scholarly journals Estimating intergenerational income mobility on sub-optimal data: a machine learning approach

2021 ◽  
Author(s):  
Francesco Bloise ◽  
Paolo Brunori ◽  
Patrizio Piraino
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Income Mobility ◽  
Objective Criterion ◽  
Parental Income ◽  
Long Run ◽  
Intergenerational Income Mobility ◽  
Out Of Sample ◽  
Supervised Learning Algorithms ◽  
Out Of Sample Prediction

AbstractMuch of the global evidence on intergenerational income mobility is based on sub-optimal data. In particular, two-stage techniques are widely used to impute parental incomes for analyses of lower-income countries and for estimating long-run trends across multiple generations and historical periods. We propose applying machine learning methods to improve the reliability and comparability of such estimates. Supervised learning algorithms minimize the out-of-sample prediction error in the parental income imputation and provide an objective criterion for choosing across different specifications of the first-stage equation. We use our approach on data from the United States and South Africa to show that under common conditions it can limit the bias generally associated to mobility estimates based on imputed parental income.

Microstructure in the Machine Age

2020 ◽  
Author(s):  
David Easley ◽  
Marcos López de Prado ◽  
Maureen O’Hara ◽  
Zhibai Zhang
Keyword(s):  
Machine Learning ◽  
Market Efficiency ◽  
Predictive Power ◽  
Explanatory Power ◽  
Futures Contracts ◽  
Machine Age ◽  
Price Process ◽  
Out Of Sample ◽  
Asset Classes ◽  
Out Of Sample Prediction

Abstract Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. We demonstrate how machine learning can be applied to microstructural research. We find that microstructure measures continue to provide insights into the price process in current complex markets. Some microstructure features with high explanatory power exhibit low predictive power, while others with less explanatory power have more predictive power. We find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about market efficiency. We also show how microstructure measures can have important cross-asset effects. Our results are derived using 87 liquid futures contracts across all asset classes.

scholarly journals Capability Deprivation, and the Intergenerational Transmission of Social Disadvantages—Empirical Evidence from Selected Countries

2018 ◽  
Vol 7 (12) ◽  
pp. 253
Author(s):  
Veronika V. Eberharter
Keyword(s):  
United States ◽  
Income Inequality ◽  
Intergenerational Transmission ◽  
Driving Forces ◽  
Economic Status ◽  
The United States ◽  
Income Mobility ◽  
Birth Cohorts ◽  
Intergenerational Income Mobility ◽  
Capability Deprivation

Based on longitudinal data from the Cross-National Equivalent File 1980–2016 (CNEF 1980–2016) the paper analyzes the extent of income inequality and capability deprivation and the driving forces of the intergenerational transmission of social and economic status of two birth cohorts in Germany, and the United States. In both the countries the empirical results show increasing inequality of the real equivalent household income, and younger cohorts experience a higher persistence of social and economic status. In the United States income inequality is more expressed than in Germany, which is in accordance with lower intergenerational income mobility. The contribution of individual and family background characteristics and capability deprivation indicators to intergenerational income mobility is more pronounced in the United States than in Germany. The significant impact of capability deprivation in childhood on the intergenerational transmission of economic chances emphasizes the importance of economic and social policy designated to guarantee the equality of opportunity.

Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach

2020 ◽  
Vol 53 (4) ◽  
pp. 513-554
Author(s):  
Daniel V. Fauser ◽  
Andreas Gruener
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Risk Prediction ◽  
Prediction Accuracy ◽  
Explanatory Power ◽  
Machine Learning Algorithms ◽  
Out Of Sample ◽  
Corporate Social Irresponsibility ◽  
Out Of Sample Prediction ◽  
Corporate Social

This paper examines the prediction accuracy of various machine learning (ML) algorithms for firm credit risk. It marks the first attempt to leverage data on corporate social irresponsibility (CSI) to better predict credit risk in an ML context. Even though the literature on default and credit risk is vast, the potential explanatory power of CSI for firm credit risk prediction remains unexplored. Previous research has shown that CSI may jeopardize firm survival and thus potentially comes into play in predicting credit risk. We find that prediction accuracy varies considerably between algorithms, with advanced machine learning algorithms (e. g. random forests) outperforming traditional ones (e. g. linear regression). Random forest regression achieves an out-of-sample prediction accuracy of 89.75% for adjusted R2 due to the ability of capturing non-linearity and complex interaction effects in the data. We further show that including information on CSI in firm credit risk prediction does not consistently increase prediction accuracy. One possible interpretation of this result is that CSI does not (yet) seem to be systematically reflected in credit ratings, despite prior literature indicating that CSI increases credit risk. Our study contributes to improving firm credit risk predictions using a machine learning design and to exploring how CSI is reflected in credit risk ratings.

Sign in / Sign up

Export Citation Format

Share Document