Determinants of Financial Development by Y. Huang

By Y. Huang

This book examines the emergence of either monetary markets and carbon markets, and provides an in-depth research at the primary determinants of monetary improvement. It appears to be like at particular stories from an monetary, political and geographical point of view.

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4. 3, is presented in column 4. 06 Note: The models are estimated by OLS. The dependent variable is FD, over 1990–99. The t-values are reported in brackets. 1. The standardized coefficients show the change of a standard deviation of FD due to a one standard deviation change in a variable for those other than initial GDP and population, binary variables. ∗ , ∗∗ and ∗∗∗ significant at 10%, 5% and 1%, respectively. HUANG: “CHAP02” — 2010/9/29 — 20:05 — PAGE 35 — #26 36 Determinants of Financial Development found significant in every model.

It also reports the residual sum-ofsquares (RSS), the equation standard error or residual standard deviation (sigma), the squared multiple correlation coefficient (R2 ) and its values adjusted for degree of freedom R2adj , the log-likelihood value and three information criteria: the Akaike Information Criterion (AIC), the Hannan-Quinn Criterion (HQ) and the Schwarz Criterion (SC). 7, which clearly shows the variables included in the GUM and in the final model. 2, the BMA analysis for the whole sample yields a subset inclusive of four “other” variables (GDP90, POP90, ETHPOL and EURFRAC), two geographic variables (REGEAP and AREA), four policy variables (CTRADE, EXPPRIM, SDBMP and SDPI) and five institutional variables (CIVLEG, COMLEG, DURABLE, KKM and PCI).

Note that most of the institutional variables appear to be significant predictors of FD, in particular, the KKM (governance index) and PCI (political constraints index) have a posterior probability of inclusion close to 1. 1 has demonstrated that geography, institutions and policy as a group are all important in the process of financial development, although their effects may be picked up by varied predictors when conditioning on other factors is in place. These results clearly suggest that it would be more appropriate to include all of them in the analysis.

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