Statistical Modeling

Discussion

Andreea L. Erciulescu, Westat

Federal Committee on Statistical Methodology (FCSM)

Research and Policy Conference 2022

October 26, 2022

The views presented in this paper are those of the author and do not represent the views of any Government Agency/Department or Westat. 1
Session Papers

I John Angle: Applying a Model of Income Distribution, Adopted as Econophysics,

to Estimating Non-Compliance with the Individual Federal Income Tax

I mathematical model

I Noé Nava: A Bayesian Model of the Probabilistic Role of Weather Variations on

Crop Yield Potential

I statistical model; Bayes inference

I Daniel Lin: Methods and Assumptions of the CPS ASEC Tax Model and Imputing

2020 Stimulus Payments

I black-box model

I Anil Rupasingha: Place-Based Tax Incentives and Minority Employment: Evidence

from New Market Tax Credit (NMTC) Program

I statistical model; matching

I Cesar Montalvo: Skill Profiles and Portability of Credentials for the Technical

Workforce

I statistical model; frequentist inference

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Modeling Income Distribution; Angle, J.

Contributions

I Mathematical model known as ‘inequality process’ (IP)

I Estimation of ‘tax gap’ (i.e., noncompliance with the federal income tax)

Discussion points

I The model is presented as ‘simple’ and accounting for ‘a wide scope of patterns in

statistics of income and wealth’ - fifteen categories of empirical income and wealth

phenomena

I Needs: explicit model specification, along with assumptions; details on estimation

approaches

I The model is validated using data from the Current Population Survey from

multiple years - distribution of income conditional on education, for age group 25+

year old

I Needs: other variables to condition on; finer age groups; other socio-demographic

groups

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Modeling Weather Variations on Crop Yield; Nava, N.

Contributions

I Bayesian formulation of the Ricardian approach that integrates agronomic evidence

on the yield-weather interaction

I Crop yield projections from Midwestern and Eastern U.S. counties for 2022, 2027,

and 2032

Discussion points

I The model is presented as hierarchical Bayes model

I Needs: definition of the Ψ function in the data level; definition of the Ψ function in

the link level; prior distribution for σ in the link level; hyper-priors for M and Σ in

the prior for β; level for Ψ

I The model is applied to crop yield, using three weather variables

I Needs: selection of weather variables; details on model fit and validation;

assumptions on weather variables 5 and 10 years into the future

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Modeling Tax and Imputing Stimulus Payments; Lin, D.

Contributions

I Tax model

I Complement to the Current Population Survey Annual Social and Economic

Supplement (CPS ASEC), including the first and second Economic Impact

Payments for the 2021 CPS ASEC

Discussion points

I The model assumptions, steps, inputs, and outputs are described in some detail

I Needs: inclusion of error from statistical matching step; description of methods used

I The model is validated by comparing aggregated model estimates against IRS

benchmarks

I Needs: alignment of underlying population and reference time in the two sources

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Modeling Tax Incentives and Minority Employment; Rupasingha, A.

Contributions

I Matching estimation approach

I Effectiveness of the New Market Tax Credit (NMTC) program on minority and

rural populations

Discussion points

I The methods described are supported by the literature; three matching methods

are compared

I Needs: assumptions; inclusion of error from statistical matching

I The variables used for matching are enumerated, along with their reference time

I Needs: possible confounding variables

6
Modeling Technical Workforce; Montalvo, C.

Contributions

I Regression - weighted least squares estimation, and network analysis

I Describe how technical skills and experience shape the salary returns for

skill-intensive occupations and identify nondegree credentials that allow skilled

technical workers to redefine career pathways in manufacturing

Discussion points

I The regression equation for salary return is defined at the individual level and

includes three covariates

I Needs: data sources; reference time

I The results are presented by major occupation group, and other groups

I Needs: different model specification for each analysis; multicolinearity tests for

models with multiple covariates

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Thank you!

AndreeaErciulescu@westat.com

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