PhD Researchers & Doctoral Candidates
Analysis for a dissertation chapter or first paper—done rigorously, and explained so you can defend and extend it yourself.
Research Methods
Econometrics & Quantitative Research
The right estimator is only half the work. We specify a model your question can defend, run the diagnostics that decide whether it holds, and report results the way economics, finance, and management reviewers expect to read them—with every assumption tested and every robustness check in place.
Cross-section · time-series · panel
R · Stata · Python · EViews
Full diagnostics & robustness
Reproducible, journal-ready
Sample output
IRF
equilibrium
Overview
Estimation you can stand behind in review
Most econometric results fail peer review not because the wrong package was used, but because the identifying assumptions were never made explicit—or never tested. A pooled regression run on data that is really a panel, a cointegrating relationship assumed rather than established, an instrument that fails the exclusion restriction: these are the objections that come back from referees, and they are avoidable.
We start from the structure of your data and the claim you want to make, then choose the estimator that fits both. For a short panel with a lagged dependent variable, that may mean System GMM rather than fixed effects. For non-stationary macro series of mixed integration order, an ARDL bounds-testing approach rather than a naive VAR. The method follows the question, and every choice is documented so a reviewer can follow the reasoning as easily as you can.
Whatever the model, the deliverable is the same: clean estimation output, the full battery of diagnostic and specification tests, robustness and sensitivity checks, and a methods section written in the register your target journal expects—all reproducible from versioned code you keep.
Who We Work With
Built for the people doing empirical work
If you recognize yourself here, this is the right desk to write to.
Faculty & Academic Researchers
Extra analytical capacity for a paper, a grant, or a revision under deadline—without adding a permanent hire.
Research Institutes & Policy Teams
Applied econometric work for evaluation and evidence programs that need to withstand external scrutiny.
Economics, Finance, Business & Management Researchers
Field-aware modeling from people who already read the literature and journals you're writing into.
Journals & Publishers
Independent methodological and statistical review of submissions where econometric rigor is in question.
Teams with Longitudinal, Panel & Macro Data
Organizations working with data observed over time or across units that needs the right dynamic estimator.
Capabilities
The full econometric toolkit, matched to your data
Organized by data structure, not by fashion. If your question needs a method not listed here, ask—this is the core, not the boundary.
Cross-sectional & foundational panel
The workhorses done rigorously—correct standard errors, tested assumptions, defensible identification.
- OLS
- Fixed effects
- Random effects
- Instrumental variables (IV)
- Two-stage least squares (2SLS)
- Panel regression
- Robust regression
Dynamics, cointegration & regimes
For series observed over time—stationarity handled properly, long-run relationships established, not assumed.
- ARDL
- NARDL
- QARDL
- VAR
- SVAR
- VECM
- Granger causality
- Toda–Yamamoto
- FMOLS
- DOLS
- Cointegration
- Structural breaks
- State-space models
- Markov-switching
- TVP-VAR
- Bayesian VAR
Dynamic, heterogeneous & non-linear panels
For panels where dynamics, cross-sectional dependence, or heterogeneous slopes make simpler estimators unsafe.
- Dynamic panel models
- Difference GMM
- System GMM
- PMG-ARDL
- Panel ARDL
- CS-ARDL
- CCEMG
- AMG
- DCCE
- Panel quantile regression
- MM-QR
- Threshold models
- PSTR
How the Analysis Works
Six steps from question to reproducible result
A transparent, best-practice sequence—each step chosen for your data and documented in the final deliverable. Nothing is a black box.
Steps are adapted to your study: cross-section vs. panel vs. time-series, static vs. dynamic, with or without endogeneity concerns. We confirm the analysis plan with you before estimation begins.
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1
Understand
Research question, hypotheses, dataset, variables, and identification strategy—clarified before any modeling choice is made.
Inputs: research question · data · identification
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2
Specify
Select the appropriate model based on the data-generating process and research objective, and justify it against the alternatives.
Methods: OLS · FE/RE · IV/2SLS · ARDL · VAR/VECM · GMM
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3
Diagnose
Test stationarity, heteroskedasticity, autocorrelation, cross-sectional dependence, endogeneity, stability, and specification.
Tests: unit-root · Hausman · Breusch–Pagan · Arellano–Bond · RESET
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4
Estimate
Run the primary model using appropriate estimators and inference procedures, with correct standard errors for the data structure.
Inference: robust · clustered · HAC · bootstrap
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5
Stress-test
Conduct robustness, sensitivity, alternative specifications, and competing-model checks to establish how far the result holds.
Checks: alternative specs · subsamples · placebo · sensitivity
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6
Report
Produce interpretable tables, figures, methodology, and results text, delivered with reproducible, versioned code you keep.
Output: tables · figures · methods text · R/Stata/Python/EViews code
Rigor by default
The checks that decide whether a result survives
Estimation is the easy part. What separates a publishable result from a rejected one is everything around it—and it is standard on every econometrics engagement.
Included on every project
- Unit-root and stationarity testing
- Heteroskedasticity, autocorrelation & cross-sectional dependence checks
- Endogeneity assessment and instrument validity
- Specification, stability, and robustness testing
- Reproducible, versioned code you keep
What You Receive
Every engagement, delivered in full
Not a black-box result and a number, but a complete, documented package you can submit, defend, and reproduce.
- Clean, documented datasets and analysis files
- Model specification and estimation strategy
- Complete diagnostic and specification tests
- Robustness and sensitivity analysis
- Publication-ready tables and figures
- Interpretation of coefficients and marginal effects
- Reproducible R, Stata, Python, or EViews code
- Journal-ready methodology and results sections
- Technical responses to methodological reviewer comments, where required
Where this fits
Part of a larger arc
Econometric analysis sits in the middle of the MAS Research Model—strongest when the design ahead of it and the reporting after it are handled with the same care.
Research Design & Planning
Identification strategy, power, and specification decided before estimation begins.
Explore methodsStatistical & Methodological Audit
An independent check of assumptions, specification, and reproducibility before submission.
Explore auditPublication & Research Support
Methods and results reporting, journal selection, and reviewer-response support.
Explore support
FAQ
Common questions
Answers to what most researchers and project leads ask before we begin an econometrics engagement.
Yes. Our Statistical & Methodological Audit is an independent review of your specification, assumptions, endogeneity handling, and robustness—the objections a referee is most likely to raise—before you submit. We can review an analysis whether or not we ran it.
By the structure of your data and the source of potential bias. A Hausman test speaks to fixed versus random effects; a dynamic relationship with a lagged dependent variable and a short time dimension points toward System GMM. We make the reasoning explicit and test the assumptions each estimator relies on, rather than defaulting to a house method.
Yes—most engagements begin with data you already have. We assess its structure and quality, document any cleaning or construction we do, and keep every transformation traceable so the final analysis is fully reproducible from the raw file you provide.
R, Stata, Python, and EViews, chosen to match the method and your own environment so you can re-run and extend the work. You receive versioned, commented code alongside the results and methods text.
Yes. Reproduction and verification is a distinct service: we take the data and, where available, the original code, and independently confirm whether the reported results hold—flagging any specification, coding, or inference issues we find. It's increasingly requested for revisions and for internal quality control.
We provide a rigorous, accurate description of the methods, specifications, diagnostics, and results—written to journal standards for you to review, adapt, and make your own. The work and the authorship remain yours; we make the reporting precise.
Yes. We help you respond to methodological referee comments—running the additional tests, alternative specifications, or robustness checks a reviewer asks for, and drafting clear technical responses that address the concern directly and defensibly.
Yes—and it's the best point to involve us. Given your research question, data structure, and identification strategy, we can advise on the right estimator and specification before you commit time to the analysis. Getting this right at the design stage prevents the costly rework that comes from choosing a method the data can't support.
Related Expertise
Often paired with econometrics
Have a dataset and a deadline?
Tell us the question and the data you're working with—we'll tell you the estimator, the diagnostics, and what it takes to make it hold.