Macroeconomic Survey Expectations
by Michael P. Clements (Author)

About the author
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.

About this book
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.

Brief contents
1 Introduction
    References
2 The Nature of Survey Expectations
    2.1 Survey Expectations Data Sources
    2.2 Forecast Horizons and Targets
    2.3 Individual and Aggregate Expectations
    2.4 Means and Combining Information
    2.5 Which Survey Data?
    References
3 Working with the Forecast Data
    3.1 Calculating Moments from the Histograms
    3.2 Fitting Continuous Distributions to the Histograms
    3.3 Empirical Illustration: The US SPF Inflation Histograms
    References
4 Assessing the Point Predictions
    4.1 Assessing the Accuracy of Point Forecasts
        4.1.2 Model Forecasts
    4.2 Tests of Forecast Optimality or Rationality
    4.3 Tests in the Presence of Instabilities
    4.4 A Panel of Forecasters
        4.4.2 Testing Consensus Forecasts When Agents Are Irrational
        4.4.3 Pooling or Individual Regressions?
    4.5 Testing for Optimality Under Asymmetric Loss
    4.6 A Brief Review of the Empirical Evidence
        4.6.4 Rationality and Asymmetric Loss
    Appendix: Derivation of the Optimal Predictor Under LINEX Loss
    References
5 Assessing the Accuracy of the Probability Distributions
    5.1 Density Evaluation
    5.2 Density Comparison
    5.3 Evaluating Regions of the Densities
    5.4 Alternative Density Scoring Rules
    5.5 Benchmark Density Forecasts
    5.6 Empirical Results
        5.6.2 The Individual Distributions
        5.6.3 Robustness of the Results to the Assumptions
    Alternative Density Scoring Rules
    References
6 Consistency of the Point Forecasts and Probability Distributions
    6.1 Calculating Bounds on the Central Moments of Histograms
    6.2 Inconsistency and Asymmetric Loss
    6.3 Rounding and the Reporting of Probability Forecasts
        6.3.1 The SPF Probabilities of Decline and Rounding
        6.3.2 The Consistency of the Decline Probability Forecasts and the Probability Distributions
        6.3.3 The Consistency of the Decline Probability Forecasts and the Probability Distributions Allowing That the Decline Probability Forecasts Have Been Rounded
        6.3.4 Rounding of Probability Forecasts and the Histogram Forecasts
    6.4 Conclusions
    References
7 Macroeconomic Uncertainty: Surveys Versus Models?
    7.1 Measuring Survey Uncertainty
        7.1.1 Empirical Results
        7.1.2 Are Survey Forecasters Targeting True Values?
    7.2 Models for Inflation and Output Growth Uncertainty
        7.2.1 MIDAS Specification
            MIDAS RMSE
    7.3 Empirical Results
    7.4 Conclusions
    Appendix: Computation of EAU with Estimated MIDAS Models
    References
8 Behavioural Models of Expectations Formation
    8.1 Evidence of Disagreement Among Forecasters
    8.2 Adaptive Learning
    8.3 Models of the Expectations Formation Process: Sticky Information
    8.4 Models of the Expectations Formation Process: Noisy Information
    8.5 Extensions to Basic IR Models
        8.5.2 Heterogeneous Precision of Signals
        8.5.3 Asymmetric Loss Functions
    8.6 Empirical Evidence
    8.7 Individual Forecasters
    References
9 Expectations Shocks and the Macroeconomy
    9.1 Short-Run and Long-Run Identification Schemes in Structural VARs
    9.2 Identification by Maximizing the Contribution of a Shock to the Forecast-Error Variance Decomposition
    9.3 Expectations and Non-fundamental Shocks
    9.4 Expectations Shocks and Macroeconomic Fluctuations: Empirical Evidence
    9.5 Uncertainty Shocks
    References
10 Postscript
    References
Index

Pages: 196 pages
Publisher: Palgrave Macmillan; 1st ed. 2019 edition (December 31, 2018)
Language: English
ASIN: B07FQ8NZXC

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