The purpose of this study is to provide an econometric modeling of demand for bank credit and not only offer useful insights to the decision-makers in the public and private sector but also support researchers and analysts in recognizing the determinants of lending in a major dynamic economic context.
This study addresses the “supply-versus-demand-puzzle” by using a demand relationship and model loan demand as a function of interest rates and economic activity that may also capture supply effects. Loan demand modeled as a function of interest rates and economic activity not only represents a demand relationship but also captures supply effects. Using the generalized methods of moments estimation, the estimations are made robust to heteroskedasticity and/or autocorrelation of unknown form. GMM–Time series (HAC) option extends the robustness by using the weighting matrix that is robust to the contemporaneous correlation of unknown form to the autocorrelation of unknown form.
In a bank-dominated financial system like India, lending rates play a significant role in the transmission of monetary policy, as well as triggering and controlling loan demand and thereby exercising a pervasive effect on the output in the economy. The estimates indicate that the elasticity of loan demand is largely determined by the lending rate (0.6) and the economic activity (0.688). For one percentage point increase in capital ratio, the loan spread would rise by 31.4 basis points, which in turn would cause an increase of 18.8 basis points in loan demand assuming that risk-weighted assets are unchanged.
This is the first of its kind studying a banking system dominated emerging economy. Second, this study is based on a rich data set covering the period from 1979 to 2012, than other papers did, to capture the long-run association involving credit booms and busts and, thus, helps in avoiding the problem of estimation spanning the dominance of either boom or the bust alone. With a newer approach for quantification of the impacts of new regulatory standards, this study offers novel insights for the estimation of lending spreads.
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