Table Of ContentBig Players and the Economic Theory of Expectations
Also by Roger Koppl
SUBJECTIVISM AND ECONOMIC ANALYSIS: Essays in Memory of
Ludwig M. Lachmann(edited with Gary Mongiori)
Big Players and the
Economic Theory of
Expectations
Roger Koppl
Professor of Economics and Finance
Fairleigh Dickinson University
Madison, New Jersey
© Roger Koppl2002
Softcover reprint of the hardcover 1st edition 2002 978-0-333-67826-8
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Library of Congress Cataloging-in-Publication Data
Koppl, Roger, 1957–
Big players and the economic theory of expectations / Roger Koppl.
p. cm.
Includes bibliographical references and index.
1. Rational expectations (Economic theory) 2. Economic forecasting.
3. Business cycles. 4. Money market–Forecasting. 5. Stock price forecasting.
6. Economics–History–20th century. I. Title.
HB3722 .K66 2002
339’.01–dc21 2001060270
10 9 8 7 6 5 4 3 2 1
11 10 09 08 07 06 05 04 03 02
To Maria, who brings joy
Contents
List of Figures ix
List of Tables x
Preface xi
Acknowledgments xiv
Part 1 Introduction
1 Introduction and Summary 3
Expectations and economic theory 3
The role of expectations in economics as a social science 8
Outline of the theory 11
Part II Methodology
2 The Misesian Context 25
Introduction 25
The methodology of Ludwig von Mises 26
Schutz and Hayek 38
3 Schutz 40
4 Hayek 56
Part III Theory
5 Language Games and Economic Theory 73
Introduction 73
Language games 74
The economy as a network of language games 83
Coordination does not require common rules 87
Natural selection of language games 92
6 Expectations 96
Introduction 96
Entrepreneurship 96
Cognitive expectations 106
Acognitive expectations 113
Hayekian expectations 115
Correlating cognitive and acognitive expectations 118
vii
viii Contents
7 Big Players 120
The general theory of Big Players 120
Modeling Big Players 123
Big Players in asset markets 128
Modeling asset prices in the presence of Big Players 131
Herding and contra-herding as error duration 135
Part IV Applications
8 Big Players in Czarist Russia 141
The policies of Bunge and Vyshnegradsky 141
The ruble and GARCH 147
R/S analysis 154
9 The Angular Distribution of Asset Returns in
Delay Space 164
The compass rose 166
Hypothesis testing with theta histograms 168
A simple simulation of X-skewing 174
Big Players induce X-skewing 177
10 Herding in Money Demand 184
Money demand and the theory of Big Players 184
The Federal Reserve as a Big Player 187
Discussion and conclusion 193
Coda
How this book relates to the emerging new
orthodoxy in economics 195
Some suggestions for further work 199
Appendixes 204
1 What is Verstehen? 204
2 What did Wittgenstein really mean? 208
3 An elementary set-theoretic model of language games 210
4 Notes on reflexivity 212
Notes 218
Bibliography 223
Name Index 237
Subject Index 241
List of Figures
5.1 A simple coordination game 90
5.2 Payoff structure 91
8.1 Daily exchange rate levels 150
8.2 Price-change data 152
8.3 R/S Analysis 159
9.1 Daily returns for Weyerhauser stock on the NYSE,
December 1963–December 1993 165
9.2 Crack and Ledoit’s (1986) “compass rose” graph for
IBM daily stock returns, January 1, 1980–October 8, 1992 167
9.3 “Theta histogram” of Weyerhauser stock returns 169
9.4 Bootstrapped theta histogram created using the data
used to construct Figure 9.3 170
9.5 “Compass rose” graph for a simulated series 176
9.6 Empirical theta histogram, Bunge period 177
9.7 Empirical theta histogram, Vyshnegradsky period 178
9.8 Bootstrapped theta histogram, Bunge period 179
9.9 Bootstrapped theta histogram, Vyshnegradsky period 180
9.10 Compass rose in absolute values, Bunge period 181
9.11 Compass rose in absolute values, Vyshnegradsky period 182
A4.1 Morgenstern’s two-person zero-sum game 213
ix
List of Tables
8.1 ADF and Phillips–Perron tests 150
8.2 General statistical information for ruble data 151
8.3 Ftest for equal unconditional variancies between
Bunge and Vyshnegradsky periods 152
8.4 Parameter estimates from a traditional GARCH(1,1)
model with no dummy interaction variables 153
8.5 Parameter estimates from GARCH(1,1) model with
dummy interaction variables on the conditional
squared error term and conditional variance term 154
8.6 Estimated values of the Hurst coefficient 162
9.1 χ2hypothesis test for Weyerhauser data 173
9.2 Bernoulli hypothesis test for Weyerhauser data 174
9.3 Results of SAS fit of simulation data to a GARCH(1,1)
model 176
9.4 Bernoulli hypothesis tests regardingθ/π= –0.5(±0.005) 181
9.5 Bernoulli hypothesis tests for ruble data,
θ/π= ±0.25(±0.005) and ±0.75 (±0.005) 182
9.6 χ2hypothesis tests for ruble data 183
10.1 Hurst coefficients 193
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