54–74.
• Montero-Manso P., Athanasopoulos G., Hyndman R.J., Talagala T.S. - FFORMA: Feature-based forecast model averaging, pp. 86–92.
• Nikolopoulos K., Thomakos D.D., Katsagounos I., Alghassab W. - On the M4.0 forecasting competition: Can you tell a 4.0 earthquake from a 3.0, pp. 203–205.
• Önkal, D. - M4 competition: What’s next, pp. 206–207.
• Ord K. - Data adjustments, overfitting and representativeness, pp. 195–196.
• Pawlikowski M., Chorowska A. - Weighted ensemble of statistical models, pp. 93–97.
• Petropoulos F., Makridakis S. - The M4 competition: Bigger. Stronger. Better, pp. 3–6.
• Petropoulos F., Svetunkov I. - A simple combination of univariate models, pp. 110–115.
• Shaub D. - Fast and accurate yearly time series forecasting with forecast combinations, pp. 116–120.
• Smyl S. - A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting, pp. 75–85.
• Spiliotis E., Kouloumos A., Assimakopoulos V., Makridakis S. - Are forecasting competitions data representative of the reality, pp. 37–53.
• Taleb N.N. - Foreword to the M4 Competition, pp. 1–2.

(résumés du n° 1/2020)