Handbook of Economic Expectations

Handbook of Economic Expectations discusses the state-of-the-art in the collection, study and use of expectations data in economics, including the modelling of expectations formation and updating, as well as open questions and directions for future research. The book spans a broad range of fields, approaches and applications using data on subjective expectations that allows us to make progress on fundamental questions around the formation and updating of expectations by economic agents and their information sets. The information included will help us study heterogeneity and potential biases in expectations and analyze impacts on behavior and decision-making under uncertainty. Front Cover Handbook of Economic Expectations Copyright Contents Contributors Preface Part 1 Expectation elicitation 1 Household surveys and probabilistic questions 1.1 History and motivation for measuring household economic expectations 1.1.1 Why economists started to elicit qualitative subjective expectations 1.1.2 Why economists started to elicit subjective probabilities 1.1.3 Widespread adoption of subjective probability elicitation 1.1.4 Main takeaway 1.2 Methodological considerations when developing surveys of expectations 1.2.1 Survey pretesting 1.2.1.1 Randomized survey-based experiments 1.2.1.2 Cognitive interviews with follow-up surveys 1.2.1.3 Example 1.2.2 Panel vs. cross-sectional surveys 1.2.3 Main takeaway 1.3 Insights and methodological advances 1.3.1 Point forecasts versus probabilistic expectations 1.3.2 Question wording and framing of point forecasts 1.3.3 Introductory framing for probabilistic expectations questions 1.3.4 Rounding, bunching and ambiguity 1.3.5 Use of visual response scales 1.3.6 Elicitation of probability distributions 1.3.6.1 Elicitation of probability density functions 1.3.6.2 Elicitation of cumulative distribution functions 1.3.7 Fitting distributions and measuring uncertainty 1.3.8 Density-based forecasts versus point forecasts 1.3.9 Individual differences in expectations and uncertainty 1.3.10 Use of expectations data in economic analysis 1.3.11 Main takeaway 1.4 Concluding remarks References 2 Firm surveys 2.1 Introduction 2.2 Quantification of qualitative survey answers 2.3 Historic business expectation surveys in the U.S. 2.3.1 Surveys of the general business outlook 2.3.2 Business investment surveys 2.3.3 Findings of the early literature 2.4 Ongoing business expectation surveys in the U.S. 2.4.1 Manufacturing Business Outlook Survey 2.4.2 CFO Survey 2.4.3 Survey of Business Uncertainty 2.4.4 Business Inflation Expectations Survey 2.4.5 ManpowerGroup Employment Outlook Survey 2.4.6 Management and Organizational Practices Survey 2.4.7 Small Business Economic Trends Survey 2.5 Firm surveys in Europe 2.5.1 Germany: ifo surveys 2.5.1.1 The history of the ifo surveys 2.5.1.2 The ifo Konjunkturtest today 2.5.1.3 The ifo Investment Survey today 2.5.1.4 Quantitative answer scales 2.5.2 France: INSEE survey 2.5.3 Italy: from ISCO to Istat 2.5.4 UK: CBI Industrial Trends Survey 2.5.5 European harmonization of business surveys 2.6 Firm surveys in Japan: the TANKAN 2.7 International cooperation 2.7.1 Centre for International Research on Economic Tendency Surveys 2.7.2 OECD and UN 2.8 Conclusion References 3 Surveys of professionals, 3.1 Surveys of professional forecasters 3.1.1 Interest and attractiveness of eliciting professional forecasters' expectations 3.1.2 Surveys of professional forecasters 3.1.3 Nature of survey expectations and concepts 3.2 Point and density forecasts: data features, measures, and properties 3.2.1 Background 3.2.2 Point forecasts 3.2.3 Density forecasts 3.2.4 A closer look at disagreement and uncertainty 3.3 Evaluation of forecaster performance 3.3.1 Data revisions 3.3.2 Rationality/efficiency of point forecasts 3.3.3 Scoring rules 3.3.4 Using the probability integral transform to assess density forecast coverage 3.3.5 Balanced vs. unbalanced panels 3.3.6 Are some forecasters better than others? 3.3.7 Professionals versus models (and other sources of forecasts) 3.4 Consistency of point and density forecasts 3.4.1 Calculating bounds on the central moments of histograms 3.4.2 Nature of loss functions – symmetric vs. asymmetric 3.4.3 Rounding of point and density forecasts 3.5 Conclusion 3.A Appendix table: surveys of professional forecasters References 4 Survey experiments on economic expectations 4.1 Introduction 4.2 Why (field) experiments on expectations? 4.2.1 Understanding decision-making 4.2.2 Understanding expectation formation 4.3 Information provision experiments 4.3.1 Design basics 4.3.2 Expectations and behavior 4.3.3 Examples 4.4 Methodological issues 4.4.1 Within-subject or between-subject design? 4.4.2 Eliciting perceptions about the provided information 4.4.3 Eliciting higher-order moments 4.4.4 Information content and presentation 4.4.5 Where and how to run these surveys? 4.5 Extensions and alternative approaches 4.5.1 Moving beyond exogenously provided information 4.5.2 Alternatives to information provision experiments 4.6 Directions for future work References Part 2 Expectations as data 5 What do the data tell us about inflation expectations? 5.1 Introduction 5.2 Data sources 5.2.1 Michigan Survey of Consumers 5.2.2 New York Fed Survey of Consumer Expectations 5.2.3 European Commission Consumer Survey 5.2.4 Ad-hoc surveys 5.2.5 Comparing elicited inflation expectations across surveys 5.3 Stylized facts 5.3.1 Time-series facts 5.3.2 Cross-sectional facts 5.3.3 Term-structure facts 5.3.4 Households versus professional forecasters 5.4 Determinants of inflation expectations 5.4.1 Exposure to price signals 5.4.2 The role of the lifetime experiences and neuroplasticity 5.4.3 The role of cognition and human frictions 5.4.4 The role of the media and communication 5.5 Inflation expectations and economic choices 5.5.1 Intertemporal consumption and saving choices 5.5.2 Financing current consumption: mortgages and borrowing 5.5.3 Investment and savings decisions 5.6 Conclusion and outlook References 6 Housing market expectations 6.1 Measuring expectations 6.1.1 Surveys about housing market expectations 6.1.2 Nonsurvey measures of housing market expectations 6.2 Determinants of expectations and expectations heterogeneity 6.2.1 Extrapolation 6.2.2 Personal experiences 6.2.3 Social interactions 6.2.4 Ownership status 6.2.5 Determinants of higher moments of belief distribution 6.3 The effects of expectations on individual housing market behavior 6.3.1 Homeownership decisions 6.3.2 Mortgage choice 6.4 House price expectations and aggregate economic outcomes 6.4.1 The housing boom of the late 1970s 6.4.2 The housing boom of the early 2000s 6.5 Conclusion References 7 Expectations in education 7.1 Introduction 7.2 Survey expectations about monetary outcomes of schooling 7.2.1 Are elicited earnings expectations meaningful? 7.2.2 Patterns and heterogeneity of earnings expectations 7.2.3 Perceived monetary returns to schooling 7.2.4 Perceived earnings risk 7.2.5 Beliefs about population earnings 7.2.6 Are elicited earnings expectations rational? 7.2.7 Other labor market outcomes 7.2.8 Monetary costs 7.3 Survey expectations about nonmonetary outcomes of schooling 7.3.1 Are elicited probabilities meaningful? Rational? 7.3.2 Academic performance, study effort, and ability 7.3.3 ``Enjoying'' education and other nonmonetary outcomes 7.3.4 Nonmonetary outcomes in the labor and marriage markets 7.3.5 Attainment and dropout 7.3.6 Education plans 7.3.7 Parental approval and parental beliefs 7.4 Analysis of schooling decisions with survey expectations 7.4.1 Monetary returns and risks 7.4.2 Monetary costs 7.4.3 Nonmonetary factors: ability, taste, and beyond 7.4.4 Parents and family decision-making 7.4.5 Peer effects 7.4.6 Centralized school choice 7.5 Analysis of expectation formation and learning 7.5.1 Earnings 7.5.2 Academic performance 7.6 Conclusion References 8 Mortality and health expectations 8.1 Introduction 8.2 Methods 8.2.1 Measurement error, focal answers, and rounding 8.2.2 Biases in small and large probabilities 8.2.3 Jointly modeling objective and subjective expectations 8.3 Survival expectations 8.3.1 Properties 8.3.2 Flatness bias in survival expectations 8.3.3 Determinants of survival expectations 8.3.3.1 Health and health behaviors 8.3.3.2 The vital status of parents and other relatives 8.3.3.3 Expectations of minority groups 8.3.4 The effect of survival expectations on economic and health outcomes 8.4 Health expectations 8.4.1 Moving to a nursing home 8.4.2 Expectations about medical expenditures 8.4.3 Substance use 8.4.4 Expectations about cognitive decline and dementia 8.4.5 Other health expectations 8.5 Conclusion 8.A Estimation of a rounding model of survival expectations 8.B Additional tables References 9 Expectations in development economics 9.1 Introduction 9.2 Measuring probabilistic expectations in surveys in developing countries 9.2.1 Percent chance format 9.2.2 Physical objects as visual aid 9.2.3 Interactive touchscreen 9.2.4 Proportion of people (like you) 9.2.5 Phone interviews 9.2.6 Eliciting subjective distribution of beliefs 9.2.7 Point expectations 9.2.8 Piloting, interviewers' training and other considerations 9.3 Patterns of answers 9.3.1 Respect of basic properties of probabilities 9.3.2 Expectations and respondents' characteristics 9.3.3 Accuracy of elicited expectations 9.3.4 Heterogeneity in beliefs 9.4 Applications 9.4.1 Health 9.4.2 Education 9.4.3 Parental investment in children 9.4.4 Migration, income, and the labor market 9.4.5 Agricultural inputs and outputs 9.4.6 Conflicts and natural disasters 9.4.7 Information experiments 9.5 Datasets 9.6 Conclusions References 10 Retirement expectations 10.1 Introduction 10.2 Theoretical framework 10.3 Measuring retirement age expectations 10.4 Eliciting a planned or expected retirement age 10.5 Subjective retirement probability 10.6 Predicting retirement: subjective probability predictions and predictive analytics 10.7 Subjective work probability predictions among non-full-time worker respondents 10.8 Research on the quality of retirement age expectations 10.9 Potential uses of retirement age expectations 10.10 Research with retirement age expectations as the left-hand-side variable 10.11 Research with retirement age expectations as the right-hand-side variable 10.12 Using conditional subjective work probability predictions to estimate effects on retirement 10.12.1 Conditioning versus subjectivity in general 10.13 Conclusions References 11 The macroeconomic expectations of firms 11.1 Introduction 11.2 Surveys of firms' macroeconomic expectations 11.3 Properties of firms' macroeconomic expectations 11.3.1 Mean inflation forecasts 11.3.2 Disagreement about inflation 11.3.3 Short and long-run expectations 11.3.4 Inattention to inflation and monetary policy 11.3.5 The joint formation of beliefs 11.4 Do firms' macroeconomic expectations matter? 11.4.1 Firms' inflation expectations and the expectations-augmented Phillips curve 11.4.2 Randomized control trials 11.5 Conclusion References 12 Firm expectations about production and prices: facts, determinants, and effects 12.1 Introduction 12.2 Surveying firm expectations 12.2.1 Background 12.2.2 Example: the ifo Business Expectations Panel 12.3 Stylized facts 12.4 Expectation formation 12.4.1 Determinants of expectations 12.4.1.1 Firm expectations 12.4.1.2 Firm uncertainty 12.4.2 Over- and underreaction to news 12.5 Firm expectations and firm decisions 12.5.1 The effect of firm expectations 12.5.2 Firm-level uncertainty and firm decisions 12.6 Conclusion References 13 Expectations of financial market participants 13.1 Introduction 13.2 Distinctions across surveys 13.3 Examples of surveys of financial market participants 13.3.1 Survey of Primary Dealers and Survey of Market Participants 13.3.2 Blue Chip Survey 13.3.2.1 Blue Chip Economic Indicators 13.3.2.2 Blue Chip Financial Forecasts 13.3.3 Consensus Economics 13.3.4 Surveys administered in other jurisdictions 13.4 Some advantages and uses of surveys 13.4.1 Risk premiums 13.4.2 Types of questions that are best answered by surveys 13.4.3 Surveys as model inputs 13.5 Drawbacks of surveys 13.5.1 Distributional inconsistencies 13.5.2 Sample 13.5.3 Rationality and rigidities 13.5.4 Forecast/revision smoothing 13.6 Conclusion References Part 3 Expectations and economic theory 14 Measuring market expectations 14.1 Introduction 14.2 Market expectations and the price of risk 14.2.1 Testable implications 14.2.2 Some asset pricing basics 14.2.3 Modeling risk premia 14.2.3.1 Return regressions 14.2.3.2 Gaussian affine term structure models 14.2.3.3 An integrative view 14.3 Extracting measures of market expectations from asset prices 14.3.1 A general approach to identifying market expectations 14.3.2 An illustration based on the oil market 14.4 Existing empirical evidence for selected markets 14.4.1 Monetary policy expectations 14.4.2 Inflation expectations 14.5 Economic applications of market-based expectation measures 14.5.1 Evaluation of economic models 14.5.2 Deriving shock measures 14.5.3 Policy analysis 14.5.4 Implications for out-of-sample forecasts 14.6 Conclusions References 15 Inference on probabilistic surveys in macroeconomics with an application to the evolution of uncertainty in the survey of professional forecasters during the COVID pandemic 15.1 Introduction 15.2 Inference on probabilistic surveys 15.2.1 The inference problem 15.2.2 Current approaches 15.2.3 A Bayesian nonparametric alternative A parametric probabilistic model A Bayesian nonparametric approach Some asymptotic properties Finite sample properties and caveats A comparison with existing approaches 15.3 Challenges in measuring uncertainty 15.4 Heterogeneity in density forecasts 15.5 Pooling and consensus forecasts 15.6 The evolution of professional forecasters' density forecasts during the COVID pandemic 15.6.1 GDP growth 15.6.2 Inflation 15.7 Conclusions References 16 Expectations data in asset pricing 16.1 Introduction 16.2 A general asset pricing framework 16.2.1 Rational expectations 16.2.2 Subjective beliefs in a single-period setting 16.2.2.1 Homogeneous subjective beliefs 16.2.2.2 Heterogeneous subjective beliefs 16.2.3 Subjective beliefs in a multiperiod setting 16.2.3.1 Common knowledge 16.2.3.2 Lack of common knowledge 16.3 Empirical dynamics of investor expectations 16.3.1 Return and price expectations 16.3.2 Cash flow expectations 16.3.3 Interest rate expectations 16.3.4 Subjective risk perceptions 16.4 Mapping survey expectations into asset pricing models 16.4.1 Are survey expectations risk adjusted? 16.4.2 Measurement error and cognitive uncertainty 16.4.3 Heterogeneity and beliefs aggregation 16.5 Models of expectations formation 16.5.1 Learning about payouts 16.5.2 Learning about prices 16.5.3 Learning biases 16.5.4 Heterogeneity 16.6 Future research directions 16.A Data sources for investor expectations References 17 The term structure of expectations 17.1 Introduction 17.2 Joint behavior of short- and long-term forecasts 17.2.1 Motivation: a simple model of long-term drift 17.2.1.1 Modeling a drift in the long-run mean 17.2.2 A model to fit the term structure of expectations 17.2.2.1 Baseline multivariate model 17.2.2.2 Data overview 17.2.3 Mapping the model to survey forecasts 17.2.4 Discussion 17.2.5 Results 17.2.5.1 Model fit Beyond consensus expectations 17.2.5.2 Evolution of the term structure of expectations 17.3 Expectations and the term structure of interest rates 17.3.1 Decomposing the term structure of interest rates 17.4 The term structure of expectations in structural models 17.4.1 A general structural model 17.4.2 The New Keynesian model 17.4.3 Implications for monetary and fiscal policy 17.5 Conclusions and further directions References 18 Expectational data in DSGE models 18.1 Introduction 18.2 Expectational data in rational expectations DSGE models 18.2.1 Do DSGE models generate expectations that fit observed data? 18.2.2 Survey expectations to evaluate alternative frictions 18.2.3 Survey expectations & news shocks 18.2.4 Survey expectations & sunspots 18.2.5 Misspecification of expectations 18.3 Expectational data and deviations from rational expectations 18.3.1 Adaptive learning 18.3.2 Survey expectations and sentiment 18.3.3 First moment vs. second moment shocks 18.4 Heterogeneity in survey expectations 18.5 Issues and limitations 18.6 Conclusions and future directions References 19 Expectations and incomplete markets 19.1 Introduction 19.2 The general setup 19.3 News shocks 19.3.1 Analytical insights 19.3.1.1 Productivity news 19.3.1.2 Interest rate news 19.3.2 Quantitative analysis 19.3.2.1 Technology news 19.3.2.2 Monetary policy news 19.4 Channels underlying savings behavior 19.5 Noise shocks 19.5.1 Model 19.5.2 Estimating the parameters 19.5.2.1 Estimating the impact of noise shocks 19.5.2.2 Estimation of structural parameters 19.5.2.3 Calibration and estimation results 19.5.3 Implications 19.6 Sunspots 19.7 Conclusions 19.A Solutions for news shocks 19.B Computing the Jacobians References 20 Dampening general equilibrium: incomplete information and bounded rationality 20.1 Introduction 20.2 Framework 20.2.1 PE and GE in a nutshell 20.2.2 Micro-foundation: a simplified New-Keynesian model 20.2.3 Full Information Rational Expectations (FIRE) 20.2.4 Beyond FIRE 20.3 Incomplete information 20.3.1 Removing common knowledge by adding idiosyncratic noise 20.3.2 Main lesson: GE attenuation 20.3.3 From rational expectations to higher-order beliefs (HOBs) 20.4 Bounded rationality 20.4.1 Level-k Thinking 20.4.2 Parenthesis: back to higher-order beliefs 20.4.3 Reflective equilibrium and cognitive hierarchy 20.5 Additional variants and dynamic extensions 20.5.1 A bridge: heterogeneous priors, or shallow reasoning 20.5.2 Dynamics I: learning 20.5.3 Dynamics II: forward-looking behavior 20.5.4 Cognitive discounting 20.6 Applications 20.6.1 Forward guidance at the zero lower bound 20.6.2 Fiscal policy 20.6.3 Other applications 20.7 Discussion: similarities, differences, and empirical backdrop 20.7.1 Key differences 20.7.2 Empirical backdrop: underreaction in average vs individual forecasts 20.8 Conclusion References 21 Expectations data in structural microeconomic models 21.1 Introduction 21.2 A model 21.2.1 Specification 21.2.2 Types of expectations data 21.2.3 Identification and the role of expectations data 21.2.4 Estimation 21.2.4.1 Estimation methods Maximum likelihood estimation Method of simulated moments (MSM) Indirect inference Non-full solution methods of estimation 21.2.5 Issues particular to structural estimation with expectations data 21.2.5.1 Constructing a model counterpart to expectations data 21.2.5.2 Use of data on choice expectations in maximum likelihood estimation 21.2.5.3 Focal point responses to probabilistic expectation questions 21.3 Literature I: expectations over the states of nature 21.3.1 Allowing for subjective expectations 21.3.2 Modeling subjective expectations 21.4 Literature II: data on choice expectations 21.4.1 Unconditional choice expectations 21.4.2 Conditional choice expectations 21.4.2.1 Stated discrete choice data 21.4.2.2 Probabilistic conditional choice expectations data 21.4.3 Strategic survey questions 21.5 Conclusion References 22 Expectations data, labor market, and job search 22.1 Introduction 22.2 Measurement 22.2.1 Data sources 22.2.2 Descriptive statistics 22.2.3 Predictive power of elicited beliefs 22.2.4 Measurement issues 22.3 Illustrative framework 22.4 Beliefs and behavior 22.4.1 Structural models with expectations data 22.4.2 Identification and empirical evidence 22.4.2.1 The perceived return to job search Exogenous variation Direct elicitations 22.4.2.2 Estimating the effect of beliefs on behavior 22.5 Beliefs and biases 22.5.1 Identification 22.5.2 Empirical evidence 22.5.3 Determinants of biased beliefs 22.5.4 Policy implications 22.6 Beliefs and heterogeneity 22.6.1 Identification 22.6.2 Applications 22.7 Conclusion References Part 4 Theories of expectations 23 Bayesian learning 23.1 Introduction 23.2 Mathematical preliminaries 23.2.1 Bayesian updating 23.2.2 The Kalman filter 23.2.3 Learning the distribution of the state 23.2.3.1 Learning the mean 23.2.3.2 Learning the precision 23.2.3.3 Nonparametric learning 23.3 Using signals to understand economic activity 23.3.1 Signal-extraction problems 23.3.1.1 A tracking problem 23.3.1.2 Permanent vs. transitory shocks Keeping uncertainty alive 23.3.1.3 Aggregate vs. idiosyncratic shocks 23.3.2 Using signals in strategic settings 23.3.2.1 A beauty contest with exogenous signals Beliefs and equilibrium Heterogeneous incomplete information Optimal use of information Responsiveness to shocks 23.3.2.2 Strategic complementarity and aggregate inertia 23.3.2.3 Strategic substitutability and aggregate volatility The role of preferences 23.4 Information choice and learning technologies 23.4.1 Sticky information 23.4.1.1 A beauty contest with infrequent information updating Information dynamics Equilibrium and optimal choices 23.4.1.2 Applications of sticky information 23.4.2 Rational inattention 23.4.2.1 Measuring information: entropy and mutual information 23.4.2.2 A tracking problem with noisy information acquisition Tracking multiple states 23.4.2.3 Applications of rational inattention 23.4.2.4 Linear cost of signal precision 23.4.3 Other learning technologies 23.4.4 Information choice as a source of inequality 23.4.5 Learning what others know 23.4.6 Information choice in strategic settings 23.5 Theories of the data economy 23.5.1 Experimentation 23.5.2 Data and growth 23.5.2.1 Data as knowledge 23.5.2.2 Data as information 23.5.3 Data and economic fluctuations 23.6 Conclusion References 24 Ambiguity 24.1 Introduction 24.2 Static choice under uncertainty 24.2.1 Preferences 24.2.2 Risk vs ambiguity: similarities and differences 24.2.3 Savings and portfolio choice 24.3 Dynamic choice and equilibrium 24.3.1 Asset pricing 24.3.2 Business cycle models with uncertainty shocks 24.4 Quantifying ambiguity using survey data 24.5 Aggregate applications 24.6 Heterogeneity and micro-to-macro applications 24.6.1 Heterogeneous perceptions of uncertainty 24.6.2 Inaction and inertia 24.6.3 Ambiguous information and asymmetric decision rules 24.7 Policy implications 24.7.1 Ambiguous policy objectives 24.7.2 Optimal policy with ambiguity-averse agents 24.8 Concluding remarks References 25 Epidemiological expectations 25.1 Introduction 25.2 Background and motivation 25.2.1 Expectational heterogeneity 25.2.2 Epidemiological models 25.2.3 Expectational tribes 25.3 What insights can the epidemiological framework offer? 25.3.1 What is an epidemiological framework? 25.3.1.1 Adapting the disease metaphor to expectations 25.3.2 One example 25.4 Literature 25.4.1 Diffusion of technology 25.4.2 Financial markets 25.4.3 Macroeconomic expectations 25.4.3.1 Sticky expectations 25.4.3.2 Sentiment and the business cycle 25.4.3.3 Learning of macroeconomic equilibria 25.4.4 Nonstructural empirical evidence 25.4.4.1 Directly measured social networks 25.4.4.2 Papers using proxies for social connections 25.4.4.3 Public media 25.4.4.4 Epidemiology and ``narrative economics'' 25.4.4.5 Social communication in animals 25.4.5 Contagion 25.4.6 Noneconomic applications 25.4.7 Future directions 25.4.7.1 New tests of competing models 25.4.7.2 New kinds of survey data 25.4.7.3 New and big data 25.4.8 Literature summation 25.5 Conclusion References Part 5 Open issues 26 Looking ahead to research enhancing measurement of expectations 26.1 Introduction 26.2 Rounding reported probabilities 26.2.1 Inferring rounding from response patterns 26.2.2 Possible reasons for rounding 26.3 Imprecise probabilities 26.3.1 Background 26.3.2 Imprecise probabilities of dementia 26.3.3 Looking ahead 26.4 Studying expectations formation 26.4.1 Microeconomic analysis of expectation formation 26.4.2 Studying expectations formation to inform macro policy analysis 26.4.3 The potential contribution of expectations measurement 26.5 Confounding beliefs and preferences 26.5.1 Evidence in research measuring probabilistic expectations 26.6 Conclusion References Index Back Cover