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Computational Methods and Models in Macroeconomics

Authors :
Surro, Christopher John
Fajgelbaum, Pablo1
Weill, Pierre-Olivier
Surro, Christopher John
Surro, Christopher John
Fajgelbaum, Pablo1
Weill, Pierre-Olivier
Surro, Christopher John
Publication Year :
2020

Abstract

New developments in computational power and methods have introduced a wide variety of new tools for economic analysis. This dissertation explores whether these new advances can help us understand and analyze macroeconomic phenomena.Chapter 1 shows how recently developed clustering methods can be helpful in identifying consumer groups based on a history of purchasing behavior. While traditional methods of customer segmentation rely on observable characteristics of consumers or the products they buy, the methods I use in this chapter rely instead on identifying groups of consumers who buy a similar set of products. If consumers who buy similar products are likely to have similar preferences, then clustering groups of consumers who buy similar products can potentially uncover groups of consumers who share unobservable characteristics that drive their preference structures without explicitly specifying those preference structures. Using simulations of a discrete choice logit demand system, I show that a density peaks clustering method can effectively uncover consumers with different preferences, using a series of examples to show how different assumptions impact the effectiveness of the clustering algorithm.In chapter 2, I apply the methods of chapter 1 to attempt to improve measurements of the cost of living. In particular, I show that methods for measuring cost of living that rely on aggregate CES representative agents will often overstate the gains from new product varieties when groups of consumers have different tastes for products. Since the purchase data of consumers shows that there is substantial heterogeneity in the sets of products consumers buy, estimating inflation using a representative agent approach could produce biased estimates. However, the methods from chapter 1 can help to mitigate the bias from heterogeneity by grouping similar consumers based on their purchase history. I apply the method to a large panel of consumers and show that clustering consu

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1287390633
Document Type :
Electronic Resource