Richard J. Cebula · American Business Review 2025 · Cointegrating regression time-series analysis · n=?

A Cointegrating Regression Analysis of the Impacts of Greater Economic Freedom and Perceived Risk from a Larger National Debt-to-GDP Ratio on the Real Cost of Borrowing for Corporations in the U.S.

Cited 1 times in the scientific literature.

Level 3 - non-randomized controlled study

Longitudinal econometric time-series analysis (by design analogy, non-clinical)

OpenAlex W4416583265 · doi:10.37625/abr.28.2.481-495 · record verified 2026-08-31

What was done

The author conducted an empirical cointegrating regression analysis using U.S. macroeconomic and financial market data. The study evaluated how variations in the level of economic freedom, the national debt-to-GDP ratio, and federal budget deficits as a percentage of GDP relate to the real borrowing costs of firms, operationalized as the ex post real interest rate yield on Moody's Baa-rated corporate bonds.

What was found

The abstract reports no numerical values, confidence intervals, sample sizes, or test statistics. Qualitatively, the analysis found empirical support for two primary hypotheses: greater economic freedom was associated with increased demand for Baa-rated bonds and lower real yields, whereas a higher national debt-to-GDP ratio was associated with reduced demand and higher real yields. Additionally, federal budget deficits were reported to exert upward pressure on corporate borrowing yields through direct market competition for funds and cumulative debt accumulation.

Why it matters

This study links fiscal policy metrics and institutional measures of economic freedom to private-sector capital costs. It suggests that fiscal deficits and expanding debt burdens can elevate private borrowing costs through risk perception and crowding-out effects.

Limits

The abstract provides no sample size, time horizon, numerical effect sizes, or statistical coefficients. Macroeconomic cointegration models are vulnerable to specification error, omitted variable bias, structural breaks, and challenges in establishing direct causal directionality.

Cited by