From Predictive Econometrics to Prescriptive Analytics: A Stochastic Optimization Framework for Asymmetric Cost Behavior
Keywords:
Cost stickiness, Asymmetric cost behavior, Dynamic panel data, Prescriptive analytics, Stochastic optimizationAbstract
Costs are known to respond asymmetrically to demand, yet the literature remains concentrated in developed markets, relies largely on static models estimated using nominal data, and offers descriptive diagnoses without prescriptive guidance. In high-inflation emerging markets, nominal growth can mask contractions in physical demand, while executives confronting crises receive limited actionable guidance from reported elasticities. This study quantifies the asymmetric real adjustment of administrative and research expenditure in an emerging market and translates the estimated frictions into scenario-based retrenchment policies. The analysis draws on non-financial firms listed in Türkiye over 2002–2025, with all monetary series deflated using the annual consumer price index. Asymmetric elasticities are estimated using two-way fixed effects with Driscoll–Kraay standard errors and complemented by two-step system generalized method of moments estimations. The estimates then enter a stochastic program in which a single ex ante adjustment policy minimizes expected profit loss and capability strain over 2,000 draws from the joint distribution of the elasticity vector, subject to feasibility bounds derived from cost adjustments historically observed under comparable shocks. Administrative costs display moderate and estimator-sensitive stickiness, whereas research expenditure is statistically unresponsive to demand contractions while increasing with demand growth, a difference confirmed by a formal cross-model equality test. No statistically significant moderating effects of leverage or capital intensity on the asymmetry are detected. The resulting policy favors retrenchment primarily through administrative overhead while explicitly accounting for the trade-off between short-term profit protection and long-term organizational resilience. By translating diagnostic elasticities into a stochastic decision framework, the study provides actionable retrenchment guidance for firms operating in volatile, high-inflation economies.
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