Testing financial liberalization hypothesis with ARDL modelling approach

Abstract

It is a stylised fact that financial ‘repression’ retards economic growth. Hence, financial liberalization is advocated to remove the stranglehold on the economy. Financial liberalization policy argues that deregulation of interest rate would result in a higher real interest rate which would lead to increased savings, increased investment and achieve efficiency in financial resource allocation. Past studies have reported inconclusive results regarding the interest rate effects on savings and investment. This examines the financial liberalization hypothesis by employing autoregressive distributed lag (ARDL) modelling approach on Nepalese data. Results show that the real interest rate affects both savings and investment positively.

Notes

¹ In addition to matching savers and investors, Todaro and Smith (2003: pp. 733–734) list five other functions that are vital at the firm level and for the economy as a whole. These include: provision of payments services, generation and distribution of information, allocation of efficient credit, pricing, pooling and trading of risks and lastly, increasing liquidity of assets.

² Commercial banks and other financial intermediaries are subject to numerous lending restrictions and face mandatory interest rate ceilings on loanable funds at levels well below the market clearing rates. The rationale for maintaining the artificial interest rate ceilings is to finance the government budget deficit by selling low interest bonds to commercial banks.

³ According to the neo-classical growth theory, an increase in the savings rate raises the long-run level of capital and output per capita.

⁴ Most developing countries face either a shortage of domestic savings to match investment opportunities and/or a shortage of foreign exchange to finance needed imports of capital and intermediate goods.

⁵ These include interest rate deregulation, pro-competition measures, reduction of reserve requirements, easing of directed credit, privatisation of banks, stringent prudential regulation, securities markets deregulation and capital account liberalization.

⁶ Structural breaks are common in time series data. Examples of structural break can be regime change, change in policy direction, external shocks, war and so forth, which may affect economic time series.

⁷ However, subsequent studies using endogenous breaks have countered this finding with Zivot and Andrews (1992) concluding that seven of these 11 variables are in fact nonstationary.

⁸ The Nepalese currency is known as Rupees and abbreviated as Rs.

⁹ The early discussion on ARDL modelling approach can be found in Charemza and Deadman (1992) and others. Pesaran and Pesaran (1997), Pesaran and Smith (1998) and Pesaran and Shin (1999) popularized ARDL approach and it is now widely used in empirical research.

¹⁰ The model selection criteria are a function of the residual sums of squares and are asymptotically equivalent.

¹¹ The mean prediction error of AIC based model is 0.0005 while that of SBC based model is 0.0063.

¹² LTDR is in the natural log form while DRR is in the level form. An anti-log of the coefficient of DRR, which is 0.1020, is 1.1074.

¹³ The mean prediction error of SBC and AIC based ARDL models are 0.0014 and −0.0089, respectively.