Ba Chu, Carleton University

Research

Bayesian inference DCA Hidden Markov processes Iterated filtering large-scale computational methods Latent variable models likelihood-free methods Monte-Carlo methods

Latest papers

Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth.
Computational economics · 2022 · first author
Time-specific average estimation of dynamic panel regressions.
Studies in nonlinear dynamics and econometrics · 2022 · first author

All 2 publications

Latest funding

Simulation-based Methods for Large Dynamic Latent Variable Models with Unobserved Heterogeneity
NSERC · 2020 · Principal investigator
$108,000
Structural Breaks in Panel Data Models with Grouped Patterns of Heterogeneity
SSHRC · 2016 · Principal investigator
$65,353
Semiparametric estimation of nonlinear models by k-nearest neighbors with dependent data
SSHRC · 2011 · Principal investigator
$33,600

All 3 grants and awards

2 publications.

01

Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth.

Chu B, Qureshi S

Computational economics2022First author

DOI: 10.1007/s10614-022-10312-z PMID: 36157276

02

Time-specific average estimation of dynamic panel regressions.

Chu B

Studies in nonlinear dynamics and econometrics2022First author

DOI: 10.1515/snde-2019-0084 PMID: 36338259

Simulation-based Methods for Large Dynamic Latent Variable Models with Unobserved Heterogeneity

Principal investigatorNSERC2020Discovery Grants Program - Individual

Principal investigators: Chu, Ba

Keywords: latent variable models; hidden markov processes; panel data; social networks; iterated filtering; likelihood-free methods; monte-carlo methods; bayesian inference; dca; large-scale computational methods

$108,000

Structural Breaks in Panel Data Models with Grouped Patterns of Heterogeneity

Principal investigatorSSHRC2016Insight Development Grant

Principal investigators: Chu, Ba

Keywords: (1) Panel Data; (2) Unobserved heterogeneity; (3) Spatial econometrics; (4) Group classification; (5) Economic Growth; (6) Exchange rate regimes; (7) Non-convex programming; (8) Structural instability; (9) Grouped pattern of heterogeneity; (10) Big data

$65,353

Semiparametric estimation of nonlinear models by k-nearest neighbors with dependent data

Principal investigatorSSHRC2011Standard Research Grants

Principal investigators: Chu, Ba Manh B.

Keywords: econometrics; microdata; statistics

$33,600

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

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Applying for

Paper to mentionMention their NSERC grant from 2020

Subject: Prospective PhD student: Bayesian inference Dear Professor Chu, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. I read your paper "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth." (2022), and it connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your NSERC-funded project "Simulation-based Methods for Large Dynamic Latent Variable Models with Unobserved Heterogeneity" (2020), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]