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Elsevier

International Journal of Forecasting

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2013, Volume 29, Issue 1

• 1-12 A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting
  by Mestekemper, Thomas & Kauermann, Göran & Smith, Michael S.
• 13-27 Comparing forecast accuracy: A Monte Carlo investigation
  by Busetti, Fabio & Marcucci, Juri
• 28-42 Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction
  by Fuertes, Ana-Maria & Olmo, Jose
• 43-59 Hierarchical shrinkage priors for dynamic regressions with many predictors
  by Korobilis, Dimitris
• 60-68 Forecasting contemporaneous aggregates with stochastic aggregation weights
  by Brüggemann, Ralf & Lütkepohl, Helmut
• 69-79 Depression and forecast accuracy: Evidence from the 2010 FIFA World Cup
  by Jain, Kriti & Bearden, J. Neil & Filipowicz, Allan
• 80-87 Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?
  by Franses, Philip Hans & Legerstee, Rianne
• 88-99 Does the Box–Cox transformation help in forecasting macroeconomic time series?
  by Proietti, Tommaso & Lütkepohl, Helmut
• 100-107 Estimation and prediction in the random effects model with AR(p) remainder disturbances
  by Baltagi, Badi H. & Liu, Long
• 108-121 Combining expert forecasts: Can anything beat the simple average?
  by Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan
• 122-130 Forecasting exact scores in National Football League games
  by Baker, Rose D. & McHale, Ian G.
• 131-141 Does the euro area forward rate provide accurate forecasts of the short rate?
  by Galvao, Ana Beatriz & Costa, Sonia
• 142-154 Quantifying survey expectations: What’s wrong with the probability approach?
  by Breitung, Jörg & Schmeling, Maik
• 155-174 Can securities analysts forecast intangible firms’ earnings?
  by Higgins, Huong
• 175-190 Evaluating probability forecasts for GDP declines using alternative methodologies
  by Lahiri, Kajal & Wang, J. George
• 191-201 Space–time autoregressive models and forecasting national, regional and state crime rates
  by Shoesmith, Gary L.
• 202-219 On downside risk predictability through liquidity and trading activity: A dynamic quantile approach
  by Rubia, Antonio & Sanchis-Marco, Lidia

2012, Volume 28, Issue 4

• 769-776 Forecasting Spanish elections
  by Magalhães, Pedro C. & Aguiar-Conraria, Luís & Lewis-Beck, Michael S.
• 777-788 Election forecasting under opaque conditions: A model for Francophone Belgium, 1981–2010
  by Dassonneville, Ruth & Hooghe, Marc
• 789-796 Forecasting Norwegian elections: Out of work and out of office
  by Arnesen, Sveinung
• 797-803 Japanese election forecasting: Classic tests of a hard case
  by Lewis-Beck, Michael S. & Tien, Charles
• 804-812 Forecasting Brazilian presidential elections: Solving the N problem
  by Turgeon, Mathieu & Rennó, Lucio
• 813-821 Forecasting Turkish local elections
  by Toros, Emre
• 822-829 Election forecasting in Lithuania: The case of municipal elections
  by Jastramskis, Mažvydas
• 830-841 Forecast errors and inventory performance under forecast information sharing
  by Ali, Mohammad M. & Boylan, John E. & Syntetos, Aris A.
• 842-848 Forecasting method selection in a global supply chain
  by Acar, Yavuz & Gardner, Everette S.
• 849-873 Ranking the predictive performances of value-at-risk estimation methods
  by Şener, Emrah & Baronyan, Sayad & Ali Mengütürk, Levent
• 874-890 Security analysts, cash flow forecasts, and turnover
  by Pandit, Shailendra & Willis, Richard H. & Zhou, Ling

2012, Volume 28, Issue 3

• 557-574 Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range
  by Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael
• 575-586 Bias correction and out-of-sample forecast accuracy
  by Kim, Hyeongwoo & Durmaz, Nazif
• 587-606 A population dependent diffusion model with a stochastic extension
  by Michalakelis, C. & Sphicopoulos, T.
• 607-622 Using a nested logit model to forecast television ratings
  by Danaher, Peter & Dagger, Tracey
• 623-631 Optimal forecasting of noncausal autoregressive time series
  by Lanne, Markku & Luoto, Jani & Saikkonen, Pentti
• 632-643 Forecasting test cricket match outcomes in play
  by Akhtar, Sohail & Scarf, Philip
• 644-659 Are freight futures markets efficient? Evidence from IMAREX
  by Goulas, Lambros & Skiadopoulos, George
• 660-674 Modeling patronage shift to a new entrant for predicting disproportionate losses for incumbent outlets
  by Jun, Duk Bin & Kim, Jungki & Park, Myoung Hwan & Cha, Kyoung Cheon
• 675-688 A varying-coefficient default model
  by Hwang, Ruey-Ching
• 695-711 The illusion of predictability: How regression statistics mislead experts
  by Soyer, Emre & Hogarth, Robin M.
• 722-738 Fast sparse regression and classification
  by Friedman, Jerome H.

2012, Volume 28, Issue 2

• 297-308 Do professional forecasters pay attention to data releases?
  by Clements, Michael P.
• 309-314 Jointly evaluating the Federal Reserve’s forecasts of GDP growth and inflation
  by Sinclair, Tara M. & Gamber, Edward N. & Stekler, Herman & Reid, Elizabeth
• 315-327 Forecasting US state-level employment growth: An amalgamation approach
  by Rapach, David E. & Strauss, Jack K.
• 328-342 Autocontour-based evaluation of multivariate predictive densities
  by González-Rivera, Gloria & Yoldas, Emre
• 343-352 Efficient evaluation of multidimensional time-varying density forecasts, with applications to risk management
  by Polanski, Arnold & Stoja, Evarist
• 353-365 Markov switching and exchange rate predictability
  by Nikolsko-Rzhevskyy, Alex & Prodan, Ruxandra
• 366-383 Predicting stock volatility using after-hours information: Evidence from the NASDAQ actively traded stocks
  by Chen, Chun-Hung & Yu, Wei-Choun & Zivot, Eric
• 384-399 Forecasting volatility with asymmetric smooth transition dynamic range models
  by Lin, Edward M.H. & Chen, Cathy W.S. & Gerlach, Richard
• 400-411 Forecasting spikes in electricity prices
  by Christensen, T.M. & Hurn, A.S. & Lindsay, K.A.
• 412-427 Forecasting a monetary aggregate under instability: Argentina after 2001
  by Ahumada, Hildegart A. & Garegnani, Maria Lorena
• 428-445 The performance of short-term forecasts of the German economy before and during the 2008/2009 recession
  by Drechsel, Katja & Scheufele, Rolf
• 446-455 Forecasting monetary policy rules in South Africa
  by Naraidoo, Ruthira & Paya, Ivan
• 456-476 Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation
  by Aron, Janine & Muellbauer, John
• 477-484 A study of outliers in the exponential smoothing approach to forecasting
  by Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian
• 485-496 Forecasting the intermittent demand for slow-moving inventories: A modelling approach
  by Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian
• 497-506 To model, or not to model: Forecasting for customer prioritization
  by Huang, Chun-Yao
• 507-518 Forecasting customer behaviour in a multi-service financial organisation: A profitability perspective
  by Audzeyeva, Alena & Summers, Barbara & Schenk-Hoppé, Klaus Reiner
• 519-531 Forecasting life expectancy in an international context
  by Torri, Tiziana & Vaupel, James W.
• 532-542 Simulating a basketball match with a homogeneous Markov model and forecasting the outcome
  by Štrumbelj, Erik & Vračar, Petar
• 543-552 A comparative analysis of data mining methods in predicting NCAA bowl outcomes
  by Delen, Dursun & Cogdell, Douglas & Kasap, Nihat

2011, Volume 27, Issue 4

• 961-967 In memory of Arnold Zellner, a great scientist and person
  by García-Ferrer, Antonio
• 968-995 Validation and forecasting accuracy in models of climate change
  by Fildes, Robert & Kourentzes, Nikolaos
• 996-999 Validation and forecasting accuracy in models of climate change: Comments
  by McSharry, Patrick E.
• 1000-1003 Commentary on "Validation and forecasting accuracy in models of climate change"
  by Keenlyside, Noel S.
• 1004-1005 Validation and forecasting accuracy in models of climate change: Postscript
  by Fildes, Robert & Kourentzes, Nikolaos
• 1006-1026 Calling recessions in real time
  by Hamilton, James D.
• 1027-1031 Comments on "Calling recessions in real time"
  by Trimbur, Thomas M.
• 1032-1038 Discussion: Calling recessions in real time
  by Wildi, Marc
• 1039-1040 Response to comments
  by Hamilton, James D.
• 1041-1057 Kernel-based calibration diagnostics for recession and inflation probability forecasts
  by Galbraith, John W. & van Norden, Simon
• 1058-1065 On economic evaluation of directional forecasts
  by Blaskowitz, Oliver & Herwartz, Helmut
• 1066-1075 How accurate are government forecasts of economic fundamentals? The case of Taiwan
  by Chang, Chia-Lin & Franses, Philip Hans & McAleer, Michael
• 1076-1088 A large factor model for forecasting macroeconomic variables in South Africa
  by Gupta, Rangan & Kabundi, Alain
• 1089-1107 Forecasting exchange rate volatility using high-frequency data: Is the euro different?
  by Chortareas, Georgios & Jiang, Ying & Nankervis, John. C.
• 1108-1115 Forecasting levels of log variables in vector autoregressions
  by Bårdsen, Gunnar & Lütkepohl, Helmut
• 1116-1127 Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search
  by Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin
• 1128-1146 The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys
  by Lui, Silvia & Mitchell, James & Weale, Martin
• 1147-1159 Decay factor optimisation in time weighted simulation -- Evaluating VaR performance
  by Zikovic, Sasa & Aktan, Bora
• 1160-1177 Modeling the demand and supply in a new B2B-upstream market using a knowledge updating process
  by Krishnan, Trichy V. & Feng, Shanfei & Beebe, Tony
• 1178-1195 Forecasting monthly and quarterly time series using STL decomposition
  by Theodosiou, Marina
• 1196-1214 Sensitivity to autocorrelation in judgmental time series forecasting
  by Reimers, Stian & Harvey, Nigel
• 1215-1240 Forecasting television ratings
  by Danaher, Peter J. & Dagger, Tracey S. & Smith, Michael S.
• 1241-1247 Robust backward population projections made possible
  by Ediev, Dalkhat M.
• 1248-1258 Forecasting elections in Turkey
  by Toros, Emre
• 1259-1270 Maximizing bidder surplus in simultaneous online art auctions via dynamic forecasting
  by Dass, Mayukh & Jank, Wolfgang & Shmueli, Galit
• 1271-1273 Book review
  by Allen, P. Geoffrey

2011, Volume 27, Issue 3

• 942-960 Booking horizon forecasting with dynamic updating: A case study of hotel reservation data
  by Haensel, Alwin & Koole, Ger
• 635-660 Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction
  by Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos
• 661-671 MLP ensembles improve long term prediction accuracy over single networks
  by Adeodato, Paulo J.L. & Arnaud, Adrian L. & Vasconcelos, Germano C. & Cunha, Rodrigo C.L.V. & Monteiro, Domingos S.M.P.
• 672-688 Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition
  by Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham
• 689-699 Conditionally dependent strategies for multiple-step-ahead prediction in local learning
  by Bontempi, Gianluca & Ben Taieb, Souhaib
• 700-707 Forecasting the NN5 time series with hybrid models
  by Wichard, Jörg D.
• 708-724 Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting
  by Luna, Ivette & Ballini, Rosangela
• 725-739 A heuristic method for parameter selection in LS-SVM: Application to time series prediction
  by Rubio, Ginés & Pomares, Héctor & Rojas, Ignacio & Herrera, Luis Javier
• 740-759 Holt's exponential smoothing and neural network models for forecasting interval-valued time series
  by Maia, André Luis Santiago & de Carvalho, Francisco de A.T.
• 760-776 Forecasting ATM cash demands using a local learning model of cerebellar associative memory network
  by Teddy, S.D. & Ng, S.K.
• 923-941 Forecasting (aggregate) demand for US commercial air travel
  by Carson, Richard T. & Cenesizoglu, Tolga & Parker, Roger
• 777-803 An empirical analysis of neural network memory structures for basin water quality forecasting
  by West, David & Dellana, Scott
• 804-816 Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange
  by Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad
• 817-821 Tourism forecasting: An introduction
  by Song, Haiyan & Hyndman, Rob J.
• 822-844 The tourism forecasting competition
  by Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C.
• 845-849 The value of feedback in forecasting competitions
  by Athanasopoulos, George & Hyndman, Rob J.
• 850-852 Winning methods for forecasting tourism time series
  by Baker, Lee C. & Howard, Jeremy
• 853-854 Winning methods for forecasting seasonal tourism time series
  by Brierley, Phil
• 855-869 Forecasting tourist arrivals using time-varying parameter structural time series models
  by Song, Haiyan & Li, Gang & Witt, Stephen F. & Athanasopoulos, George
• 870-886 Combination of long term and short term forecasts, with application to tourism demand forecasting
  by Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham
• 887-901 Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals
  by Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen
• 902-922 Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures
  by Fildes, Robert & Wei, Yingqi & Ismail, Suzilah
• 923-941 Forecasting (aggregate) demand for US commercial air travel
  by Carson, Richard T. & Cenesizoglu, Tolga & Parker, Roger
• 942-960 Booking horizon forecasting with dynamic updating: A case study of hotel reservation data
  by Haensel, Alwin & Koole, Ger
• 902-922 Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures
  by Fildes, Robert & Wei, Yingqi & Ismail, Suzilah
• 887-901 Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals
  by Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen
• 870-886 Combination of long term and short term forecasts, with application to tourism demand forecasting
  by Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham
• 855-869 Forecasting tourist arrivals using time-varying parameter structural time series models
  by Song, Haiyan & Li, Gang & Witt, Stephen F. & Athanasopoulos, George
• 845-849 The value of feedback in forecasting competitions
  by Athanasopoulos, George & Hyndman, Rob J.
• 822-844 The tourism forecasting competition
  by Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C.
• 804-816 Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange
  by Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad
• 777-803 An empirical analysis of neural network memory structures for basin water quality forecasting
  by West, David & Dellana, Scott
• 760-776 Forecasting ATM cash demands using a local learning model of cerebellar associative memory network
  by Teddy, S.D. & Ng, S.K.
• 740-759 Holt’s exponential smoothing and neural network models for forecasting interval-valued time series
  by Maia, André Luis Santiago & de Carvalho, Francisco de A.T.
• 725-739 A heuristic method for parameter selection in LS-SVM: Application to time series prediction
  by Rubio, Ginés & Pomares, Héctor & Rojas, Ignacio & Herrera, Luis Javier
• 708-724 Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting
  by Luna, Ivette & Ballini, Rosangela
• 700-707 Forecasting the NN5 time series with hybrid models
  by Wichard, Jörg D.
• 689-699 Conditionally dependent strategies for multiple-step-ahead prediction in local learning
  by Bontempi, Gianluca & Ben Taieb, Souhaib
• 672-688 Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition
  by Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham
• 661-671 MLP ensembles improve long term prediction accuracy over single networks
  by Adeodato, Paulo J.L. & Arnaud, Adrian L. & Vasconcelos, Germano C. & Cunha, Rodrigo C.L.V. & Monteiro, Domingos S.M.P.
• 635-660 Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction
  by Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos

2011, Volume 27, Issue 2

• 197-207 Quantiles as optimal point forecasts
  by Gneiting, Tilmann
• 208-223 Combining probability forecasts
  by Clements, Michael P. & Harvey, David I.
• 224-237 Forecast combination through dimension reduction techniques
  by Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva
• 238-251 Combining exponential smoothing forecasts using Akaike weights
  by Kolassa, Stephan
• 252-265 Forecasting correlated time series with exponential smoothing models
  by Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta
• 266-280 Direct and iterated multistep AR methods for difference stationary processes
  by Proietti, Tommaso
• 281-307 Incorporating vintage differences and forecasts into Markov switching models
  by Nalewaik, Jeremy J.
• 308-319 Prediction intervals in conditionally heteroscedastic time series with stochastic components
  by Pellegrini, Santiago & Ruiz, Esther & Espasa, Antoni
• 320-332 Bootstrap prediction intervals for SETAR models
  by Li, Jing
• 333-346 A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP
  by Banbura, Marta & Rünstler, Gerhard
• 347-364 Multivariate semi-nonparametric distributions with dynamic conditional correlations
  by Del Brio, Esther B. & Ñíguez, Trino-Manuel & Perote, Javier
• 365-378 Shrinkage estimation of semiparametric multiplicative error models
  by Brownlees, Christian T. & Gallo, Giampiero M.
• 379-393 Scoring rules and survey density forecasts
  by Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F.
• 394-412 Density forecasting through disaggregation
  by Kim, Kun Ho
• 413-437 Asymmetric loss functions and the rationality of expected stock returns
  by Aretz, Kevin & Bartram, Söhnke M. & Pope, Peter F.
• 438-451 Predicting economic contractions and expansions with the aid of professional forecasts
  by Chua, Chew Lian & Tsiaplias, Sarantis
• 452-465 Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7
  by Dovern, Jonas & Weisser, Johannes
• 466-481 Real-time macroeconomic forecasting with leading indicators: An empirical comparison
  by Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F.
• 482-495 One model and various experts: Evaluating Dutch macroeconomic forecasts
  by Franses, Philip Hans & Kranendonk, Henk C. & Lanser, Debby
• 496-511 Forecasting national activity using lots of international predictors: An application to New Zealand
  by Eickmeier, Sandra & Ng, Tim
• 512-528 Open economy forecasting with a DSGE-VAR: Head to head with the RBNZ published forecasts
  by Lees, Kirdan & Matheson, Troy & Smith, Christie
• 529-542 MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area
  by Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian
• 543-560 Are VIX futures prices predictable? An empirical investigation
  by Konstantinidi, Eirini & Skiadopoulos, George
• 561-578 Forecasting the direction of the US stock market with dynamic binary probit models
  by Nyberg, Henri
• 579-591 Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models
  by Yu, Wei-Choun & Zivot, Eric
• 592-601 Forecasting accuracy of wind power technology diffusion models across countries
  by Dalla Valle, Alessandra & Furlan, Claudia
• 602-618 Forecasting temperature to price CME temperature derivatives
  by Dupuis, Debbie J.
• 619-630 A Bradley-Terry type model for forecasting tennis match results
  by McHale, Ian & Morton, Alex
• 631-633 The Business Forecasting Deal
  by Kolassa, Stephan
• 619-630 A Bradley-Terry type model for forecasting tennis match results
  by McHale, Ian & Morton, Alex
• 602-618 Forecasting temperature to price CME temperature derivatives
  by Dupuis, Debbie J.
• 592-601 Forecasting accuracy of wind power technology diffusion models across countries
  by Dalla Valle, Alessandra & Furlan, Claudia
• 579-591 Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models
  by Yu, Wei-Choun & Zivot, Eric
• 561-578 Forecasting the direction of the US stock market with dynamic binary probit models
  by Nyberg, Henri
• 543-560 Are VIX futures prices predictable? An empirical investigation
  by Konstantinidi, Eirini & Skiadopoulos, George
• 529-542 MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area
  by Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian
• 512-528 Open economy forecasting with a DSGE-VAR: Head to head with the RBNZ published forecasts
  by Lees, Kirdan & Matheson, Troy & Smith, Christie
• 496-511 Forecasting national activity using lots of international predictors: An application to New Zealand
  by Eickmeier, Sandra & Ng, Tim
• 482-495 One model and various experts: Evaluating Dutch macroeconomic forecasts
  by Franses, Philip Hans & Kranendonk, Henk C. & Lanser, Debby
• 466-481 Real-time macroeconomic forecasting with leading indicators: An empirical comparison
  by Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F.
• 452-465 Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7
  by Dovern, Jonas & Weisser, Johannes
• 438-451 Predicting economic contractions and expansions with the aid of professional forecasts
  by Chua, Chew Lian & Tsiaplias, Sarantis
• 413-437 Asymmetric loss functions and the rationality of expected stock returns
  by Aretz, Kevin & Bartram, Söhnke M. & Pope, Peter F.
• 394-412 Density forecasting through disaggregation
  by Kim, Kun Ho
• 379-393 Scoring rules and survey density forecasts
  by Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F.
• 365-378 Shrinkage estimation of semiparametric multiplicative error models
  by Brownlees, Christian T. & Gallo, Giampiero M.
• 347-364 Multivariate semi-nonparametric distributions with dynamic conditional correlations
  by Del Brio, Esther B. & Ñíguez, Trino-Manuel & Perote, Javier
• 333-346 A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP
  by Bańbura, Marta & Rünstler, Gerhard
• 320-332 Bootstrap prediction intervals for SETAR models
  by Li, Jing
• 308-319 Prediction intervals in conditionally heteroscedastic time series with stochastic components
  by Pellegrini, Santiago & Ruiz, Esther & Espasa, Antoni
• 281-307 Incorporating vintage differences and forecasts into Markov switching models
  by Nalewaik, Jeremy J.
• 266-280 Direct and iterated multistep AR methods for difference stationary processes
  by Proietti, Tommaso
• 252-265 Forecasting correlated time series with exponential smoothing models
  by Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta
• 238-251 Combining exponential smoothing forecasts using Akaike weights
  by Kolassa, Stephan
• 224-237 Forecast combination through dimension reduction techniques
  by Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva
• 208-223 Combining probability forecasts
  by Clements, Michael P. & Harvey, David I.
• 197-207 Quantiles as optimal point forecasts
  by Gneiting, Tilmann

2011, Volume 27, Issue 1

• 1-13 Group-based judgmental forecasting: An integration of extant knowledge and the development of priorities for a new research agenda
  by Wright, George & Rowe, Gene
• 14-40 Group-based forecasting?: A social psychological analysis
  by Kerr, Norbert L. & Tindale, R. Scott
• 41-49 Group diversity and decision quality: Amplification and attenuation of the framing effect
  by Yaniv, Ilan
• 50-68 Influence of differentiated roles on group forecasting accuracy
  by Önkal, Dilek & Lawrence, Michael & Zeynep SayIm, K.
• 69-80 Role thinking: Standing in other people's shoes to forecast decisions in conflicts
  by Green, Kesten C. & Armstrong, J. Scott
• 81-102 Judgmental aggregation strategies depend on whether the self is involved
  by Soll, Jack B. & Mannes, Albert E.
• 103-120 Forecasting another's enjoyment versus giving the right answer: Trust, shared values, task effects, and confidence in improving the acceptance of advice
  by Van Swol, Lyn M.
• 121-133 Forecasting clients' reactions: How does the perception of strategic behavior influence the acceptance of advice?
  by Jodlbauer, Barbara & Jonas, Eva
• 134-151 People consultation to construct the future: A Delphi application
  by Landeta, Jon & Barrutia, Jon
• 152-165 A virtual and anonymous, deliberative and analytic participation process for planning and evaluation: The Concept Mapping Policy Delphi
  by Klenk, Nicole L. & Hickey, Gordon M.
• 166-182 The predictive validity of peer review: A selective review of the judgmental forecasting qualities of peers, and implications for innovation in science
  by Benda, Wim G.G. & Engels, Tim C.E.
• 183-195 Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task
  by Graefe, Andreas & Armstrong, J. Scott
• 81-102 Judgmental aggregation strategies depend on whether the self is involved
  by Soll, Jack B. & Mannes, Albert E.
• 69-80 Role thinking: Standing in other people’s shoes to forecast decisions in conflicts
  by Green, Kesten C. & Armstrong, J. Scott
• 50-68 Influence of differentiated roles on group forecasting accuracy
  by Önkal, Dilek & Lawrence, Michael & Zeynep Sayım, K.
• 41-49 Group diversity and decision quality: Amplification and attenuation of the framing effect
  by Yaniv, Ilan
• 183-195 Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task
  by Graefe, Andreas & Armstrong, J. Scott

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