Macroeconomic Survey Expectations -  Michael P. Clements

Macroeconomic Survey Expectations (eBook)

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2018 | 1. Auflage
XVI, 205 Seiten
Palgrave Macmillan (Verlag)
978-3-319-97223-7 (ISBN)
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Why should we be interested in macroeconomic survey expectations? This important book offers an in-depth treatment of this question from a point of view not covered in existing works on time-series econometrics and forecasting. Clements presents the nature of survey data, addresses some of the difficulties posed by the way in which survey expectations are elicited and considers the evaluation of point predictions and probability distributions. He outlines how, from a behavioural perspective, surveys offer insight into how economic agents form their expectations.


Michael Clements is Professor of Econometrics at the ICMA Centre, Henley Business School, University of Reading, UK. He is Series Editor of Palgrave Texts in Econometrics and Palgrave Advanced Texts in Econometrics, and has published extensively on time series econometrics, modelling and forecasting. 

Michael Clements is Professor of Econometrics at the ICMA Centre, Henley Business School, University of Reading, UK. He is Series Editor of Palgrave Texts in Econometrics and Palgrave Advanced Texts in Econometrics, and has published extensively on time series econometrics, modelling and forecasting. 

Contents 8
List of Figures 12
List of Tables 13
1 Introduction 15
References 18
2 The Nature of Survey Expectations 21
2.1 Survey Expectations Data Sources 23
2.2 Forecast Horizons and Targets 24
2.3 Individual and Aggregate Expectations 26
2.4 Means and Combining Information 30
2.5 Which Survey Data? 34
References 35
3 Working with the Forecast Data 38
3.1 Calculating Moments from the Histograms 39
3.2 Fitting Continuous Distributions to the Histograms 42
3.3 Empirical Illustration: The US SPF Inflation Histograms 45
References 47
4 Assessing the Point Predictions 49
4.1 Assessing the Accuracy of Point Forecasts 50
4.1.1 Survey Expectations 50
4.1.2 Model Forecasts 52
4.2 Tests of Forecast Optimality or Rationality 54
4.3 Tests in the Presence of Instabilities 58
4.4 A Panel of Forecasters 60
4.4.1 Testing Consensus Forecasts When Agents Are Rational 60
4.4.2 Testing Consensus Forecasts When Agents Are Irrational 62
4.4.3 Pooling or Individual Regressions? 63
4.5 Testing for Optimality Under Asymmetric Loss 65
4.6 A Brief Review of the Empirical Evidence 69
4.6.1 Testing for Optimality 69
4.6.2 Testing Allowing for Instabilities 69
4.6.3 Testing Using a Panel of Forecasters 70
4.6.4 Rationality and Asymmetric Loss 70
Appendix: Derivation of the Optimal Predictor Under LINEX Loss 71
References 72
5 Assessing the Accuracy of the Probability Distributions 76
5.1 Density Evaluation 77
5.2 Density Comparison 79
5.3 Evaluating Regions of the Densities 82
5.4 Alternative Density Scoring Rules 83
5.5 Benchmark Density Forecasts 84
5.6 Empirical Results 89
5.6.1 The Aggregate Distributions 89
5.6.2 The Individual Distributions 96
5.6.3 Robustness of the Results to the Assumptions 101
Evaluating Regions of the Densities 101
Alternative Density Scoring Rules 103
5.7 Conclusion 104
References 104
6 Consistency of the Point Forecasts and Probability Distributions 108
6.1 Calculating Bounds on the Central Moments of Histograms 110
6.2 Inconsistency and Asymmetric Loss 113
6.3 Rounding and the Reporting of Probability Forecasts 119
6.3.1 The SPF Probabilities of Decline and Rounding 120
6.3.2 The Consistency of the Decline Probability Forecasts and the Probability Distributions 126
6.3.3 The Consistency of the Decline Probability Forecasts and the Probability Distributions Allowing That the Decline Probability Forecasts Have Been Rounded 128
6.3.4 Rounding of Probability Forecasts and the Histogram Forecasts 129
6.4 Conclusions 131
References 132
7 Macroeconomic Uncertainty: Surveys Versus Models? 134
7.1 Measuring Survey Uncertainty 136
7.1.1 Empirical Results 139
7.1.2 Are Survey Forecasters Targeting True Values? 142
7.2 Models for Inflation and Output Growth Uncertainty 144
7.2.1 MIDAS Specification 145
MIDAS EAU 148
MIDAS RMSE 148
7.3 Empirical Results 149
7.4 Conclusions 151
Appendix: Computation of EAU with Estimated MIDAS Models 152
References 153
8 Behavioural Models of Expectations Formation 155
8.1 Evidence of Disagreement Among Forecasters 157
8.2 Adaptive Learning 159
8.3 Models of the Expectations Formation Process: Sticky Information 162
8.4 Models of the Expectations Formation Process: Noisy Information 165
8.5 Extensions to Basic IR Models 167
8.5.1 Heterogeneous Beliefs About Long-Run Outcomes 167
8.5.2 Heterogeneous Precision of Signals 168
8.5.3 Asymmetric Loss Functions 169
8.6 Empirical Evidence 170
8.7 Individual Forecasters 173
References 179
9 Expectations Shocks and the Macroeconomy 183
9.1 Short-Run and Long-Run Identification Schemes in Structural VARs 184
9.2 Identification by Maximizing the Contribution of a Shock to the Forecast-Error Variance Decomposition 188
9.3 Expectations and Non-fundamental Shocks 191
9.4 Expectations Shocks and Macroeconomic Fluctuations: Empirical Evidence 192
9.5 Uncertainty Shocks 194
References 195
10 Postscript 197
References 199
Index 200

Erscheint lt. Verlag 31.12.2018
Verlagsort Cham
Sprache englisch
Themenwelt Wirtschaft Allgemeines / Lexika
Wirtschaft Betriebswirtschaft / Management Unternehmensführung / Management
Wirtschaft Volkswirtschaftslehre
Schlagworte Behavioural Models of Expectations Formation • Density evaluation • economic forecasting • Expectations shocks • Forecast data • Macroeconometrics • Macroeconomic survey expectations • Macroeconomic uncertainty • Point Forecasts • probability distributions • Time Series Econometrics
ISBN-10 3-319-97223-5 / 3319972235
ISBN-13 978-3-319-97223-7 / 9783319972237
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