FORMULATING HYPOTHESES IN THE PROCESS OF DEVELOPING A RESEARCH DESIGN, SELECTING A SYSTEM OF VARIABLES , AND JUSTIFYING AN ECONOMETRIC IDENTIFICATION STRATEGY
Keywords:
research design; hypothesis; variable selection; operationalization; identification; endogeneity; causal inference; instrumental variables; DiD; RD; panel data.Abstract
This paper discusses the core steps of developing an empirical research design: hypothesis formulation , variable system selection , and justification of an econometric identification strategy . The research workflow is treated as a coherent chain —“ problem–theory–hypothesis–measurement–identification–estimation–validation”—and the paper systematizes the main threats to internal validity (endogeneity, reverse causality, omitted variable bias, selection, measurement error) and practical ways to mitigate them. We show how to convert theoretical statements into testable and falsifiable hypotheses, operationalize constructs, and choose indicators and proxy variables. The paper then reviews major identification strategies—randomization, natural experiments, instrumental variables, difference-in-differences, regression discontinuity, panel fixed effects, matching, and synthetic control—emphasizing their assumptions and diagnostic checks. In the "Results" section, an illustrative scenario of evaluating an education program is presented with explicit hypotheses, a variable matrix, tables, and conceptual figures (a design-selection flowchart and a causal diagram). The paper concludes with actionable guidance on transparency, robustness, and replicability in econometric research.
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