THE RELATIONSHIP BETWEEN FDI AND EXPORTS INSTITUTION: NAME: DATE: Table A. Confidence Intervals FDI per capita Exports per capita Whole sample H countries L countries Whole sample H countries L countries 2007 95% Upper -107944.225 647865.462 46758.852 -94766.960 405424.978 118391.313 Lower -619464.675 137397.450 11095.161 -361879.573 164683.498 -4929.370 99% Upper -19102.109 736614.060 52799.704 -49927.624 447279.644 139279.843 Lower -708306.791 48648.853 5054.309 -406718.909 122828.832 -25817.900 Table B. Hypothesis Tests 1995 2007 2016 FDI per capita Mean difference -82932.690 -363704.450 -503714.430 Std error difference 35033.347 125105.796 216762.082 t-ratio -2.367 -2.907 -2.324 Exports per capita Mean difference -110728.088 -228323.267 -222655.838 Std error difference 29012.727 66280.709 82392.372 t-ratio -3.817 -3.445 -2.702 Table C. Correlations. (Note only complete the lower part of each panel) FDI per capita Exports per capita YSC Whole sample FDI per capita 1 Exports per capita 0.694 1 YSC 0.409 0.437 1 H countries FDI per capita 1 Exports per capita 0.720 dummy. Only FDI coefficient was statistically significant in this model and it implied that 1% increase in FDI per capita resulted in 0.873% increase in exports per capita. The 1995 regression model with H dummy variable explain the variation in exports per capita better than the model without a dummy variable (adjusted R squared is higher by 0.1%). The 2007 and 2016 regression model are best explained by the models with dummy variables due to high values of adjusted R squared. REFERENCES Chapter 2. Correlation functions. (n.d.). Multivariate Characteristic and Correlation Functions. doi:10.1515/9783110223996.67 Lai J. Yang B. Lin D. Kerkhoff A. J. & Ma K. (2013). The Allometry of Coarse Root Biomass: Log-Transformed Linear Regression or Nonlinear Regression? PLoS ONE 8(10) e77007. doi:10.1371/journal.pone.0077007 Model specification in regression analysis. (n.d.). Understanding Regression Analysis 166-170. doi:10.1007/978-0-585-25657-3_35 Regression analysis with dummy variables. (n.d.). Understanding Regression Analysis 128-132. doi:10.1007/978-0-585-25657-3_27 Test T. (2017). test. doi:10.2172/1373483 [...]
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