Iv the model and estimation technique the theoretical

Info icon This preview shows pages 10–12. Sign up to view the full content.

IV. THE MODEL AND ESTIMATION TECHNIQUE The theoretical framework presented above exposed the channels through which money supply and inflation can influence economic growth. The model specified captures the objectives of the study and is based on the outcome of the theoretical frame work. To this end, this study formulates a monetary growth model on the basis of Polan and Grauwe (2005) type model that connects money and growth but with alterations on the right left hand variables whilst considering short and long run analyses of balanced growth and stability of the system. The model is specified as follows. 0 1 2 3 ln ln ln ln t t t t t y M K INF = φ + φ + φ + φ + ε (12) φ 1 > 0, φ 2 > 0, φ 3 < 0 (13) Where lny is the natural log of real output, lnM is the natural log of money supply; lnK is the natural log of gross domestic investment and å is a random error, which is assumed to be white noise, normally and identically distributed with zero mean and a constant variance. An increase in money supply makes output to rise by the same proportion as money will leave the real balances unchanged. This will not affect equilibrium condition because money
Image of page 10

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

Money Supply, Inflation and Economic Growth in Nigeria 157 supply will not change. When this occurs, it implies that the model exhibits neutrality of money. This implies that a change in money supply has no effect on output. The implication of inflation and capital stock on growth is unequivocally captured in equation (12). Equation (13) specifies the apriori theoretical expectations. Pesaran et al . (2001) developed a new Auto-Regressive Distributed Lag (ARDL) bounds testing approach for testing the existence of a cointegration relationship. The bound testing approach has certain econometric advantages in comparison to other single cointegration procedures (Engle and Granger, 1987; Johansen, 1988; Johansen and Juselius, 1990). Firstly, endogeneity problems and inability to test hypotheses on the estimated coefficients in the long-run associated with the Engle-Granger (1987) method are avoided. Secondly, the long and short-run parameters of the model in question are estimated simultaneously. Thirdly, the econometric methodology is relieved of the burden of establishing the order of integration amongst the variables and of pre-testing for unit roots. The ARDL approach to testing for the existence of a long-run relationship between the variables in levels is applicable irrespective of whether the underlying regressors are purely I (0), purely I (1), or fractionally integrated. Finally, as argued in Narayan (2005), the small sample properties of the bounds testing approach are far superior to that of multivariate cointegration. The approach, therefore, modifies the Auto-Regressive Distributed Lag (ARDL) framework while overcoming the inadequacies associated with the presence of a mixture of I (0) and I (1) regressors in a Johansen-type framework. A priori we expect growth to be significantly influenced by money, domestic prices and capital stock..
Image of page 11
Image of page 12
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern