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
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
Research Objectives and Core Themes
This paper aims to estimate the elasticity of intertemporal substitution (EIS) using microdata from the Survey of Consumer Expectations (SCE), while investigating whether the estimation power of different sociodemographic groups varies to better understand the impact of expectations on macroeconomic outcomes.
- Estimation of the EIS coefficient based on individual survey expectations.
- Evaluation of the predictive power of various sociodemographic groups regarding macroeconomic indicators.
- Application of microdata to avoid auxiliary assumptions required by traditional GMM methods.
- Analysis of excess sensitivity to income, stock prices, and inflation expectations.
- Impact of demographic characteristics, such as education and risk affinity, on estimation accuracy.
Excerpt from the Book
1. Introduction
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. From this point of view, deeper questions may arise: “Why are inflation expectations relevant for individual economic decisions?” The literature explains that inflation expectations, as part of real interest rates, can influence investment and consumption decisions (Coibion, et al., 2020, p. 2).
The pioneering work of (Hall, 1978), (Hall, 1988) and (Hansen & Singleton, 1982), (Hansen & Singleton, 1983) analyzed this relationship for the Consumption Euler Equation: Etct+1 - ct = σ-1(it - Etπt+1), (1) where Etct+1 - ct is the expected consumption growth from t to t+1 ; it - Etπt+1 is the perceived real interest rate from t to t+1 and σ-1 is the inverse of elasticity term of the intertemporal substitution (EIS). Knowing the latter term, we could potentially classify the effect of perceived real interest rates on expected consumption growth. This implication allows us to see a causal coherence. If the real interest rate increases, current consumption may fall because of the higher return on savings; but current consumption may also rise if the household decides to consume more immediately because they feel richer. This leads us to our research questions: “What is the value of σ?” and “What is the effect of perceived real interest rates on expected consumption growth?”
Summary of Chapters
1. Introduction: Outlines the significance of expectations in macroeconomic modeling and defines the research questions regarding the EIS and the predictive power of different demographic groups.
2. Theoretical Background of estimating the EIS: Discusses the transition from macro to microdata, reviews existing literature on predictive power and expectation formation, and provides an overview of empirical findings on the EIS.
3. Data: Introduces the Survey of Consumer Expectations (SCE) as the primary data source, explaining its structure and the categorization of questions used for the empirical analysis.
4. Empirical results: Details the quantitative assessment of predictive power, estimates the EIS using the baseline model, and tests for excess sensitivity across various independent variables.
5) Conclusion: Synthesizes the core findings, confirms the significance of the EIS between 0.8 and 0.9, and discusses the implications of these findings for economic policy.
Keywords
Elasticity of Intertemporal Substitution, EIS, Survey of Consumer Expectations, SCE, Microdata, Inflation Expectations, Consumption Euler Equation, Predictive Power, Excess Sensitivity, Macroeconomic Modeling, Sociodemographic Groups, Forecast Accuracy, Monetary Policy, Interest Rates.
Frequently Asked Questions
What is the fundamental focus of this paper?
The paper focuses on estimating the elasticity of intertemporal substitution (EIS) using individual-level microdata from the Survey of Consumer Expectations (SCE).
What are the central thematic fields covered?
The research covers expectation formation, the predictive accuracy of households regarding macroeconomic indicators, and the behavioral response of consumption to interest rate and income changes.
What is the primary research goal?
The primary goal is to determine the value of the EIS coefficient and to examine whether the ability to make accurate forecasts differs across various sociodemographic groups.
Which scientific methodology is applied?
The study employs OLS (Ordinary Least Squares) and IV (Instrumental Variable) regressions based on the Consumption Euler Equation, utilizing a unique microdata-based approach to avoid common auxiliary assumptions.
What topics are discussed in the main section?
The main section covers the theoretical foundations of the EIS, a description of the SCE dataset, the empirical testing of predictive power using specific equations, and an analysis of excess sensitivity to factors like income and stock prices.
Which keywords best characterize this work?
Key terms include Elasticity of Intertemporal Substitution (EIS), Survey of Consumer Expectations (SCE), microdata, and excess sensitivity.
How does educational level impact the results according to the paper?
The study finds that higher educated individuals generally exhibit better predictive power and that significant differences in EIS estimation exist depending on the level of education.
What conclusion does the author draw regarding the COVID-19 pandemic?
The author suggests that empirical findings regarding the EIS can significantly increase the effectiveness and targeting accuracy of economic stimulus programs designed to support the economy during crises.
- Citar trabajo
- Julian Fischer (Autor), 2020, Estimation of Elasticity of Intertemporal Substitution. Empirical Monetary Economics, Múnich, GRIN Verlag, https://www.grin.com/document/961676
