Our Core Services

We offer a full suite of econometric and data-driven services designed for both research and strategic applications:

Panel Data Econometrics
We model cross-sectional and time-series data using Fixed and Random Effects estimations, Driscoll–Kraay corrections, FGLS, and System GMM for robust inference and dynamic analysis.

Financial and Policy Modelling
We evaluate firm value, capital structure, dividend policy, ESG, and AI disclosure effects using advanced regression and interaction models.

Causal Inference and Policy Evaluation
We employ Difference-in-Differences (DID), Instrumental Variables (IV), and Propensity Score Matching (PSM) to establish causal relationships and assess programme or policy impacts.

Time Series Analysis and Forecasting
We apply ARIMA, VAR, and GARCH frameworks to forecast financial and macroeconomic trends for informed planning and investment decisions.

Structural Equation Modelling (SEM)
We design mediation and moderation models using AMOS, SmartPLS, and Stata to explore complex cause-and-effect dynamics.

Diagnostics and Robustness Testing
We conduct heteroskedasticity, multicollinearity, serial correlation, and endogeneity checks to ensure validity, consistency, and reliability in all analyses.

Who We Work With

We collaborate with a wide range of clients who require empirically sound analysis and actionable insights:

• Researchers and PhD Candidates: We assist in model specification, estimation, interpretation, and robustness testing to produce publication-grade results.

• Financial Institutions and Corporates: We provide analytical support for performance measurement, valuation, and risk assessment.

• Government and Development Agencies: We offer evidence-based policy evaluation and impact assessment models that inform reform and investment decisions.

Our clients trust us to bring analytical precision, transparency, and contextual understanding to their data-driven projects.

Methodology and Tools

We combine academic rigour with modern analytical software.
Our modelling is carried out using Stata, R, EViews, and Python, ensuring reproducibility and clarity at every stage. Each project is accompanied by full documentation, diagnostics, and clearly interpretable outputs designed for reports, publications, and board presentations.

Selected Works & Empirical Analyses

Our empirical work demonstrates our ability to bridge theory with applied econometrics across diverse research themes and sectors.

Firm Value and Dividend Policy

1. Panel regression analysis of leverage, profitability, and firm size using Fixed and Random Effects estimations with Driscoll–Kraay standard errors.

2. Tobit and Quantile regression assessing how profitability shapes dividend behaviour among Nigerian manufacturing firms.

3. Logistic regression estimating dividend payment likelihoods among consumer goods firms on the Nigeria Exchange.

4. Random-Effects models examining how ownership concentration and institutional shareholding influence earnings quality and firm value.

5. Quantile regressions assessing how debt and profitability jointly affect firm valuation.

AI, ESG, and Sustainability Studies

1. System GMM estimation analysing the effect of AI disclosure on Return on Assets and Tobin’s Q for Deposit Money Banks.

2. Panel models testing the governance-enhancing role of AI adoption in Nigerian banks.

3. Principal Component Analysis (PCA) on environmental, social, and governance indices with Driscoll–Kraay regression validation.

4. Mediation models exploring how financing structure channels sustainability effects.

5. Moderation analysis showing firm size’s impact on the AI disclosure–performance link during the pandemic.

Governance, Risk, and Policy Impact Studies

1. SEM models (SmartPLS) analysing how audit committee expertise reduces earnings manipulation.

2. Panel models linking governance, risk management, and capital adequacy to credit rating outcomes.

3. Panel models using the Altman Z-Score to link the Cash Conversion Cycle to firm stability.

4. DID models estimating the Treasury Single Account’s (TSA) causal impact on liquidity and performance.

5. Logistic regression based on the Beneish M-Score examining ownership, profitability, and audit quality effects.

Collectively, these works illustrate our expertise in applying econometric models — System GMM, Tobit, Quantile, SEM, and DID — to generate statistically rigorous, policy-relevant insights that inform practice and academic discourse alike.

Our Value Proposition

We believe that econometric analysis should not only explain relationships but guide strategy.
Our team combines the analytical precision of academia with the strategic perspective of finance professionals, ensuring each project delivers results that are both statistically defensible and practically useful.

“Empirical evidence should not only explain — it should guide decisions.”

We adhere to the highest standards of confidentiality, transparency, and ethical research, ensuring that all client data and findings remain secure and professionally managed.

Book a Consultation

Are you ready to turn data into meaningful insight?
Whether you’re developing a thesis, evaluating a policy, or analysing corporate performance, we are ready to assist.

email us at info@sasng.net or simon.akpadaka@gmail.com

Ethics and Confidentiality

We maintain the highest standards of professional conduct.
All data shared with us are treated with strict confidentiality and are used solely for analytical purposes. Our commitment to ethical econometric practice ensures reproducibility, transparency, and integrity in every engagement.