Econometric and finance analytics for economics, finance, management and policy research: models that respect the structure of your data and identification strategies that make causal claims credible.
Econometric work usually starts with the shape of your data. Many units observed over a few years point to panel methods such as fixed effects, random effects or dynamic GMM; one or a few series observed over many periods point to time series methods such as ARIMA, VAR, VECM or GARCH. If your question is explicitly causal — did a policy, reform or decision change an outcome? — the design matters more than the estimator, and causal inference methods such as instrumental variables, difference-in-differences, regression discontinuity or matching become central.
Some questions have their own specialized toolkit. Market reactions to announcements are measured with event studies; the performance of banks, hospitals or schools against best practice is measured with DEA or stochastic frontier analysis; and firm valuation or ratio variables for research are built with transparent financial models. All econometric deliverables include diagnostic tests, robust or clustered standard errors where appropriate, and code that reproduces every table.
Support specifying and estimating static and dynamic panel models, choosing between fixed and random effects, getting standard errors right and reporting diagnostics reviewers look for.
Support testing stationarity, choosing and estimating univariate and multivariate time series models, modeling volatility and reporting diagnostics correctly.
Support choosing an identification strategy that fits your data, estimating it correctly and reporting the diagnostics that make causal claims credible.
Support designing and running financial event studies, from defining event dates and windows to estimating abnormal returns and choosing test statistics robust to real-world data problems.
Support measuring technical, scale and cost efficiency with DEA or SFA, choosing inputs and outputs, tracking productivity change over time and explaining efficiency differences.
Support building transparent financial models for academic and research purposes — discounted cash flow, cost of capital, multiples and ratio analysis — with assumptions you can defend.
Tell us what you’re working on and where you’re stuck. We’ll recommend the right kind of help and send a quote.