Abstract:
Purpose: This study explores the dynamics of rebar steel pricing in Pakistan, emphasizing the interplay of macroeconomic indicators and resource price dynamics. It aims to analyze the factors influencing steel price volatility and develop robust econometric models to enhance forecasting accuracy and inform policy interventions. Design/Methodology/Approach: Utilizing monthly data from 2017 to 2024, this research applies Autoregressive Distributed Lag (ARDL) and Autoregressive Integrated Moving Average (ARIMA) models to capture short- and long-term relationships between steel prices, GDP growth, exchange rate fluctuations, and resource price dynamics. Econometric analyses ensure a comprehensive understanding of the contributing factors and predictive models. Findings: The results reveal that exchange rate volatility is the most significant driver of steel price fluctuations, followed by crude oil prices. Lagged steel prices indicate price inertia, reflecting the persistent influence of historical trends. The ARDL model outperforms ARIMA in explaining price dynamics, showcasing its ability to incorporate macroeconomic factors effectively. Policy gaps, such as exchange rate instability and inefficient energy pricing mechanisms, exacerbate market volatility. Significance: This research provides actionable insights for policymakers and industry stakeholders to stabilize the steel market and reduce reliance on imports. By integrating economic theory with empirical modeling, it contributes to the academic understanding of commodity pricing dynamics and offers a replicable framework for similar studies in developing economies.
