Springer principles of forecasting a handbook for researchers and practitioners 2001 ISBN0792379306

PRINCIPLES OF FORECASTING: A Handbook for Researchers and Practitioners INTERNATIONAL SERIES IN OPERATIONS RESEARCH & MANAGEMENT SCIENCE Frederick S Hillier, Series Editor Stanford University Saigal, R / LINEAR PROGRAMMING: A Modern Integrated Analysis Nagurney, A & Zhang, D / PROJECTED DYNAMICAL SYSTEMS AND VARIATIONAL INEQUALITIES WITH APPLICATIONS Padberg, M & Rijal, M / LOCATION, SCHEDULING, DESIGN AND INTEGER PROGRAMMING Vanderbei, R / LINEAR PROGRAMMING: Foundations and Extensions Jaiswal, N.K / MILITARY OPERATIONS RESEARCH: Quantitative Decision Making Gal, T & Greenberg, H / ADVANCES IN SENSITIVITY ANALYSIS AND PARAMETRIC PROGRAMMING Prabhu, N.U / FOUNDATIONS OF QUEUEING THEORY Fang, S.-C., Rajasekera, J.R & Tsao, H.-S.J / ENTROPY OPTIMIZATION AND MATHEMATICAL PROGRAMMING Yu, G / OPERATIONS RESEARCH IN THE AIRLINE INDUSTRY Ho, T.-H & Tang, C S / PRODUCT VARIETY MANAGEMENT El-Taha, M & Stidham , S / SAMPLE-PATH ANALYSIS OF QUEUEING SYSTEMS Miettinen, K M / NONLINEAR MULTIOBJECTIVE OPTIMIZATION Chao, H & Huntington, H G / DESIGNING COMPETITIVE ELECTRICITY MARKETS Weglarz, J / PROJECT SCHEDULING: Recent Models, Algorithms & Applications Sahin, I & Polatoglu, H / QUALITY, WARRANTY AND PREVENTIVE MAINTENANCE Tavares, L V / ADVANCED MODELS FOR PROJECT MANAGEMENT Tayur, S., Ganeshan, R & Magazine, M / QUANTITATIVE MODELING FOR SUPPLY CHAIN MANAGEMENT Weyant, J./ ENERGY AND ENVIRONMENTAL POLICY MODELING Shanthikumar, J.G & Sumita, U./APPLIED PROBABILITY AND STOCHASTIC PROCESSES Liu, B & Esogbue, A.O / DECISION CRITERIA AND OPTIMAL INVENTORY PROCESSES Gal, T., Stewart, T.J., Hanne, T./ MULTICRITERIA DECISION MAKING: Advances in MCDM Models, Algorithms, Theory, and Applications Fox, B L./ STRATEGIES FOR QUASI-MONTE CARLO Hall, R.W / HANDBOOK OF TRANSPORTATION SCIENCE Grassman, W.K./ COMPUTATIONAL PROBABILITY Pomerol, J-C & Barba-Romero, S / MULTICRITERION DECISION IN MANAGEMENT Axsäter, S / INVENTORY CONTROL Wolkowicz, H., Saigal, R., Vandenberghe, L./ HANDBOOK OF SEMI-DEFINITE PROGRAMMING: Theory, Algorithms, and Applications Hobbs, B F & Meier, P / ENERGY DECISIONS AND THE ENVIRONMENT: A Guide to the Use of Multicriteria Methods Dar-El, E./ HUMAN LEARNING: From Learning Curves to Learning Organizations Armstrong, J S./ PRINCIPLES OF FORECASTING: A Handbook for Researchers and Practitioners Balsamo, S., Personé, V., Onvural, R./ ANALYSIS OF QUEUEING NETWORKS WITH BLOCKING Bouyssou, D et al/ EVALUATION AND DECISION MODELS: A Critical Perspective Hanne, T./ INTELLIGENT STRATEGIES FOR MET A MULTIPLE CRITERIA DECISION MAKING Saaty, T & Vargas, L./ MODELS, METHODS, CONCEPTS & APPLICATIONS OF THE ANALYTIC HIERARCHY PROCESS Chatterjee, K & Samuelson, W./ GAME THEORY AND BUSINESS APPLICATIONS PRINCIPLES OF FORECASTING: A Handbook for Researchers and Practitioners edited by J Scott Armstrong University of Pennsylvania The Wharton School Philadelphia, Pennsylvania USA KLUWER ACADEMIC PUBLISHERS NEW YORK, BOSTON, DORDRECHT, LONDON, MOSCOW eBook ISBN: Print ISBN: 0-306-47630-4 0-7923-7930-6 ©2002 Kluwer Academic Publishers New York, Boston, Dordrecht, London, Moscow Print ©2001 Kluwer Academic Publishers Dordrecht All rights reserved No part of this eBook may be reproduced or transmitted in any form or by any means, electronic, mechanical, recording, or otherwise, without written consent from the Publisher Created in the United States of America Visit Kluwer Online at: and Kluwer's eBookstore at: http://kluweronline.com http://ebooks.kluweronline.com PREFACE I have been working on forecasting issues for four decades For many years, I had an ambition to write a book on principles summarizing knowledge in forecasting Big ideas are nice, but how can they be made a reality? Fred Hillier, from Stanford University, was actually a step ahead of me He suggested that I write a comprehensive book on forecasting as part of his “International Series in Operations Research and Management Science.” Gary Folven, my editor at Kluwer was enthusiastic, so the Forecasting Principles Project was born in the middle of 1996 In my previous book, Long-Range Forecasting, I summarized empirical research on forecasting but translated few of the findings into principles As a result, an update of that book would not I needed a new approach Because knowledge in forecasting has been growing rapidly, I also needed help What an amazing amount of help I received First there are the 39 co-authors of this handbook I chose them based on their prior research They summarized principles from their areas of expertise To ensure that the principles are correct, I sought peer reviews for each paper Most of the authors acted as reviewers and some of them such as Geoff Allen, Chris Chatfield, Fred Collopy, Robert Fildes, and Nigel Harvey reviewed many papers I also received help from the 123 outside reviewers listed at the end of this book They are excellent reviewers who told me or my co-authors when our thinking was muddled Sometimes they reviewed the same paper more than once Some of the reviewers, such as Steve DeLurgio and Tom Yokum, reviewed many papers Amy Myers prepared mailing lists, sent mailings, handled requests from authors, tracked down missing persons, and other things that would have been done much less effectively by me Can I thank the Internet? I marvel that edited books appeared before the Internet It does not seem feasible to conduct such a joint undertaking without it It allowed us to see each other’s work and enabled me to send thousands of messages to contributors and reviewers Many thousands Try to that without the Internet! The staff at the Lippincott Library of the Wharton School was extremely helpful Mike Halperin, head of the Lippincott Library, suggested resources that would be useful to practitioners and researchers, provided data and sources on various topics, and did citation studies Jean Newland and Cynthia Kardon were able to track down data and papers from sketchy information The Lippincott Library also has a service that enables easy searches; I click titles on my computer screen and the papers appear in my mailbox a few days later Wonderful! As part of my contract with Kluwer, I was able to hire Mary Haight, the editor for Interfaces She was instrumental in ensuring that we communicated the principles effectively No matter how hard we worked on the writing, Mary always found many ways to improve it Seldom would there be a paragraph with no suggestions and I agreed with her changes 95% of the time She edited the entire book Raphael Austin then offered to read all of my papers He did wonders on improving clarity John Carstens helped to design the layout for the chapters and solved word-processing problems He also handled the revisions of my papers, making good use of his Ph.D in English vi PRINCIPLES OF FORECASTING by helping me to find better ways to express what I was trying to say and suggesting better ways to present charts and tables Meredith Wickman provided excellent and cheerful assistance in word processing and rescued me in my struggles with Microsoft’s Word Patrice Smith did a wonderful job on proofreading The Forecasting Principles Website (http://forecastingprinciples.com) was originally established to allow for communication among the handbook’s authors John Carstens, our webmaster, designed such an effective site that it quickly became apparent that it would be of general interest He translated my vague ideas into clearly designed web pages He continues to update the site, averaging about two updates per week over the past three years Able assistance has also been provided by our computer experts, Simon Doherty and Ron McNamara The site serves as a companion to the handbook, containing supporting materials and allowing for updates and continuing peer review It also provides decision aids to help in the implementation of forecasting principles J Scott Armstrong March, 2001 DEDICATION I first met Julian Simon in 1981, although I had been aware of his research much earlier At the time, I was being considered for a chaired-professor position in marketing at the University of Illinois Julian, whom I regarded as one of the outstanding researchers in the field, was on that faculty but was not being offered a chair It struck me as unfair There was no doubt in my mind that Julian was more deserving of that chair than I was Julian and I kept in touch over the years He would call to discuss new ideas or to suggest things we might work on Usually, our ambitious plans remained on the to-do list One of his ideas was for me to compare published economic forecasts by Milton Friedman with those by Paul Samuelson Our hypothesis was that Friedman would prove more accurate because he followed theories, whereas Samuelson followed his instincts (Friedman told me he would support the project, but I never did hear from Samuelson on this issue.) In any event, their forecasts turned out to be too vague to code They also appeared to follow the adage, “Forecast a number or forecast a date, but never both.” Julian was a constant source of support for my work It was with great sadness that I learned of his death in 1998 For me, he stands as the ideal professor He knew how to find important problems, was tireless in his pursuit of answers, and had no ideological blinders He asked how the data related to the hypotheses and did so in a simple, direct, and fearless fashion His writing was clear and convincing These traits were, of course, positively infuriating to many people His forecasts also proved upsetting Consider the following: “Conditions (for mankind) have been getting better There is no convincing reason why these trends should not continue indefinitely.” Julian’s broad-ranging work includes much that is relevant to forecasters As was true for other areas in which he worked, his findings in forecasting have held up over time They live on in this book I dedicate this book to the memory of Julian Simon J Scott Armstrong March, 2001 This Page Intentionally Left Blank CONTENTS v Preface vii Dedication Introduction J Scott Armstrong, The Wharton School, University of Pennsylvania Role Playing Role Playing: A Method to Forecast Decisions 13 15 J Scott Armstrong, The Wharton School, University of Pennsylvania Intentions Methods for Forecasting from Intentions Data 31 33 Vicki G Morwitz, Stern School, New York University Expert Opinions Improving Judgment in Forecasting 57 59 Nigel Harvey, Department of Psychology, University College London Improving Reliability of Judgmental Forecasts 81 Thomas R Stewart, Center for Policy Research, State University of New York at Albany Decomposition for Judgmental Forecasting and Estimation 107 Donald G MacGregor, Decision Research, Eugene, Oregon Expert Opinions in Forecasting: The Role of the Delphi Technique 125 Gene Rowe, Institute of Food Research, and George Wright, University of Strathclyde Conjoint Analysis Forecasting with Conjoint Analysis 145 147 Dick R Wittink, Yale University and Trond Bergestuen, American Express Judgmental Bootstrapping Judgmental Bootstrapping: Inferring Experts’ Rules for Forecasting J Scott Armstrong, The Wharton School, University of Pennsylvania 169 171 Author Index Martino, J., 126, 143 Martorelli, W.P., 187, 191 Mason, C H., 620, 625, 629, 630 Massy, William F., 615, 620, 621, 628, 629 Maté, Carlos, 300, 741 Mathews, B.P., 407, 408, 411, 413, 416 Maurer, J., 359 Maury, R., 29 Mayer, T., 315, 359, 461, 469 Mazur, D J., 550, 554 Mazursky, D., 549, 554 McBride, D J., 71, 77 McClain, J O., 191, 287, 299 McClelland, A G R., 135, 143 McClelland, J., 246, 250, 255 McCloskey, D N., 310, 359, 462, 469, 702, 730, 801, 813, 822 McCollough, J., 367, 385 McCullough, B D., 485, 493, 741 McCurdy, T.H., 329, 359 McDonald, J., 315, 349, 359 McHugh, A K., 75, 79 McIntyre, S H., 89, 100, 368, 383, 550, 554, 580, 593, 813, 822 McKenzie, E., 230, 241 McKeown, J C, 392, 394, 402 McLauchlan, W G., 163, 164, 165, 167 McLaughlin, R L., 200, 213 McLeavy, D.W., 452, 469 McLeod, J C., 300 McNees, S K., 95, 104, 340, 353, 359, 361, 409, 416, 422, 437, 439, 706, 730, 731, 742 McNeil, J., 41, 54 McNown, R,, 559, 562, 566, 573, 574 McPhillamy, D J., 370, 386, 458, 469, 533, 772, 823 Mead, R., 11, 250, 255 Meade, N., 11, 12, 227, 240, 241, 282, 425, 438, 468, 470, 485, 493, 555, 577, 578, 580, 582, 583, 586, 587, 588, 590, 592, 594, 595, 620, 622, 625, 629, 630, 689, 730, 755, 777, 793, 808, 810, 812, 821, 822 Meehl, P E., 8, 12, 91, 94, 102, 110, 123, 136, 143, 178, 184, 186, 190, 286, 299, 368, 373, 385,409,416,418, 436, 822 Meese, R., 219, 241, 777, 822 Mehaffey, B J., 177, 190 Melard, G., 667, 675 Mendelson, H., 444, 468, 595 Menon, G., 110, 123 Mentzer, J T., 75, 79, 367, 369, 370, 385, 460, 469, 635, 639, 640, 641, 648, 649, 742, 767, 768, 769, 823 Merrem, F H., 105, 300 Merz, J., 546, 550, 554 Messe, L A., 27, 30 Metcalfe, M., 635, 637, 639, 644, 645, 646, 649 Michael, G C., 294, 299 Migon, H S., 590, 594 835 Milgram, S., 23, 30 Miller, D., 226, 227, 231, 236, 242, 243, 660, 676, 742 Miller, J G., 175, 191 Miller, N., 192 Miller, P M., 393, 402 Miller, R B., 485, 493 Miller, R L., 166 Millimet, C R., 87, 104 Mills, T., 241, 254, 437, 469, 822 Milner, C., 105 Milojkovic, J D., 512 Milstein, R M., 186, 191 Miner, F C., 130, 131, 138, 143 Miniard, P W., 53, 54 Mintzberg, H., 398, 402 Mitchell, W C., 233, 240, 791 Mitofsky, W J., 2, 12 Mixon, D, 23, 30 Mizon, G E., 314, 324, 325, 331, 351, 359, 360 Moeckel, C., 510, 512 Moinpour, R., 131, 132, 137, 143, 422, 437 Monaco, J A., 292, 300 Moninger, W R., 87, 105, 295, 300 Monsell, B C., 224, 240 Montgomery, D B., 155, 160, 162, 167, 615, 620, 621, 628, 629, 803, 822, 823 Moore, G H., 791, 822 Moore, J S., 292, 300 Moore, W.L., 159, 166 Morgan, M.G., 543, 554 Morris, J S., 112, 122, 316, 350, 355, 688, 742 Morrison, D G., 41, 45, 55, 56, 615, 620, 621, 628, 629, 742, 803, 823 Morwitz, V G., 9, 12, 22, 30, 31, 32, 33, 38, 42, 43, 47, 49, 50, 51, 52, 54, 55, 60, 79, 429, 435, 551, 554, 613, 615, 630, 685, 697, 698, 730, 755, 788, 803, 817, 820, 821, 823 Morzuch, B J., 362, 742 Moskowitz, H., 175, 178, 191 Moss, D J., 436 Moss, S., 289, 300 Mosteller, F., 701, 723, 729, 730 Mowen, J C., 192, 742 Moyer, R C., 311, 349, 360 Moynihan, P., 19, 30 Muchinsky, P.M., 393, 403 Muller, E., 578, 581, 592, 594, 617, 629 Mullick, S., 366, 384 Mullin, T., 115, 122 Mumpower, J L., 106, 742 G., 70, 79 Murdick, R G., 372, 385 Murdock, S., 558, 563, 574 Murnighan, J K., 129, 130, 137, 142, 504, 512 Murphy, A H., 70, 79, 95, 99, 100, 104, 298, 449, 466, 504, 514, 550, 551, 554, 769, 783, 823 Murphy, K R., 84, 87, 101 836 Murray, M P., 320, 360 Myers, P., 143 Myers, R J, 329, 360 N ’gbala, A., 531, 539 Nacher, B., 617, 629 Nachtsheim, C J., 360 Naert, P A., 600, 604, 608, 611, 754 Nagel, E., 445, 469 Naik, G., 324, 351,360 Nair, R D., 411, 415, 420, 423, 428, 437 Nakanishi, M., 598, 610 Nalebuff, B J., 22, 29, 785, 820 Narasimhan, C., 615, 630 National Industrial Conference Board, 418, 438 National Research Council, 512, 547, 554 Neale, M A., 496, 498, 511, 514 Neftci, S N., 823 Nelder, J., 250, 255 Nelson, C R., 224, 241, 314, 349, 360 Nelson, M., 249, 255 Nelson, M W., 110, 122 Nerenz, D R., 30 Neslin, S., 600, 604, 608, 610 Neter, J., 313, 360 Nevers, J V., 585, 595 Nevin, J R., 219, 241 Newbold, P., 219, 234, 241, 315, 335, 357, 430, 438, 476, 492, 635, 637, 638, 641, 649, 665, 676 Newcomb, E L., 226 Newman, J R., 550, 554 Newton, J., 213, 241, 254, 282, 385, 730, 822 Newton, J R., 792, 823 Nigrini, M., 226, 227, 241 Noelle-Neuman, E., 133, 143 Noh, J., 329, 360 Norris, K.B., 510, 515, 731 Nuclear Regulatory Commission, 547, 554 Nunnally, J C., 83,104 Nute, D., 298, 300 O’Brien, T., 143 O’Connell, R T., 492 O’Connor, K M , 438 O’Connor, M J., 10, 11, 60, 71, 73, 75, 76, 78, 79, 80, 85, 88, 91, 105, 192, 215, 216, 238, 242, 245, 247, 248, 249, 250, 251, 254, 255, 271, 282, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 399, 402, 403, 406, 407, 408, 409, 410, 411, 413, 415, 416, 421, 423, 431, 432, 437, 486, 493, 496, 514, 687, 691, 731, 755, 790, 797, 822, 823, 824 O’Connor, R M., Jr., 69, 76, 104 O’Leary, C J., 23, 30 Obermiller, C., 53, 54 Ofir, C., 549, 554, 742 Ogburn, W F., 7, 12 Ohlin, L E., 456, 469, 715, 730, 787, 823 PRINCIPLES OF FORECASTING Okun, A E., 428, 429, 438 Öller, L., 742 Önkal, D., 70,79 Ord, K., 241, 243, 254, 437, 469, 583, 592, 595, 731, 742, 822 Ormerod, P., 289, 300, 357 Orne, M T., 21, 30 Osberg, T M., 39, 40, 55 Oskamp, S., 499, 514, 810, 823 Osterwald-Lenum, M., 338, 360 Ouwersloot, H., 742 Overton, T., 53, 54, 377, 384, 798, 818, 820 Ozer, D J., 774, 823 Padilla, C., 527, 539 Paese, P W., 499, 514 Pagan, A R., 315, 329, 354, 357 Page, A L., 155, 166 Page, T J., 53, 54 Page, W., 718, 730, 809, 821 Palm, F., 357 Palmer, D K., 369, 384 Pan, Z., 196, 213 Pant, P N., 459, 461, 469, 567, 574, 783, 823 Parenté, F J., 127, 129, 137, 140, 143 Park, C S., 161, 167 Park, T., 352, 360 Parker, B R., 155, 166 Parker, P M., 583, 595 Parket, I.R., 607, 611 Parpia, B, 574 Parsons, A., 444, 466, 687, 709, 728 Parsons, L J., 598, 610 Parzen, E, 213, 241, 254, 282, 385, 469, 730, 822 Pasteels, J., 742 Patil, R., 246, 248, 255 Pattinson, H., 742 Patuwo, B E., 250, 252, 256 Paul, R J., 123, 243, 286, 287, 288, 289, 299, 373 Payne, D E., 52, 55 Payne, J W., 161, 166, 393, 403 Payne, S L., 133, 143, 784, 823 Peach, J.T., 315, 360 Pearce, S L., 275, 279, 282 Pearl, J., 683, 729 Pearson, R L., 742 Pearson, T., 103 Pecotte, B., 574 Peecher, M E., 115, 123 Peel, D A., 635, 648 Pei, B.K., 510, 512 Pencavel, J H., 422, 438 Peppers, L C., 635, 637, 638, 644, 645, 648 Peristiani, S C, 340, 353, 355 Perron, P., 777, 820 Perry, P., 2, 12, 48, 55, 808, 823 Peters, J T., 90,104 Author Index Peterson, R A., 578, 585, 594 Pfaffenberger, R., 325, 355 Pfeifer, P E., 496, 514 Pflaumer, P., 559, 563, 574 Phillips, J S., 461, 467 Phillips, L D., 69, 79, 100, 103, 134, 142, 496, 504, 514, 769, 822 Phillips, R S., 300 Picard, R.R., 488, 493 Pickett, R M., 78 Pindyck, R S., 635, 638, 642, 649 Pitz, G F., 73, 79 Ploberger, W., 332, 351, 353 Plous, S., 110, 123, 424, 438, 500, 514, 766, 785, 799, 806, 810, 823 PoKempner, S J., 418, 438 Porter, R., 608, 610 Poses, R M., 420, 428, 438 Post, W., 378, 560, 575 Potter, M C., 499, 512 Potts, R J., 104 Poulton, E C., 74, 79, 163, 166 Prescott, E C., 318, 355 Press, W., 250, 255 Preston, M G., 96, 104 Pringle, L G., 52, 55, 615, 630 Pryor, J B., 525, 539 Putsis, W P., 585, 595 Qiu, L., 12, 192, 243, 300, 386, 439, 470, 732, 761 Quadrel, M J., 550, 553 Quandt, R E., 332, 351, 360 Qui, L., 282 R aeside, R., 595, 742 Rafi, M., 12,192, 243, 282, 300, 386, 439, 470, 732, 761 Ragan, J.W., 503, 514 Raiffa, H., 108, 123 Raju, N.S., 770, 821 Ramanaiah, N V., 84, 94, 104 Randall, E J., 27, 30, 540 Rao, A V., 223, 242, 788, 823 Rao, S K., 587, 595 Rao,V R., 601, 611 Rasche, R H., 350, 358 Ravishankar, N., 476, 493 Read, S J., 533, 539 Reagan-Cirincione, P., 105, 289, 293, 300 Refenes, A P., 249, 251, 255 Reibstein, D J., 162, 163, 167, 191 Reilly, R R., 220, 242 Reis, H T., 29 Reiss, A J., 8, 12 Reiter, S., 457, 466 Reitsch, A G., 635, 637, 641, 648 837 Remus, W., 10, 73, 79, 192, 215, 245, 247, 248, 249, 250, 251, 254, 255, 394, 399, 403, 410, 416, 756, 797, 823 Requena, I., 251, 254 Reuyl, J C., 598, 600, 604, 605, 608, 609, 611 Reynolds, K D., 540 Rhyne, D M., 367, 385 Rice, G., 369, 385 Richard, J F., 314, 315, 357, 360, 388 Richards, R M., 428, 430, 438 Richardson, A., 537, 539 Richardson, J T., 580, 594 Riddington, G L., 318, 360, 823 Riggs, W E., 128, 136, 137, 143 Rippe, R.D., 342, 360 Ritzman, L P., 11, 60, 61, 79, 227, 238, 242, 388, 392, 393, 395, 397, 403, 405, 406, 408, 411, 412, 416, 426, 428, 430, 432, 438, 681, 696, 706, 731, 756, 790, 823 Roberts, W F., 300 Robertson, I D., 220, 242 Robertson, T S., 592, 594 Roebber, P J., 85, 95, 104, 105, 175, 191 Rogers, A., 559, 562, 565, 566, 567, 573, 574 Rogers, E M., 578, 580, 595, 777, 823 Roggman, L A., 418, 437 Rohrbaugh, J., 84, 87, 101, 131, 137, 138, 143 Ronis, D L., 496, 514, 515 Roose, J E., 180, 185,191 Rose, E.L., 325, 351, 355 Rose,T.L., 191, 507, 515 Rosenbaum, H F., 155, 166 Rosenkrantz, S A., 687, 730 Rosenthal, R., 687, 730, 795, 810, 820, 823 Ross, J., 497, 514 Ross, L., 501, 512, 513, 515, 525, 531, 535, 538, 540 Ross, M., 379, 384, 502, 512 Ross, W T., 742 Rossana, R.J., 316, 350, 360 Rossow, G., 128, 137, 142 Roth, J., 787, 822 Rothenberg, T J., 336, 337, 352, 356 Rothschild, B H., 23, 29 Rothstein, H G., 88, 104 Rowe, G., 10, 22, 30, 58, 60, 79, 125, 127, 129, 130, 131, 132, 136, 137, 139, 140, 143, 144, 370, 385, 561, 574, 687, 698, 699, 730, 756, 776, 781, 797, 808, 823 Rowse, G L., 421, 438 Roy, S K., 12, 324, 351, 360, 731 Rubinfeld, D L., 635, 638, 642, 649 Rudebusch, G D., 791, 821 Rudorfer, G., 249, 254 Rumelhart, D., 246, 250, 251, 255 Rush, H., 718, 730 Russo, J E., 68, 77, 79 Rycroft, R S., 655, 676, 742 838 Saari, B B., 69, 79 Saaty,T.,410,416 Sackman, H., 127, 139, 143 Sagaria, S D., 71, 80 Salancik, J.R., 132, 143 Samuelson, P., 3, 12 Sandberg, W R., 503, 514 Sanders, D E., 510, 515, 688, 731 Sanders, F., 95, 104 Sanders, H T., 2, 12 Sanders, N R., 11, 60, 61, 72, 79, 227, 238, 242, 282, 367, 369, 385, 388, 390, 392, 393, 395, 397, 403, 405, 406, 408, 411, 412, 416, 421, 426, 428, 430, 432, 438, 439, 681, 696, 706, 731, 757, 789, 790, 823 Sanderson, W C., 565, 568, 569, 570, 573, 574 Santhanam, R., 292, 300 Sanzogni, L., 250, 255 Sarantis, N., 350, 352, 360 Sarbin, T R., 12, 373, 385 Sargan, D., 315, 357 Sarle, C F., 304, 360 Sarmiento, C., 326, 360 Sauer, P L., 742 Saunders, C., 135, 144 Saville, P D., 76, 552 Sawtooth Software, 160, 166, 167 Sayrs, L.W., 196, 213 Scarso, E., 588, 594 Schaefer, E., 563, 575 Scheibe, M., 140, 143 Schipper, L., 105 Schmidt, F., 742 Schmidt, R., 742 Schmitt, N., 69, 79, 177, 191, 295, 300 Schmitt, R., 564, 574 Schmittlein, D C., 38, 42, 43, 47, 49, 50, 51, 55, 56, 625, 630, 742 Schnaars, S P., 52, 54, 220, 228, 242, 270, 278, 282, 374, 385, 426, 428, 431, 438, 444, 469, 528, 529, 531, 540, 742 Schnarch, B., 30 Schneider, S K., 530, 540 Schneidman, E S., 172, 191 Schockor, J H., 824 Schoemaker, P J H., 531, 532, 535, 540 Schofer, J., 140, 143 Schorling, C., 601, 609 Schramke, C J., 87, 102 Schreuder, R., 723, 731 Schriver, K.A., 550, 554 Schucany, W R., 485, 493 Schultz, R L., 598, 610, 633, 648 Schupack, M R., 461, 469 Schwartz, A J., 103, 334, 337, 346, 352, 460, 468 Schwartzman, D F., 540 Schwarz, G., 768, 823 Schweiger, D M., 503, 514 PRINCIPLES OF FORECASTING Schwenk, C., 503, 514, 776, 823 Schwert, G W., 336, 337, 352, 360 Scott, S., 224, 242 Seater, J.J., 316, 350, 360 Seaver, D A., 73, 79 Seeman, K., 191 Seethuraman, P B., 163, 164, 165, 167 Seitsma, J., 251, 255 Sen, S., 246, 248, 254 Sen, S K., 615, 630 Sen, T., 88, 104 Seskin, E P., 428, 429, 431, 432, 437 Sethuraman, P., 380, 386, 765, 823 Sewall, M A., 52, 55 Sexton, R S., 250, 255 Sexton, T A., 461, 469 Shahidullah, M., 559, 563, 564, 567, 574 Shamir, J., 450, 469 Shamseldin, A Y., 423, 428, 431, 438 Shanteau, J., 67, 77, 688, 729 Shapiro, A., 185, 189, 445, 451, 452, 467 Shapiro, S S., 328, 351, 361 Sharda, R., 246, 248, 255 Shaw, P., 512 Shaw, R., 300 Sheather, S J., 486, 492 Sheehan, P W., 30 Sheehan, R G., 334, 352, 357 Sheppard, B H., 38, 39, 47, 55 Sherden, W A., 7, 12, 371, 386 Sherman, R T., 521, 526, 530, 535, 540 Sherman, S J., 48, 49, 55, 498, 514, 524, 525, 531, 534, 535, 540, 742, 810, 823 Shiller, R.J., 314, 356 Shim, J., 89, 105, 634, 635, 638, 641, 649 Shiskin, J., 224, 242, 796, 823 Shocker, A D., 43, 52, 55, 242, 616, 617, 630, 676 Shockor, J H., 213, 731 Shoemaker, P J H., 742 Shoemaker, R., 600, 604, 608, 610 Shoesmith, G L., 352, 361 Short, B., 512 Shrauger, J S., 39, 40, 55 Shukla, R K., 142 Shulman, L S., 19, 29 Shulman, R S., 616, 628 Siegel, J., 634, 635, 638, 641, 649 Siegel, J S., 566, 574 Siegel, S., 783, 790, 811, 818, 823 Silk, A J., 38, 41, 46, 50, 51, 52, 54, 55, 615, 616, 620, 621, 628, 629, 630 Silver, M S., 300, 742 Silverman, B G., 294, 300, 742 Simester, D., 177, 191 Simmons, L F., 241, 254, 469, 822 Simon, H A., 4,12, 62, 79 Simon, J., 231, 237, 242, 269, 731 Sims, C A., 321, 361 Author Index Sincich, T., 220, 234, 242, 559, 562, 563, 564, 565, 566, 574, 720, 731 Singer, A., 606, 611 Sirohi, N., 742 Sivacek, J M., 27, 30 Skogstad, A L., 430, 437 Skousen, M., 3, 12 Skov, R B., 540 Skutsch, M., 140, 143 Slovic, P., 67, 79, 84, 87, 103, 104, 109, 110, 116, 118, 120, 123, 142, 177, 191, 290, 299, 370, 386, 458, 469, 500, 513, 514, 522, 533, 539, 540, 548, 553, 554, 742, 772, 822, 823 Slusher, M P., 534, 535, 540, 742 Smart, C N., 213, 242, 243, 664, 666, 668, 676, 731, 742, 824 Smith, B B., 96, 104 Smith, B T., 444, 469 Smith, D D., 366, 384 Smith, D K., Jr., 617, 630 Smith, L., 27, 30 Smith, L D., 813, 824 Smith, M A., 766 Smith, M C., 220, 242, 447, 469 Smith, P., 294, 300 Smith, S K., 220, 228, 234, 242, 375, 386, 558, 559, 562, 563, 564, 565, 566, 567, 574, 575, 720, 731, 743 Smyth, D J., 311, 349, 356, 688, 729 Snell, L., 30 Snider, E., 172, 187 Sniezek, J A., 132, 134, 137, 138, 144, 393, 403, 499, 500, 504, 514, 515, 528, 529, 539, 743 Soliman, M.A., 324, 351, 361 Solow, R M., 222, 242 Soni, H., 601, 611 Sonnberger, H., 359 Sorensen, N K., 337, 352, 358 Sosvilla-Rivero, S., 337, 355 Sowey, E R., 324, 361 Spanos, A., 314, 361 Sparkes, J R., 75, 79 Speier, H., 27, 29 Spencer, B D., 561, 572 Spencer, D E., 334, 335, 361 Speroff, T., 512 Spetzler, C S., 73, 80 Spivey.W A., 122, 212 Sprafka, S A., 19, 29 Squire, P., 2, 12, 699, 731 Srba, F., 314, 320, 355 Srinivasan, V., 151, 154, 155, 160, 161, 163, 166, 167, 379, 384, 625, 630, 822 Stäel von Holstein, C A S., 73, 80 Staelin, R., 608, 610 Stapel, I., 790, 824 Starbuck, W H., 16, 29, 459, 461, 469, 567, 574, 783, 807, 821, 823 Statman, M., 16, 30 839 Staw, B M., 497, 514 Steadman, H J., 288, 299 Steckel, J H., 602, 611 Steffey, D., 201, 212 Steiner, G A., 4, 12 Steinmann, D O., 103 Stekler, H O., 340, 353, 356, 470, 688, 730, 743 Stellwagen, E., 743 Stengos, T., 329, 359 Stenson, H H., 99, 105 Stephan, W G., 446, 469 Stephens, M A., 351, 355 Sterman, J D., 185,190 Stern, G., 319, 331, 356 Stevens, C F., 199, 212 Stewart, C., 350, 352, 360 Stewart, T R., 57, 59, 60, 80, 81, 85, 86, 87, 88, 89, 94, 96, 98, 99, 100, 103, 104, 105, 127, 128, 136, 139, 144, 172, 176, 183, 189, 191, 279, 282, 289, 294, 295, 300, 378, 386, 543, 554, 687, 691, 692, 731, 757 Stinchcombe, M., 246, 254, 255 Stock, C B., 540 Stock, J H., 311, 319, 321, 332, 336, 337, 338, 351, 352, 356, 361 Stoneman, P., 587, 594 Stoto, M., 563, 574 Stoughton, T., 243, 743 Stroop, J R., 95, 105, 429, 438 Stubbs, D A., 824 Stuckert, R P., 764, 824 Sudman, S., 133, 144, 784, 785, 807, 824 Sultan, F., 582, 595 Summers, D A., 87, 103, 104 Sutherland, J., 96, 103 Sutton, J., 227, 242 Swamy, P A V B., 318, 319, 329, 354, 361 Swanson, D., 558, 567, 574 Swanson, G E., 822 Sweet, A L., 743 Swets, J A., 78, 573 Szczypula, J., 10, 194, 195, 197, 199, 200, 201, 202, 205, 206, 207, 212, 213, 278, 281, 690, 695, 696, 729, 757, 821 Tamblyn, R., 26, 30 Tang, Z., 248, 255 Tanner, J.C., 581, 595 Tape, T G., 70, 80 Tashman, L J., 11, 223, 224, 231, 232, 242, 243, 278, 282, 300, 470, 632, 651, 658, 665, 676, 722, 731, 758, 766, 767, 812, 824 Tavlas, G S., 329, 354 Taylor, F W., 172, 173, 191 Taylor, S E., 530, 532, 540 Tayman, J., 558, 563, 567, 574, 575 Teigen, K H., 682, 731, 801, 824 Tellis, G J., 288, 300, 380, 386, 765, 823, 824 Terrell, R D., 331, 357 840 Tessier, T H., 227, 242 Tetlock, P E., 506, 507, 509, 515, 778, 824 Teukolsky, S., 255 Theil, H., 36, 41, 42, 55, 448, 469, 568, 771, 780, 787, 806, 815, 819, 820, 824 Thomas, R J., 426, 436, 616, 630 Thomas, R P., 529, 538 Thombs, L A., 485, 493 Thompson, J L., 635, 648 Thompson, P A., 485, 493 Thompson, S C., 532, 540 Thorndike, R L., 428, 430, 438 Thursby, J G., 332, 351, 361 Tien, C., 377, 378, 385 Tierney, J., 269, 282 Timmers, H., 88, 105 Timminga, E., 296, 299 Tinsley, P A., 318, 361 Tobin, J., 2, 34, 55 Todd, F J., 98, 103, 105 Tomich, E., 30 Tong, H., 482, 493 Topol, M T., 528, 529, 531, 540 Torgerson, W S., 177, 179, 192, 294, 300 Trafimow, D., 500, 515 Trajtenberg, M., 582, 595 Trost, R P., 350, 352, 358 Trumble, D., 329, 356 Trumbo, D., 87, 105 Tryfos, P., 635, 641, 649 Tsay, R S., 322, 350, 359 Tucker, L R., 98, 99, 105 Tukey, J W., 701, 730 Tuljapurkar, S., 559, 573 Tull, D S., 719, 731, 768 Turing, A M., 290, 291, 300, 824 Turner, D S., 410, 416 Turoff, M., 127, 139, 142, 143 Tversky, A, 72, 74, 80, 103, 110, 123, 132, 133, 135, 142, 144, 192, 300, 498, 502, 512, 513, 514, 515, 522, 523, 529, 530, 533, 539, 540, 545, 550, 553, 554, 764, 773, 785, 806, 822, 824 Twain, M., 230 Tweney, R D., 91, 104 Tyebjee, T T., 16, 30, 768, 824 Tzavalis, E., 313, 353 Ullman, D G., 87, 105 Ungar, L., 246, 247, 254 Urban, G L., 52, 55, 152, 167, 614, 615, 616, 617, 630 Vaccaro, J A., 250, 255 Vallone, R P., 497, 515 Van de Ven, A.H., 127, 130, 137, 144 Van den Bulte, C., 585, 595, 623, 630, 743 Van den Heijden, A H C., 723, 731 van der Duin, P., 532, 539 PRINCIPLES OF FORECASTING Van Dijk, D J C., 797, 821 Van Imhoff, E., 560, 575 Vandome, P., 428, 432, 438 Vanhonacker, W R., 602, 611 Vassilopoulos, A I., 200, 204, 206, 208, 211 Vaupel, J W., 560, 571, 573, 575 Veall, M R., 485, 493 Vere, D J., 311, 349, 361 Vetter, D E., 452, 453, 468, 713, 730 Vettering, W., 255 Vokurka, R J., 275, 279, 282, 743 von Winterfeldt, D., 73, 79, 110, 122, 546, 550, 554 von zur Muehlen, P., 319 Voss, P R., 567, 574, 575 Vriens, M., 148, 151, 155, 167 Wade, N., 452, 469 Wagenaar, W A., 71, 80, 88, 105, 497, 515, 723, 731, 790, 807, 824 Walker, H E., 424, 438 Wallace, H A., 173, 191 Wallsten, T S., 496, 512, 513, 546, 550, 553 Walster, G W., 142 Wang, D F., 515 Warner, S L., 40, 55 Warshaw, P R., 38, 39, 47, 53, 55 Wason, P C., 71, 80, 501, 515 Wasserman, P D., 246, 255 Wasserman, W., 360 Watson, G S., 311, 319, 321, 330, 332, 338, 351, 356, 361, 547, 553 Weaver, D., 525, 540 Weaver, J., 300 Webb, J J., 315, 360, 687 Webby, R G., 11, 60, 80, 85, 105, 238, 242, 271, 282, 388, 389, 390, 391, 399, 401, 403, 406, 416, 691, 706, 731, 758, 790, 824 Webster, E C., 454, 470 Wei, W W S., 476, 493 Weigend, A., 251, 255 Weimann, G., 451, 470, 723, 731 Weinberg, B D., 152, 167 Weinberg, C B., 421, 428, 433, 438 Weiss, D L., 191, 605, 608, 610 Weitz, B A., 822 Weitz, R R., 298, 300 Wellman, B T., 680, 681, 730 Welty, G., 128, 144 Wendell, J., 352, 356 Wenger, W., 132, 143 Werner, P D., 177, 191, 507, 510, 515 West, K D., 330, 361 West, P., 550, 554 West, P M., 601, 611 West, R., 73, 78 West, S G., 436 Weverbergh, M., 600, 604, 608, 611 Weyant, J M., 436 Author Index Whalley, P., 135, 144 Wheelwright, S C., 249, 255, 634, 635, 637, 641, 644, 645, 648, 761, 822 White, H., 246, 254, 255, 448, 507, 563, 564, 566, 575, 743 Whitecotton, S M., 510, 515, 688, 731 Whittaker, W., 681, 730 Whybark, C., 463, 468 Wiggins, N., 177, 179, 191 Wigton, R S., 70, 80 Wildt, A., 331, 351, 361 Wiley, K., 11, 534, 539 Wilk, M B., 328, 361 Wilkinson, L., 723, 731 Wilkinson, M., 342, 360 Willemain, T R., 200, 213, 223, 242, 282, 407, 408, 416, 669, 676, 689, 731, 743, 788, 824 Willham, C F., 549 William, W H., 30, 112, 122, 386, 439 Williams, D W., 226, 227, 231, 236, 242, 660, 676 Williams, W H, 234, 243, 316, 350, 356, 482, 484, 494 Willis, F N., 30 Willis, R H, 23, 30 Willis, Y A., 23, 30 Wilson, J H., 634, 635, 644, 649 Wilson, R., 547, 553 Wilson, R D., 52, 55, 615, 617, 630 Wilson, T D., 53, 55 Wilsted, W D., 94, 105 Wimbush, J C., 804, 824 Wind, Y J., 203, 213, 616, 617, 629, 630 Winkler, R L., 95, 96,104, 105, 213, 234, 241, 254, 282, 298, 385, 411, 416, 420, 422, 426, 428, 430, 431, 436, 437, 438, 439, 466, 469, 484, 488, 493, 504, 514, 554, 719, 730, 743, 783, 822, 823 Winman,A., 505, 515 Winston, C., 4,12, 450, 470 Winters, P R., 203, 782, 787, 824 Wisniewski, M., 617, 618, 629 Witt, C A., 344, 361, 375, 386 Witt, S F., 344, 361, 375, 386 Wittink, D R., 10, 48, 55, 60, 80, 146, 147, 148, 151, 155, 158, 159, 160, 162, 163, 164, 165, 166, 167, 176, 178, 192, 289, 300, 316, 350, 377, 386, 601, 604, 605, 606, 607, 608, 610, 611, 654, 676, 695, 702, 731, 754, 758, 761, 773, 787, 799, 816, 824 Woehr, D J., 433, 437 Wold, H., 8, 12 Wolf, M., 616, 628 Wolfe, C., 410, 416 Wolfe, H.D., 418, 439 841 Wong, B K., 292, 300 Wong, H., 434, 436 Woo, C., 134, 142, 378, 384 Woodlock, J W., 615, 619, 620, 621, 622, 623, 628, 629 Wortmann, R L., 76, 552 Wright, G., 10, 22, 28, 30, 58, 60, 69, 77, 78, 79, 87, 95, 101, 125, 127, 129, 130, 131, 132, 133, 134, 135, 136, 137, 139, 140, 142, 143, 144, 192, 239, 260, 281, 370, 385, 390, 391, 401, 414, 486, 494, 514, 532, 538, 561, 574, 634, 635, 639, 641, 644, 649, 687, 698, 699, 728, 730, 757, 759, 776, 781, 797, 808, 823 Wright, M., 379, 386 Wright, P., 162, 167 Wroblewski, W J., 212 Wu, L S-Y., 476, 493 Wyer, R S., 527, 539 Yang, M., 338, 354 Yaniv, I, 380, 384, 496, 512 Yar, M., 481, 492, 494, 667, 675, 676 Yates, J F, 84, 88, 103, 496, 510, 514, 515 Yeo, S., 314, 320, 355 Yesavage, J A., 191, 507, 515 Yitzhaki, S., 582, 595 Yntema, D B., 177, 179, 192, 294, 300 Yokum, J T., 243, 297, 298, 300, 331, 351, 361, 369, 370, 386, 439, 454, 470, 558, 694, 714, 731, 743, 783, 789, 824 Yoo, B S., 322, 337, 338, 350, 352, 356, 358 Yoon, Y., 297, 299 York, K M., 97, 106 Young, K C., 300 Young, P., 583, 592, 595, 743 Yu, Y C., 564, 573 Yum, C S., 743 Z apata, H O., 350, 361 Zarnowitz, V., 428, 430, 439, 743 Zehner, K S., 514 Zeithammer, R., 617, 618, 629 Zellner, A., 197, 199, 200, 202, 213, 305, 357, 361, 455, 456, 470, 604, 611, 743, 818 Zeng, Y., 560, 575 Zhang, G., 250, 252, 256 Zhang, W., 353 Zhenglian, W., 560, 575 Zhu, Y., 515 Ziliak, S T., 310, 349, 359, 462, 469, 702, 730, 801, 813, 822 Zimbardo.P., 17, 18, 21, 30 Zmijewski, 401 Zubrick, S M., 300 Zuckerman, M., 29 This Page Intentionally Left Blank SUBJECT INDEX ACA method, 162 Academic journals, 25, 61 success, 233, 295, 373, 764 Acceleration, 230, 763 Acceptability of forecasts, 723, 724, 807 of methods, 18, 297, 370, 372, 638, 694, 734 Accuracy measures, 456, 568–569, 588, 641–642 Adaptive models, 202, 205, 209, 318, 319, 432, 645, 724, 824 Additive decomposition, 112–113, 683, 776 model, 177, 375, 477, 482, 763 seasonality, 225, 637 trend, 227, 230, 234, 279, 690 Adjusted 457, 641, 647, 763, 803, 813 Adjusting forecasts, 52, 72–74, 135, 153, 200, 203, 230, 272, 237, 340, 388, 390, 396–397, 406–414, 479, 641, 662, 705–706, 764, 789, 805 Advertising elasticity, 380, 765 predicting effects of, 185 predicting pages, 180–183, 420, 773 African nations problem, 764 Aggregation across decision units, 164–165 experts or judges, 95, 126, 128–129, 605 products, 223, 315–316, 669 space 223 time, 223, 317 AID (Automatic Interaction Detector), 295, 764 Air travel, 2, 381, 488 Airline deregulation, Akaike Information Criterion (AIC), 334, 352, 658, 764 Algorithmic decomposition, 109–111, 117–118, 120 Almanac questions, 111, 128, 137, 500, 509 Alternative environments, 369, 641, 703 explanations, 498, 501–503, 510, 530 futures, 544 Ambiguity, 414, 510, 544–547, 550–552 Analogous data, 220, 446–447, 686 task, 379 Anchoring, 72–74, 121, 133, 135, 531, 764 Anscombe’s quartet, 458 Anticipations, see expectations ARCH model, 328–330, 764 ARIMA, 196, 307, 327, 328–329, 345–346, 477, 479–481, 489, 559, 566, 588 Artificial data, 186, 306, 393–394, 396–397, 399, 407– 408 tasks, 139, 182–184, 293, 294 Ashley’s theorem, 309 Assessment center tests, 765 Assumptions (testing of), 445–449, 465, 672–673 Asymmetric errors, 234, 482, 487, 765, 774 loss functions, 68, 96 Attraction models, 600, 765 Attributional bias, 766 Autocorrelation, 72, 315, 319, 324–325, 327, 330, 351, 481, 562, 658, 717, 766, 769, 774, 779, 787, 795, 805 Automatic feature identification, 295, 764 Automatic Interaction Detector, 295, 764 Automobile forecasts, 37, 43, 49, 235, 265, 393, 429, 529, 730, 807 Autoregressive (AR) model, 196, 307, 320–321, 325–327, 331, 371, 477, 485, 490, 562, 589, 765, 766, 769, 772, 777, 785, 818 Availability heuristic, 498, 522, 524, 529–531, 766 Backcasting, 226, 381, 447–448, 766 Backward telescoping, 51 Bafflegab, 451, 747, 766–767 Bank teller, 806 Bankruptcy, 181, 311, 773 Base rate, 40, 99–100, 133, 218, 226, 230, 444, 456, 497, 500, 695–696, 767, 769 Basic trend, 267–269, 279, 767, 773 Bass model, 317, 581, 584, 623 Battle of Dorking, 809 Bayesian analysis, 334–5, 767 forecasting, 311 method, 455, 485–486, 491, 582, 590, 767, 768 model averaging, 490 pooling, 199–210, 767 vector autoregressive models, 196–198, 323, 347 Beecham vs Yankelovich, 726 Benchmarks, 41, 136, 236, 239, 277, 278–280, 308–309, 319, 324, 345–347, 406, 444, 584, 592, 602, 652, 655–656, 663, 672, 767, 772, 787, 810, 815 844 Benford’s Law, 226 BFE (Bold freehand extrapolation), 768 Biased data, 636–637, 685–687 effect on combining, 489 forecaster, 236, 396, 410–414, 421, 445, 449– 450, 500–501, 726–727, 789, 808 hindsight, 462, 505, 509, 544–549, 786 judgment, 7, 34, 40–42, 48–53, 60–65, 68–74, 100, 132–6, 172, 175, 294–295, 367, 370, 766, 784–785, 799–800 nonresponse, 464, 798, 818 researcher, 220 statistical method, 253, 267–268, 279, 324, 330–331, 340, 480, 488, 566, 585, 603604, 765, 808 Bicycle pump study, 379 Blue Chip Economic Indicators, 419–420, 422 Bootstrapping judgmental, 10, 94, 171–192, 286–296, 368, 376–377, 380, 641, 701, 706, 768, 789, 791–792 statistical, 328, 485 Bottom-up forecasts, 315–316, 349–350, 669, 682, 768, 805 Box-Jenkins, 231, 234, 245–250, 309, 366–367, 488, 769 Brainstorming, 682, 769 Bridge hands, 504–505, 509 Brier score, 99, 421, 428, 769 Broken leg cues, 184, 409 Brown vs U.S Board of Education, 446, 726 Brunswik lens model, see lens model Business cycles, 770, 790, 791, 793, 797 C able television, 526–527, 585–586 Calibration data, 231, 233, 447, 461, 463–464, 626, 763, 783, 786 of experts, 504–505, 769–770 rules, 277–278 Camera sales, 311, 381, 432, 795, 813 Cannonical correlation, 770 Caribou chips, 457 Carter-Ford election, 523 Causal chain, 533, 635, 684, 770, 812 explanations, 230, 233, 446, 505, 507, 519, 534–535, 598 factors, 112, 200, 560 forces, 235–237, 262–280, 391, 453, 635, 683, 696, 770 information, 396–398, 408–409, 565 methods, 374, 638, 663, 692–693 models, 342–344, 445, 567–568, 662, 770 relationship, 201 variables, 173–177, 183, 303, 305–320, 338– 341, 325–326, 375, 380, 448, 462, 658, 663, 701, 703–704 PRINCIPLES OF FORECASTING Census Program X-11 or X-12, 224, 770, 787 Chinese epidemics, 270, 451 Cholesterol, 4–5 Chromium prices, 269–270 Citations, 4, 174, 292, 812 Clarence Thomas confirmation, 454–455 Classical decomposition, 279, 771 Cleaning data, 222, 249, 655–656, 688 Clustering,200–211 Coefficient of determination, see Coefficient of variation, 198, 204–205, 268, 274, 412, 424, 771 Cognitive dissonance, 162, 771 feedback, 63, 66, 69–70, 771 strain, 289 Coherence, 135, 350–532, 771 Cohort model, 557–560, 564–568, 771 Cold cognitive error, 499 Combined estimates, 109, 227, 272, 772 forecasts, 69–70, 116, 128, 136, 174, 261, 372, 411, 417–435 methods, 247 model, 203 prediction intervals, 476, 486, 665 Commensurate measures, 533, 772 Communication of information, 137, 548–550, 555 principles, Company earnings, 394, 395, 423, 424, 428 Comparing methods, 459–463, 798 policies, 380 Compensatory model, 772 Competing hypotheses, 276, 444, 796, 813 Competition, 203, 211, 224–235, 316, 345, 382, 673, 783, 785, 794 Complex methods, 227–228, 374–375, 451, 694–695, 712 models, 94, 158–160, 185, 305, 308, 312, 485, 658 problems (tasks), 5, 62, 70, 88, 92, 108, 181– 182, 288, 296–297, 399, 410 situations, 261, 296, 789 v simple methods, 566, 587, 593, 619–621, 638, 652 writing, 451 Concrete examples, 553 Conditional forecast, 308, 318, 326, 339, 348, 483, 584, 772, 782 Conditional v unconditional forecasts, 89, 704 Confidence intervals, 65, 73, 233, 369, 424, 476, 528, 560, 563, 580, 772, 817 Confirmation bias, 4, 63, 69, 71 Conflict situations, 15–16, 20, 26–28, 807 Conjoint analysis, 145–167, 178, 184, 186, 289, 370, 601, 654, 763, 772 Subject Index Conjunction fallacy, 530, 533, 773 Consensus, 131, 180, 421, 456, 497, 503, 561, 568, 773 Conservatism, 217, 229–230, 262, 271, 561, 638, 689, 695, 773, 787, 814 Consistent forecasts, 377 trends, 271, 773 Construct validity, 424, 446, 454, 456, 465, 687, 774, 779 Consumer behavior, 624, 815 durables, 148, 367, 578, 583, 587 price index, 221 Contextual information, 389–396, 406, 408, 410– 414, 774 Contingency plans, 66, 529–530, 701, 723, 772, 809 Contrary series, 235, 267–271, 275–276, 764, 774 Control group, 129, 453, 500, 774 Controlled feedback, 126 Convenience stores, 804 Convention center forecast, Corn, quality of, 173 Correlation coefficient, 774, 796, 803 matrix, 824 Cost-benefit analysis, 464, 670 Cost savings, 296, 369–370 Coupon redemption rates, 432 Cross-sectional analysis data, 177, 184, 197, 201, 208, 215, 218, 226–230, 285, 302, 311, 313, 375, 378, 444, 446, 456, 651, 463, 700, 775 Cross-validation, 775, 778 Cue, 84–96, 173-176, 184, 371, 409, 775 Cumulative error (also CUMRAE), 775, 270–274, 463, 471 Current status, 220, 409, 422, 775 Cycles, 3, 233, 268, 562, 638, 695, 770, 775, 793, 800 Dallas Cowboys, 172 Damped seasonably, 255 Damped trend, 230–233, 262, 463, 637, 690, 775, 795 Data access to, 450–451, 711 cleaning, 222, 249, 655–656, 688 collection, 152, 161, 165, 306, 312, 624, 637, 650 generating process (DGP), 312, 775 mining, 10, 317, 702, 758 preparation, 219, 637, 645–646, 652, 688 sources, 636, 646, 684–685 Debias, 68, 501, 510, 531–532, 535 Decay forces, 263–269, 391, 683, 770, 775 845 Decision making, 17, 19, 23, 110, 133, 223, 371, 379, 495–496, 504, 508, 522, 680–682, 717 Decomposition by causal forces, 265–267, 635 of judgment, 107–123 Delphi technique, 125–144, 370, 373, 776, 780, 795, 797 Deregulation, effects of, 450 Detrend, 776 Development costs, 369, 464 Diagnostic tests, 314, 662–663 Differencing, 305, 324, 338, 352, 765, 777 Diffusion curve, 578 index, 778 models, 577–595, 622, 625, 779 of innovations, 197, 777 of principles, 631–676 Disaggregation, 559–560, 605, 607–608, 768, 776, 816 Disconfirming evidence, 71, 454, 778 Discontinuity, 399, 687, 778 Discriminant analysis, 43, 778 Disjunctive model, 778 Double cross-validation, 778 moving average, 778 Dummy variable, 174, 224, 313, 318–319, 328, 337, 352, 667, 778, 789 DuPont, 297 Durbin-Watson statistic, 324, 335, 326, 330–331, 351, 766, 779 Ease of implementation, 130, 369 interpretation, 369, 454, 785 use, 275, 369–370, 454, 568, 655, 671 Eastport, Maine, 378 Eclectic research, 779, 803 Econometric forecasts, 233, 409, 420, 422, 427 indicators, 419–422, 496, 791, 797 Elasticity of advertising, 765 income, 446, 703 price, 184, 450, 765, 781 Elections, political, 2, 227, 699, 808 Ellipses, 179, 294 Empirical prediction intervals, 234, 673 Employment interviews, 369 Ensemble forecasts, 418, 780 Epidemics, 270, 451 Error asymmetric, 234, 482, 487, 765, 774 correction model, 308, 320–324, 335, 347, 352, 772, 780 cost of, 449 distribution, 91, 326–331, 482–483, 487, 780 846 measure, 277–278, 449, 455–460, 470–472, 642–643, 664, 683, 715-716, 783–784, 814–815 Esso Petroleum vs Mardon, 727 Exjoint analysis, 178 Exotic animals, 107 Expectations, 8, 10, 36–38, 44, 263, 269, 637, 698, 774, 781, 814 Experiential training, 688, 794 Experimentation, 21–23, 27, 368 Expert forecasts, 182, 293, 367, 376–380, 429– 430, 641 Explanation effect, 499–501, 781 Exploratory research, 782 Ex post forecasts, 309, 331, 339–347, 447, 461– 462, 465, 663, 716–717, 782, see also unconditional Extreme estimates, 114, 127 values, 131, 199 Eyeball extrapolations, 393 Face validity, 21, 290–291, 455–456, 782, 817 Facilitator, 126–127, 131, 782 Factor analysis, 782 FAITH model, 445, 451, 452 Falkland islands, 16 Fault tree, 68, 290 Feature identification (time series), 279–280, 782 FedEx, 378 Feedback, 60, 63–71, 96–97,126–141, 173–185, 289, 380, 410–414, 421–422, 504–511, 543546, 771, 782, 791–792, 800, 809–810 Field experiment (field studies), 22, 219–220, 379, 781 Filter, 196–209, 783, 790 Financial forecasts, 328 markets, Fireman study, 421, 529, 533, 535 First differences, 308, 318, 320, 336, 339, 766, 777, 793 Focus Forecasting, 444, 463 Focus groups, 6, 783 Football, 25–26, 128, 172, 187, 422, 502, 507– 509, 523–524, 535–536 Ford automobile forecasts, 235 Forecast competition, 203, 211, 316, 224–235, 382, 451, 783, 794 origin, 327, 340 records, 57, 60 Fortune survey, 429 Forward-telescoping, 50–51 Framing, 132, 697, 784–785 Freedom of speech, 132 Full disclosure, 276, 290, 449–451, 656, 711– 712 PRINCIPLES OF FORECASTING Functional form, 160, 177, 228, 246, 268, 279, 317, 375, 619, 785, 812 Gambler’s fallacy, 785 Game theory, 22, 368, 785 Garbage fee, 377 Gasoline prices, 219–223, 231–232, 236–237 General Motors, 273 Genetic algorithm, 785 Global assessment, 786 Goodness of fit, 786 Graphs, 66, 71–72, 222, 409–410, 432, 656 Great Depression of 1990, 810 Grid search, 231 GRIFFIN, 397, 399 Gun control, 425, 681 H eterogeneous experts, 128, 698 Heteroscedasticity, 319, 327–333, 786, 793 Heuristic, 73–74, 279, 498, 521, 522–524, 529– 531, 544, 766, 786 Hierarchical model, 99, 200–201, 653–654,786 Hierarchy of effects, 615, 786 Highway deaths, 265–267 Hindsight bias, 63–64, 69–70, 462, 505, 509, 544–549, 786 Hit rate, 786 HIV, 534, 536, 525 Hockey, 172, 187 Holdout sample, 708 tests, 156, 164 Horse race betting, 504 trading problem, 784 Hospital forecasting practices, 367, 680 interns, 70, 184 Iceland, 801 IFO, 814 Illusion of control, 787 Income tax, 206 Inconsistency, 60, 64–65, 71–74 Inconsistent trends, 271, 276, 787 Information acquisition, 82, 86, 89, 92–94, 99–100 processing, 86–88, 92–96, 99–100 Input-output analysis, 448, 696, 788 Instabilities, 229, 232, 236, 268, 272–275, 319, 332–333, 391, 423, 639, 788 Intentions, 22, 33–56, 368, 377–378, 428–429, 755, 788 Interactions among decision makers, 16–28, 130, 136–140 rules, 289 variables, 150–151, 159–160, 600, 635, 683, 764, 788 Subject Index Intercept corrections (level corrections), 272, 340, 409 Intermittent series, 200, 222, 237, 689, 788 Internal Revenue Service, 226, 450 International Institute of Forecasters, 7, 789 International Symposium on Forecasting, 7, 448, 631 Intuition, 5, 90, 109–110, 789 Inverse Power Transform (IPT), 581 IQ scores, 452 Irrelevant data, 637, 688 early data, 268, 279 J ackknife, 463, 789 Journal of Consumer Research, 815 Judgmental adjustment, 231, 397, 405–414, 479, 789 bootstrapping, 169–192, 283, 287, 292–296, 376–380, 641, 768 extrapolations, 2, 85–86, 396–397, 789, 390 override, 669, 673 Jury decisions, 23, 27 Juster scale, 36–41, 790 Kalman filter, 196–209, 790 Laboratory experiment, 21–22, 219, 379 Leading indicators, 196–197, 367, 779, 791 Lens model, 85–87, 93, 96, 98–100, 792 Life on the Mississippi, 230 Linda problem, 806 Literary Digest, Lockheed Corporation, 27 Logistic, 317, 579–591,793 Logit, 793 Log-log model, 689, 786, 788, 793 Lucas critique, 309 Market share, 50, 69, 152–158, 185, 219, 221, 379, 422, 445, 502, 597–611, 747, 752, 753, 765 Markov chains, 486, 794 Mean square error, 456, 459–460, 568–569, 641– 643, 717, 795 Mechanical adjustments, 340, 409, 789, 815 combinations, 421 integration of judgment, 441 Medical decision making, 68, 89, 287, 746, 751, 752, 757 forecasts, 74, 100, 495 school admissions, 186, 508–509 Meta-analysis, 39, 93, 95, 195, 288, 313, 423, 582, 600, 801 Mini-Delphi, 776, 780 Minimum wage, effect of, 450 Mistakes, 222, 232, 278, 375, 452, 459, 643, 691 847 Mitigation, 795 MMPI, 179, 293, 295 Model complexity, 158, 159, 160, 185, 308, 566–567, 764 development, 169, 174–175, 182–286, 312, 350–351 specification, 305, 336, 345, 600, 607 uncertainty, 486, 488, 491 Moving average, 224, 228–229, 307, 367, 765, 778, 782, 796 origin, see successive updating Multicollinearity, 176, 315, 340, 351, 774, 796 Multiple hypotheses (competing hypotheses), 276, 444, 796, 813 N ational Football League, 23–26 National Science Foundation, 508 Natural resources, prices of, 237, 269–270, 718 Neural networks, 245–256, 488, 601, 652–655, 748, 797 Nil hypothesis, 799 Nixon in China, 70 Nobel Prize, 2, 222, 788 Nondirective interviewing, 783, 797 Nonexperimental data, 798 Nonresponse bias, 464, 798, 808, 814, 818 Normal Group Technqiue (NGT), 127, 130, 797 Nowcasting, 128, 226, 798 Nuclear power plants, 419, 547 Null hypothesis, 798 Objectives, 20, 308–310, 396, 558, 634, 636, 645–646, 747, 801 Occam’s razor, 799 Optimism, 7, 96, 236, 379, 488, 528, 719, 768, 799 Ordinal scale, 149, 277, 799 Outliers, 196–199, 206–209, 222, 224, 268, 279, 326–328, 457–460, 482, 487–488, 791, 795, 798, 800, 806, 807, 818 Overconfidence, 65–66, 73–74, 134–135, 424, 486, 495–515, 531, 549, 721, 752, 786, 800, 806 P analba, 24, 26 Philadelphia Flyers, 172, 187 Planning, 2–3, 65, 532–538, 747, 801 Plumber example, 520 Policy analysis, 3, 305–309, 318, 339, 370, 377, 425, 560, 640, 701–702, 721–723 capturing, 172 changes, 223, 236, 445, 450, 462, 716 Political conventions, 20 effects on forecast, 367, 410, 449–451, 507, 789 848 elections, 227, 377, 699, 808 polls, 2, 48, 162, 446 role playing, 17 Population forecasting, 220–222, 234, 375, 557– 575, 685, 720 Poverty estimates, 803 Practical significance, 801 Preciseness, 801 Precision, 802 Prediction interval (PI), 222, 233–235, 328, 424, 475–494, 501, 530–531, 643–644, 665–667, 718–723, 765, 772, 776, 783, 800, 802 President Reagan, 773 Product hierarchy, 802 Production forecasts, 178, 410 rules, 260, 289 systems, 289 Projective test, 803 Protocol, 260–261, 287–288, 293–295, 802, 803 Proxy variable, 803 Psychometrics, 83 Public opinion polls, 20 Purchase intentions, 33–56, 616, 755, 803 Quasi-experimental data, 803 Radiologists, 68 RAE (Relative Absolute Error), 277, 423, 455– 461, 471–472, 784, 806 Rainfall, 423, 428, 431 RAND Corporation, 126, 139 Randomized Response Technique, 804 Reactance, 536 Reconciling forecasts, 805 Regressing forces, 805 Regression analysis, 84, 173–183, 226, 305–362, 367, 640, 701, 704, 722, 764, 779, 791, 792, 796, 799, 805, 810, 813 to the mean, 263–264, 268, 566 Regulation, by the government, 4, 450 Reinforcing series, 267–270, 806 Relative Absolute Error (RAE), 277, 423, 455– 461, 471–472, 784, 806 Reliability of causal forces, 265, 279 coding, 19 confidence intervals, 369 data, 450, 465 judgment, 81–106, 128, 369, 397 methods (or models) 181–182, 453, 806 scales, 38 Replication, 177, 232, 450–453, 465, 548, 643, 711–713, 806 Representativeness, 806 Response error, 40, 464, 697, 806, 814 Retrospective process tracing, 807 Risk, 550, 665, 706, 718, 721, 777 PRINCIPLES OF FORECASTING Robust trend, 262, 372, 807 Role playing, 13–30, 368, 378, 785, 807 taking, 807 Rolling horizon, see successive updating Roosevelt-Landon election, Root mean square error (RMSE), see mean square error R-square, 457–458, 640–643, 647, 717, 803-804 Rule-based forecasting, 257–282, 452, 808 Sales forecasting, 5, 38, 65, 72, 228–229, 260, 269, 367, 389–392, 394–396, 398–400, 413, 421, 613–617, 686, 719, 768 Sampling error, 464, 802, 808, 814 Samuelson’s Economics, Saturation level, 578–592, 622–623, 793, 808 Scaling data, 249, 451, 579 errors, 277, 455–456, 568, 660, 783 intentions/expectations, 33–55 preferences, 149 questions, 74, 546 time series, 198–200 variables, 174, 177, 180 Scenarios, 519–540, 644, 766, 808 S-curve, 197, 615, 806, 813, 814 Seasonality, 203, 206–208, 218, 224–226, 249, 337–338, 637, 656, 770–771, 775, 787, 796, 809 Seer-sucker theory, 809 Segmentation, 43–44, 112, 560, 764, 768, 771, 776, 809 Selecting data, 219–221 experts, 356, 413 functional form, 279 graduate students, 186 forecasting methods, 62, 231, 266, 270–271, 275, 363,–386, 391, 444, 565–567, 588– 592, 638, 657–660, 669–670 job applicants, 8, 93–95, 172, 184, 185, 187, 369, 433, 447, 461, 772 stocks, 288 Supreme Court justices, 454 time series, 200 versus combining, 426 Self-confidence, 809 Self-defeating prophecy, 810 Self-fulfilling prophecy, 810 Sensitivity analysis, 810 Setwise regression, 810 Shoplifting, 536 Short time series, 278, 483 Shrinkage, 810 Shrinking, 423, 640, 810 Simplicity, value of, 8, 19, 69, 84, 158–160, 176–181, 185, 209, 227–231, 248, 261, 269, 289, 305, 508–309, 324, 333–335, 374–375, Subject Index 444, 566–567, 587–590, 619–621, 638, 722– 723 Simultaneous equations, 324, 684, 811 Skewness, 181, 327–328, 482, 656, 765 Socially desirable (undesirable) behavior, 48, 803, 804 Social Security Administration, 561 Stolen motorcycle, case of the, 290 South Africa, 505–507 S-shaped curve, 577–579, 620–621, 802, 810, 812 Starting values, 226, 766, 812 Start-up series, 263 Stein estimation, 196 Stepwise regression, 457, 701, 813 Stock market, 262, 371 Structural changes, 231 Structured judgment, 60, 130, 137–138, 230– 231, 260, 263, 369–370, 373, 398, 409–410, 421, 561, 780, 782, 813 Students admissions, 179, 186 as subjects, 17 behavior, 39, 526–527 dental, 447 grades, 179, 295, 792 job choices, 155, 495 law, 290 medical, 70, 508–509 Subjective probabilities, 70, 133–134 Successive updating, 228, 277, 463–464, 814 Suicide, 172 Supreme Court, 454 Suspicious pattern, 268, 814 Telecommunications, 205, 232, 453, 583–590, 747, 753, 754, 758, 807 Telescoping, 50-51, 814 Theft, 804 Theory seer-sucker, 809 use of, 8, 10, 305–339, 425, 636, 640, 701, 702 value of, 10, 450, 685, 813, 815 Time magazine, 180, 420, 773 Time-series decomposition, 279, 771, 818 849 features, 268, 279 Tom W problem, 133 Top-down forecasts, 112, 315–316, 669, 816 Tornados, 2, 449 Track record, 363, 371–372, 423, 434, 638, 654, 658, 660, 663, 673 Tracking signals, 232, 816 Trade-off analysis, 816 Trading days, 816 Transformations, 482, 583, 637, 644, 656, 720, 793, 816 Transylvania problem, 393 Trimmed means, 422 Truman-Dewey election, Turning point, 198, 566, 817 Unbiased, see bias Uncertainty, 133–135, 225, 233–236, 262, 273–275, 411–413, 424–427, 434, 473– 494, 501–502, 528–532, 545, 569–570, 579-580, 637–646, 665–667, 705, 718– 723, 772, 795, 817, 825 Unconditional forecasts, 781, 817 Unit roots, 777, 817, 320–322, 334–338 United Nations, 764 U.S Congressional mailings, 264–265 University of Texas Medical School, 186, 508– 509 W ashington and Lee University, 20 Wealth, concentration of, 450 Weather forecasting, 2, 70, 86–89, 95, 175, 223– 224, 293, 295, 418, 420, 423, 449, 496, 504– 505, 726, 782 Weatherhead data, 235, 270 Weighted Application Blank, 818 Weighting forecasts, 130–132, 267, 274, 411, 421–425, 453, 567 responses, 43 observations, 655, 673 White Queen, 448 Wine prices, 341 Winsorizing, 222, 223, 471, 818 Y akima Valley, 726 Yale School of Medicine, 186 ... HUMAN LEARNING: From Learning Curves to Learning Organizations Armstrong, J S./ PRINCIPLES OF FORECASTING: A Handbook for Researchers and Practitioners Balsamo, S., Personé, V., Onvural, R./ ANALYSIS... on various topics, and did citation studies Jean Newland and Cynthia Kardon were able to track down data and papers from sketchy information The Lippincott Library also has a service that enables... Judgmental Forecasting 495 Hal R Arkes, Department of Psychology, Ohio State University 517 16 Gaining Acceptance Scenarios and Acceptance of Forecasts 519 W Larry Gregory and Anne Duran, Department