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Author Index Volume 21 (2005)
International Journal of Forecasting 21 (2005) 805 – 807 www.elsevier.com/locate/ijforecast
Author Index Volume 21 (2005) (The issue number is given in parentheses)
Alsem, K.J., see Paap, R. Andersson, P., J. Edman and M. Ekman, Predicting the World Cup 2002 in soccer: Performance and confidence of experts and non-experts Armstrong, J.S., F. Collopy and J.T. Yokum, Decomposition by causal forces: a procedure for forecasting complex time series Awartani, B.M.A. and V. Corradi, Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries Bos, C.S. and A. Justel, On model selection criteria as a starting point for sequential detection of non-linearity Boylan, J.E., see Syntetos, A.A. Bunn, D.W., see Hippert, H.S. Capon, N., see Jaffry, S. Carvalho, V.M. and A.C. Harvey, Growth, cycles and convergence in US regional time series Casals, J., see Jerez, M. Chevillon, G. and D.F. Hendry, Non-parametric direct multi-step estimation for forecasting economic processes Choi, H.-s. and N.M. Kiefer, Software evaluation: EasyReg International Collopy, F., see Armstrong, J.S. Conejo, A.J., J. Contreras, R. Espı´nola and M.A. Plazas, Forecasting electricity prices for a day-ahead pool-based electric energy market Contreras, J., see Conejo, A.J. Corradi, V., see Awartani, B.M.A. Crato, N., A mild skepticism on nonlinear forecasting: Some comments on the paper by Harvill and Ray Croux, C., see Lemmens, A.
doi:10.1016/S0169-2070(05)00114-7
(1)
53–71
(3) 565–576
(1)
25–36
(1) 167–183
(4) 749–754 (2) 303–314 (3) 425–434 (1)
73–85
(4) 667–686 (4) 687–689
(2) 201–218 (3) 609–616 (1) 25–36
(3) 435–462 (3) 435–462 (1) 167–183
(4) 729–730 (2) 363–375
De Gooijer, J.G., see Garcı´a-Ferrer, A. Dekimpe, M.G., see Lemmens, A. Del Hoyo, J., Comments on Fok, van Dijk and Franses’s paper: bForecasting aggregates using panels of nonlinear time seriesQ Duarte, A., I.A. Venetis and I. Paya, Predicting real growth and the probability of recession in the Euro area using the yield spread Edman, J., see Andersson, P. Edmundson, B., see Webby, R. Ekman, M., see Andersson, P. Engel, J., D. Haugh and A. Pagan, Some methods for assessing the need for non-linear models in business cycle analysis Espasa, A., Comments on bThe Marshallian macroeconomic model: A progress reportQ by Arnold Zellner and Guillermo Israilevich Espı´nola, R., see Conejo, A.J. Evgeniou, T., see Hibon, M. Falk, B. and A. Roy, Forecasting using the trend model with autoregressive errors Ferna´ndez-Macho, J., Comments on bCombining filter design with model-based filteringQ Fildes, R., The IJF, the Institute and forecasting software Fok, D., D. van Dijk and P.H. Franses, Forecasting aggregates using panels of nonlinear time series Forrest, D., J. Goddard and R. Simmons, Odds-setters as forecasters: The case of English football Franses, P.H. and D. van Dijk, The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production Franses, P.H., see Fok, D. Franses, P.H., see Koning, A.J. Franses, P.H., see Paap, R.
(4) 623–625 (2) 363–375
(4) 795–797
(2) 261–277 (3) 565–576 (3) 411–423 (3) 565–576
(4) 651–662
(4) 647–650 (3) 435–462 (1) 15–24
(2) 291–302 (4) 711–715 (2) 199–200
(4) 785–794
(3) 551–564
(1) 87–102 (4) 785–794 (3) 397–409 (1) 53–71
806
Author Index to Volume 21
Galbraith, J.W. and T. KVVsV nbay, Content horizons ˙ for conditional variance forecasts (2) 249–260 Garcı´a-Ferrer, A., J.G. De Gooijer, P. Poncela and E. Ruiz, Introduction to nonlinearities, business cycles, and forecasting (4) 623–625 Gardner Jr., E.S. and A.B. Koehler, Comments on a patented bootstrapping method for forecasting intermittent demand (4) 617–618 Ghiassi, M., H. Saidane and D.K. Zimbra, A dynamic artificial neural network model for forecasting time series events (2) 341–362 Goddard, J., see Forrest, D. (3) 551–564 Goddard, J., Regression models for forecasting goals and match results in association football (2) 331–340 Green, K.C., Game theory, simulated interaction, and unaided judgement for forecasting decisions in conflicts: Further evidence (3) 463–472 Gutierrez, M.-I., see Vuchelen, J. (1) 103–117 Hansson, J., P. Jansson and M. Lo¨f, Business survey data: Do they help in forecasting GDP growth? Harrison, R., G. Kapetanios and T. Yates, Forecasting with measurement errors in dynamic models Harvey, A.C., see Carvalho, V.M. Harvill, J.L. and B.K. Ray, A note on multi-step forecasting with functional coefficient autoregressive models Haugh, D., see Engel, J. Hendry, D.F., see Chevillon, G. Hibon, M. and T. Evgeniou, To combine or not to combine: selecting among forecasts and their combinations Hibon, M., see Koning, A.J. Hippert, H.S., D.W. Bunn and R.C. Souza, Large neural networks for electricity load forecasting: Are they overfitted? Horva´th, C., see Wieringa, J.E. Hubrich, K., Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy? Hyndam, R.J., Editorial Israilevich, G., see Zellner, A. Jaffry, S. and N. Capon, Alternative methods of forecasting risks in Naval manpower planning Jansson, P., see Hansson, J. Jerez, M., J. Casals and S. Sotoca, Growth, cycles, and convergence in US regional time series: A personal point of view Justel, A., see Bos, C.S. Kaiser, R. and A. Maravall, Combining filter design with model-based filtering (with an application to business-cycle estimation)
(2) 377–389
(3) 595–607 (4) 667–686
(4) 717–727 (4) 651–662 (2) 201–218
(1) 15–24 (3) 397–409
(3) 425–434 (2) 279–289
(1) 119–136 (1) 1–1 (4) 627–645
(1) 73–85 (2) 377–389
(4) 687–689 (4) 749–754
(4) 691–710
Kapetanios, G., see Harrison, R. Kholodilin, K.A. and V.W. Yao, Measuring and predicting turning points using a dynamic bi-factor model Kiefer, N.M., see Choi, H.-s. Koehler, A.B., see Gardner Jr., E.S. Koning, A.J., P.H. Franses, M. Hibon and H.O. Stekler, The M3 competition: Statistical tests of the results KVVsV nbay, T., see Galbraith, J.W. ˙ Lawrence, M. and M. O’Connor, Judgmental forecasting in the presence of loss functions Lemmens, A., C. Croux and M.G. Dekimpe, On the predictive content of production surveys: A pan-European study Lo¨f, M., see Hansson, J. Macaulay, A., see Pollock, A.C. Maravall, A., see Kaiser, R. Martin, G.M., see McCabe, B.P.M. McCabe, B.P.M. and G.M. Martin, Bayesian predictions of low count time series Medeiros, M.C., see Tera¨svirta, T. Medeiros, M.C., see Tera¨svirta, T. Mouchart, M. and J.V.K. Rombouts, Clustered panel data models: an efficient approach for nowcasting from poor data Nikolopoulos, K., Forcasting volatility in the Financial markets Nikolopoulos, K., Neural networks in business forecasting Nikolopoulos, K., Advances in business and management forecasting Novales, A., Comments on: bLinear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examinationQ Nunes, L.C., see Rua, A. O’Connor, M., see Lawrence, M. O’Connor, M., see Webby, R. ¨ nkal, D., see Pollock, A.C. O Ortega, J.A. and P. Poncela, Joint forecasts of Southern European fertility rates with non-stationary dynamic factor models Paap, R., E. van Nierop, H.J. van Heerde, M. Wedel, P.H. Franses and K.J. Alsem, Consideration sets, intentions and the inclusion of bdon’t knowQ in a two-stage model for voter choice Pagan, A., see Engel, J. Pascual, L., J. Romo and E. Ruiz, Bootstrap prediction intervals for power-transformed time series
(3) 595–607
(3) 525–537 (3) 609–616 (3) 617–618
(3) 397–409 (2) 249–260
(1)
3–14
(2) 363–375 (2) 377–389 (3) 473–489 (4) 691–710 (2) 315–330 (2) 315–330 (4) 755–774 (4) 781–783
(3) 577–594
(2) 394–394 (2) 394–395 (2) 395–395
(4) 775–780 (3) 503–523 (1) 3–14 (3) 411–423 (3) 473–489
(3) 539–550
(1) 53–71 (4) 651–662
(2) 219–235
Author Index to Volume 21 Paya, I., see Duarte, A. Pen˜a, D. and J. Rodriguez, Detecting nonlinearity in time series by model selection criteria Pe´rez Quiro´s, G., Comments on bSome methods for assessing the need for non-linear models in business cycle analysisQ Plazas, M.A., see Conejo, A.J. Pollock, A.C., A. Macaulay, M.E. Thomson ¨ nkal, Performance evaluation of and D. O judgemental directional exchange rate predictions Poncela, P., see Garcı´a-Ferrer, A. Poncela, P., see Ortega, J.A. Raeside, R., Market response models: econometric and time series analysis Ramnath, S., S. Rock and P. Shane, Value Line and I/B/E/S earnings forecasts Rangvid, J., see Rapach, D.E. Rapach, D.E., M.E. Wohar and J. Rangvid, Macro variables and international stock return predictability Ray, B.K., see Harvill, J.L. Reeves, J.J., Bootstrap prediction intervals for ARCH models Rock, S., see Ramnath, S. Rodriguez, J., see Pen˜a, D. Rombouts, J.V.K., see Mouchart, M. Romo, J., see Pascual, L. Ro¨sch, D., An empirical comparison of default risk forecasts from alternative credit rating philosophies Roy, A., see Falk, B. Rua, A. and L.C. Nunes, Coincident and leading indicators for the euro area: A frequency band approach Ruiz, E., see Garcı´a-Ferrer, A. Ruiz, E., see Pascual, L.
(2) 261–277
(4) 731–748
(4) 663–666 (3) 435–462
(3) 473–489 (4) 623–625 (3) 539–550
(2) 392–393 (1) 185–198 (1) 137–166
(1) 137–166 (4) 717–727 (2) (1) (4) (3) (2)
237–248 185–198 731–748 577–594 219–235
(1) 37–51 (2) 291–302
(3) 503–523 (4) 623–625 (2) 219–235
Saidane, H., see Ghiassi, M. (2) 341–362 Schwarz, H., see Willemain, T.R. (3) 619–620 Shane, P., see Ramnath, S. (1) 185–198 Shore, J., see Ye, M. (3) 491–501 Simmons, R., see Forrest, D. (3) 551–564 Sloboda, B., Forecasting, time series and regression: an applied approach (2) 391–392 Smart, C.N., see Willemain, T.R. (3) 619–620
Sotoca, S., see Jerez, M. Souza, R.C., see Hippert, H.S. Stekler, H.O., see Koning, A.J. Syntetos, A.A. and J.E. Boylan, The accuracy of intermittent demand estimates Tera¨svirta, T., D. van Dijk and M.C. Medeiros, Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination Tera¨svirta, T., Van Dijk, D. and Medeiros, M.C., Reply Thomson, M.E., see Pollock, A.C. Van Dijk, D., see Fok, D. Van Dijk, D., see Franses, P.H. Van Dijk, D., see Tera¨svirta, T. Van Dijk, D., see Tera¨svirta, T. Van Heerde, H.J., see Paap, R. Van Nierop, E., see Paap, R. Venetis, I.A., see Duarte, A. Vuchelen, J. and M.-I. Gutierrez, A direct test of the information content of the OECD growth forecasts Webby, R., M. O’Connor and B. Edmundson, Forecasting support systems for the incorporation of event information: An empirical investigation Wedel, M., see Paap, R. Wieringa, J.E. and C. Horva´th, Computing level-impulse responses of log-specified VAR systems Willemain, T.R., C.N. Smart and H. Schwarz, Author’s response to Koehler and Gardner Wohar, M.E., see Rapach, D.E. Yao, V.W., see Kholodilin, K.A. Yates, T., see Harrison, R. Ye, M., J. Zyren and J. Shore, A monthly crude oil spot price forecasting model using relative inventories Yokum, J.T., see Armstrong, J.S. Zellner, A. and G. Israilevich, The Marshallian macroeconomic model: A progress report Zimbra, D.K., see Ghiassi, M. Zyren, J., see Ye, M.
807 (4) 687–689 (3) 425–434 (3) 397–409 (2) 303–314
(4) 755–774 (4) 781–783 (3) 473–489 (4) (1) (4) (4) (1) (1) (2)
785–794 87–102 755–774 781–783 53–71 53–71 261–277
(1) 103–117
(3) 411–423 (1) 53–71
(2) 279–289 (3) 619–620 (1) 137–166 (3) 525–537 (3) 595–607
(3) 491–501 (1) 25–36
(4) 627–645 (2) 341–362 (3) 491–501
Author Index Volume 21 (2005) (The issue number is given in parentheses)
Alsem, K.J., see Paap, R. Andersson, P., J. Edman and M. Ekman, Predicting the World Cup 2002 in soccer: Performance and confidence of experts and non-experts Armstrong, J.S., F. Collopy and J.T. Yokum, Decomposition by causal forces: a procedure for forecasting complex time series Awartani, B.M.A. and V. Corradi, Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries Bos, C.S. and A. Justel, On model selection criteria as a starting point for sequential detection of non-linearity Boylan, J.E., see Syntetos, A.A. Bunn, D.W., see Hippert, H.S. Capon, N., see Jaffry, S. Carvalho, V.M. and A.C. Harvey, Growth, cycles and convergence in US regional time series Casals, J., see Jerez, M. Chevillon, G. and D.F. Hendry, Non-parametric direct multi-step estimation for forecasting economic processes Choi, H.-s. and N.M. Kiefer, Software evaluation: EasyReg International Collopy, F., see Armstrong, J.S. Conejo, A.J., J. Contreras, R. Espı´nola and M.A. Plazas, Forecasting electricity prices for a day-ahead pool-based electric energy market Contreras, J., see Conejo, A.J. Corradi, V., see Awartani, B.M.A. Crato, N., A mild skepticism on nonlinear forecasting: Some comments on the paper by Harvill and Ray Croux, C., see Lemmens, A.
doi:10.1016/S0169-2070(05)00114-7
(1)
53–71
(3) 565–576
(1)
25–36
(1) 167–183
(4) 749–754 (2) 303–314 (3) 425–434 (1)
73–85
(4) 667–686 (4) 687–689
(2) 201–218 (3) 609–616 (1) 25–36
(3) 435–462 (3) 435–462 (1) 167–183
(4) 729–730 (2) 363–375
De Gooijer, J.G., see Garcı´a-Ferrer, A. Dekimpe, M.G., see Lemmens, A. Del Hoyo, J., Comments on Fok, van Dijk and Franses’s paper: bForecasting aggregates using panels of nonlinear time seriesQ Duarte, A., I.A. Venetis and I. Paya, Predicting real growth and the probability of recession in the Euro area using the yield spread Edman, J., see Andersson, P. Edmundson, B., see Webby, R. Ekman, M., see Andersson, P. Engel, J., D. Haugh and A. Pagan, Some methods for assessing the need for non-linear models in business cycle analysis Espasa, A., Comments on bThe Marshallian macroeconomic model: A progress reportQ by Arnold Zellner and Guillermo Israilevich Espı´nola, R., see Conejo, A.J. Evgeniou, T., see Hibon, M. Falk, B. and A. Roy, Forecasting using the trend model with autoregressive errors Ferna´ndez-Macho, J., Comments on bCombining filter design with model-based filteringQ Fildes, R., The IJF, the Institute and forecasting software Fok, D., D. van Dijk and P.H. Franses, Forecasting aggregates using panels of nonlinear time series Forrest, D., J. Goddard and R. Simmons, Odds-setters as forecasters: The case of English football Franses, P.H. and D. van Dijk, The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production Franses, P.H., see Fok, D. Franses, P.H., see Koning, A.J. Franses, P.H., see Paap, R.
(4) 623–625 (2) 363–375
(4) 795–797
(2) 261–277 (3) 565–576 (3) 411–423 (3) 565–576
(4) 651–662
(4) 647–650 (3) 435–462 (1) 15–24
(2) 291–302 (4) 711–715 (2) 199–200
(4) 785–794
(3) 551–564
(1) 87–102 (4) 785–794 (3) 397–409 (1) 53–71
806
Author Index to Volume 21
Galbraith, J.W. and T. KVVsV nbay, Content horizons ˙ for conditional variance forecasts (2) 249–260 Garcı´a-Ferrer, A., J.G. De Gooijer, P. Poncela and E. Ruiz, Introduction to nonlinearities, business cycles, and forecasting (4) 623–625 Gardner Jr., E.S. and A.B. Koehler, Comments on a patented bootstrapping method for forecasting intermittent demand (4) 617–618 Ghiassi, M., H. Saidane and D.K. Zimbra, A dynamic artificial neural network model for forecasting time series events (2) 341–362 Goddard, J., see Forrest, D. (3) 551–564 Goddard, J., Regression models for forecasting goals and match results in association football (2) 331–340 Green, K.C., Game theory, simulated interaction, and unaided judgement for forecasting decisions in conflicts: Further evidence (3) 463–472 Gutierrez, M.-I., see Vuchelen, J. (1) 103–117 Hansson, J., P. Jansson and M. Lo¨f, Business survey data: Do they help in forecasting GDP growth? Harrison, R., G. Kapetanios and T. Yates, Forecasting with measurement errors in dynamic models Harvey, A.C., see Carvalho, V.M. Harvill, J.L. and B.K. Ray, A note on multi-step forecasting with functional coefficient autoregressive models Haugh, D., see Engel, J. Hendry, D.F., see Chevillon, G. Hibon, M. and T. Evgeniou, To combine or not to combine: selecting among forecasts and their combinations Hibon, M., see Koning, A.J. Hippert, H.S., D.W. Bunn and R.C. Souza, Large neural networks for electricity load forecasting: Are they overfitted? Horva´th, C., see Wieringa, J.E. Hubrich, K., Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy? Hyndam, R.J., Editorial Israilevich, G., see Zellner, A. Jaffry, S. and N. Capon, Alternative methods of forecasting risks in Naval manpower planning Jansson, P., see Hansson, J. Jerez, M., J. Casals and S. Sotoca, Growth, cycles, and convergence in US regional time series: A personal point of view Justel, A., see Bos, C.S. Kaiser, R. and A. Maravall, Combining filter design with model-based filtering (with an application to business-cycle estimation)
(2) 377–389
(3) 595–607 (4) 667–686
(4) 717–727 (4) 651–662 (2) 201–218
(1) 15–24 (3) 397–409
(3) 425–434 (2) 279–289
(1) 119–136 (1) 1–1 (4) 627–645
(1) 73–85 (2) 377–389
(4) 687–689 (4) 749–754
(4) 691–710
Kapetanios, G., see Harrison, R. Kholodilin, K.A. and V.W. Yao, Measuring and predicting turning points using a dynamic bi-factor model Kiefer, N.M., see Choi, H.-s. Koehler, A.B., see Gardner Jr., E.S. Koning, A.J., P.H. Franses, M. Hibon and H.O. Stekler, The M3 competition: Statistical tests of the results KVVsV nbay, T., see Galbraith, J.W. ˙ Lawrence, M. and M. O’Connor, Judgmental forecasting in the presence of loss functions Lemmens, A., C. Croux and M.G. Dekimpe, On the predictive content of production surveys: A pan-European study Lo¨f, M., see Hansson, J. Macaulay, A., see Pollock, A.C. Maravall, A., see Kaiser, R. Martin, G.M., see McCabe, B.P.M. McCabe, B.P.M. and G.M. Martin, Bayesian predictions of low count time series Medeiros, M.C., see Tera¨svirta, T. Medeiros, M.C., see Tera¨svirta, T. Mouchart, M. and J.V.K. Rombouts, Clustered panel data models: an efficient approach for nowcasting from poor data Nikolopoulos, K., Forcasting volatility in the Financial markets Nikolopoulos, K., Neural networks in business forecasting Nikolopoulos, K., Advances in business and management forecasting Novales, A., Comments on: bLinear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examinationQ Nunes, L.C., see Rua, A. O’Connor, M., see Lawrence, M. O’Connor, M., see Webby, R. ¨ nkal, D., see Pollock, A.C. O Ortega, J.A. and P. Poncela, Joint forecasts of Southern European fertility rates with non-stationary dynamic factor models Paap, R., E. van Nierop, H.J. van Heerde, M. Wedel, P.H. Franses and K.J. Alsem, Consideration sets, intentions and the inclusion of bdon’t knowQ in a two-stage model for voter choice Pagan, A., see Engel, J. Pascual, L., J. Romo and E. Ruiz, Bootstrap prediction intervals for power-transformed time series
(3) 595–607
(3) 525–537 (3) 609–616 (3) 617–618
(3) 397–409 (2) 249–260
(1)
3–14
(2) 363–375 (2) 377–389 (3) 473–489 (4) 691–710 (2) 315–330 (2) 315–330 (4) 755–774 (4) 781–783
(3) 577–594
(2) 394–394 (2) 394–395 (2) 395–395
(4) 775–780 (3) 503–523 (1) 3–14 (3) 411–423 (3) 473–489
(3) 539–550
(1) 53–71 (4) 651–662
(2) 219–235
Author Index to Volume 21 Paya, I., see Duarte, A. Pen˜a, D. and J. Rodriguez, Detecting nonlinearity in time series by model selection criteria Pe´rez Quiro´s, G., Comments on bSome methods for assessing the need for non-linear models in business cycle analysisQ Plazas, M.A., see Conejo, A.J. Pollock, A.C., A. Macaulay, M.E. Thomson ¨ nkal, Performance evaluation of and D. O judgemental directional exchange rate predictions Poncela, P., see Garcı´a-Ferrer, A. Poncela, P., see Ortega, J.A. Raeside, R., Market response models: econometric and time series analysis Ramnath, S., S. Rock and P. Shane, Value Line and I/B/E/S earnings forecasts Rangvid, J., see Rapach, D.E. Rapach, D.E., M.E. Wohar and J. Rangvid, Macro variables and international stock return predictability Ray, B.K., see Harvill, J.L. Reeves, J.J., Bootstrap prediction intervals for ARCH models Rock, S., see Ramnath, S. Rodriguez, J., see Pen˜a, D. Rombouts, J.V.K., see Mouchart, M. Romo, J., see Pascual, L. Ro¨sch, D., An empirical comparison of default risk forecasts from alternative credit rating philosophies Roy, A., see Falk, B. Rua, A. and L.C. Nunes, Coincident and leading indicators for the euro area: A frequency band approach Ruiz, E., see Garcı´a-Ferrer, A. Ruiz, E., see Pascual, L.
(2) 261–277
(4) 731–748
(4) 663–666 (3) 435–462
(3) 473–489 (4) 623–625 (3) 539–550
(2) 392–393 (1) 185–198 (1) 137–166
(1) 137–166 (4) 717–727 (2) (1) (4) (3) (2)
237–248 185–198 731–748 577–594 219–235
(1) 37–51 (2) 291–302
(3) 503–523 (4) 623–625 (2) 219–235
Saidane, H., see Ghiassi, M. (2) 341–362 Schwarz, H., see Willemain, T.R. (3) 619–620 Shane, P., see Ramnath, S. (1) 185–198 Shore, J., see Ye, M. (3) 491–501 Simmons, R., see Forrest, D. (3) 551–564 Sloboda, B., Forecasting, time series and regression: an applied approach (2) 391–392 Smart, C.N., see Willemain, T.R. (3) 619–620
Sotoca, S., see Jerez, M. Souza, R.C., see Hippert, H.S. Stekler, H.O., see Koning, A.J. Syntetos, A.A. and J.E. Boylan, The accuracy of intermittent demand estimates Tera¨svirta, T., D. van Dijk and M.C. Medeiros, Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination Tera¨svirta, T., Van Dijk, D. and Medeiros, M.C., Reply Thomson, M.E., see Pollock, A.C. Van Dijk, D., see Fok, D. Van Dijk, D., see Franses, P.H. Van Dijk, D., see Tera¨svirta, T. Van Dijk, D., see Tera¨svirta, T. Van Heerde, H.J., see Paap, R. Van Nierop, E., see Paap, R. Venetis, I.A., see Duarte, A. Vuchelen, J. and M.-I. Gutierrez, A direct test of the information content of the OECD growth forecasts Webby, R., M. O’Connor and B. Edmundson, Forecasting support systems for the incorporation of event information: An empirical investigation Wedel, M., see Paap, R. Wieringa, J.E. and C. Horva´th, Computing level-impulse responses of log-specified VAR systems Willemain, T.R., C.N. Smart and H. Schwarz, Author’s response to Koehler and Gardner Wohar, M.E., see Rapach, D.E. Yao, V.W., see Kholodilin, K.A. Yates, T., see Harrison, R. Ye, M., J. Zyren and J. Shore, A monthly crude oil spot price forecasting model using relative inventories Yokum, J.T., see Armstrong, J.S. Zellner, A. and G. Israilevich, The Marshallian macroeconomic model: A progress report Zimbra, D.K., see Ghiassi, M. Zyren, J., see Ye, M.
807 (4) 687–689 (3) 425–434 (3) 397–409 (2) 303–314
(4) 755–774 (4) 781–783 (3) 473–489 (4) (1) (4) (4) (1) (1) (2)
785–794 87–102 755–774 781–783 53–71 53–71 261–277
(1) 103–117
(3) 411–423 (1) 53–71
(2) 279–289 (3) 619–620 (1) 137–166 (3) 525–537 (3) 595–607
(3) 491–501 (1) 25–36
(4) 627–645 (2) 341–362 (3) 491–501