| ID | 148505 |
| Title Proper | Causality direction of the stock market–growth nexus |
| Other Title Information | application of gmm dynamic panel data and the panel ganger non-causality tests |
| Language | ENG |
| Author | Deyshappriya, N P Ravindra |
| Summary / Abstract (Note) | This study is an attempt to model the linkages between stock market development and economic growth in developed and emerging markets. The causality direction between stock market development and economic growth has also been examined in order to provide solid policy implications. Mainly, the relationship and direction of causality between stock market development and economic growth were tested using dynamic panel data analysis based on the Generalised Methods of Moment (GMM) and panel Granger non-causality, respectively. Additionally, panel unit root tests were also applied to check the stationary of the selected variables to avoid misleading results. The study focuses on 20 countries, both developed and emerging markets, and data were collected over the period 1990–2014 mainly from World Bank data series. Dynamic panel data analysis confirms that there is a statistically significant relationship between stock market development and economic growth in both developed and emerging markets. Further, the study emphasises that only the finance-led growth hypothesis is valid for emerging markets while developed markets support bidirectional causality between stock market development and economic growth, reflecting the existence of both the finance-led growth hypothesis and the growth-led finance hypothesis. Hence, it is crucial to formulate appropriate policies to shift unproductively allocated funds towards stock markets to meet long-term capital requirements to encourage higher economic growth. |
| `In' analytical Note | Margin Vol. 10, No.4; Nov 2016: p.446-464 |
| Journal Source | Margin 2016-12 10, 4 |
| Key Words | Economic Growth ; Dynamic Panel Data Analysis ; Stock Market Development ; Finance-led Growth Hypothesis ; Granger Non-causality Test |