Inside Into
Kauppakorkeakoulun julkaisuportaali
Aalto-yliopiston kauppakorkeakoulun gradujen tiedot nyt Aaltodocissa: Aaltodoc-julkaisuarkisto
Kauppakorkeakoulu | Taloustieteen laitos | Kansantaloustiede | 2016
Tutkielman numero: 14370
Machine learning in applied econometrics: Deriving personal income drivers with randomized decision forests
Tekijä: Ikonen, Henri
Otsikko: Machine learning in applied econometrics: Deriving personal income drivers with randomized decision forests
Vuosi: 2016  Kieli: eng
Laitos: Taloustieteen laitos
Aine: Kansantaloustiede
Asiasanat: taloustieteet; economic science; ekonometria; econometrics; tietämyksenhallinta; knowledge management; oppiminen; learning; varallisuus; wealth; kehitys; development; Yhdysvallat; United States
Sivumäärä: 66
» hse_ethesis_14370.pdf pdf  koko: 2 MB (1374443)
Avainsanat: econometrics; machine learning; decision trees; causality; big data; random forests; income; american community survey
In this paper I explore a modern field of research in applied econometrics: machine learning and the estimation of synthetic treatment effects.

Data generation is currently on an exponential growth path: smart phones, social media and networks of interconnected devices are generating information at an unprecedented pace. The size, structure and velocity of these information streams vary to a great extent. The field of econometrics is also evolving: classic econometric models can lead to biased results with big data and will not scale up to modern data sets.

I propose the well- performing Random Forests algorithm for use in econometrics. To adjust this method for causal analysis, recent theory on causal decision trees is explored. The proposed framework is then tested by estimating personal income drivers for the top 1% in U.S. population. The data used is the American Community Survey 5- year sample consisting of approximately 20 million rows.

It appears that high income is in fact driven by four core factors: education, experience, working hours and gender. To rank these predictors, a synthetic treatment effect simulation is run. I find that investing in education after a master's degree has a significant positive effect in the likelihood of high income. Additionally, it appears that the negative gender income effect for females can be undone with a combination of work experience and exceptional work- ethic.
Verkkojulkaisut ovat tekijänoikeuden alaista aineistoa. Teokset ovat vapaasti luettavissa ja tulostettavissa henkilökohtaista käyttöä varten. Aineiston käyttö kaupallisiin tarkoituksiin on kielletty.