In this thesis the predictive power of individual survey participants on expected macroeconomic values is analysed. The research results of Crump (2015) are investigated and the elasticity of intertemporal substitution (EIS) is estimated according to their model. The core estimate results in an EIS coefficient of 0.839, which is marginally higher than previous results in literature.
Considering the predictive power, demographic variables, sensitivity components, and time dependent fixed effects, the range is approximately 0.6 to 0.9, depending on the specification, where the estimated EIS values range. In particular the subdivision in different levels of education allows interesting and validated implications. The interpretation of the EIS offers a helpful contribution for many market actors and especially economic policymakers. For example, the effectiveness and targeting accuracy of an economic stimulus plan in the current corona crisis could be increased with the help of the empirical findings of the EIS.
Especially in times of crisis, such as the current corona pandemic, we are repeatedly reminded that in our economic world all market participants and institutions are deeply interconnected due to dependencies and expectations. Not only central banks, which in recent years have been accused of having a reduced capacity to act as a result of low interest rate policies, but also investors, entrepreneurs and governments are interested in gaining a deeper understanding of the impact of expectations on real value development.
Table of contents
Abstract
List of tables and figures
Table of Abbreviations and Symbols
1. Introduction
2. Theoretical Background of estimating the EIS
2.1 Micro- and Macrodata
2.2 Predictive Power
2.3 Evidence of the Elasticity of Intertemporal Substitution (EIS)
3. Data
4. Empirical results
4.1 Testing the predictive power
4.2 Estimating the EIS
4.3 Tests for Excess Sensitivity
5) Conclusion
References
Tables
Table 1 Baseline Specification from (Crump, et al., 2015, p. 33)
Table 2 Qualitative results of respondent’s predictions
Table 3 Quantitative results of respondent’s predictions
Table 4 Regression of the QTP by demographic-groups
Table 5 Baseline Specification own approach
Table 6 Baseline Specification for different demographic groups
Table 7 Excess Income Sensitivity
Table 8 Excess Stock Price Sensitivity
Table 9 Excess Earnings Sensitivity
Table 10 Long Term Future Inflation Sensitivity
Table 11 Time Dependent Fixed Effects
Appendix
Survey Questions
Conditioning Variables and Specifications
Control Variables: Demos (Categorical)
Control Variables: Test Predictions
Control Variables: Excess Sensitivity
Figure 1 Mean value of QTP4INFL separated by education level & race
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