摘要:本文是一篇研究美国失业率的决定因素的留学生论文,失业率是最重要的宏观经济绩效的指标。失业率的出现是由于非竞争性工资差别造成的不正常的劳动力供应。从1945年至少到1968年的这段时期,欧洲主要的经济体的失业率比今天的标准低很多。
ficient Standard. Error t-Statistic Probability
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LNRGDP -16.77569 4.231093 -3.964859*** 0.0004
CPI 0.107573 0.026539 4.053451*** 0.0003
FDI -0.00000707 0.00000294 -2.406591*** 0.0217
C 143.5731 34.56874 4.153265*** 0.0000
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Notes: LNRGDP = Ln Real Gross Domestic Product, CPI = Consumer Price Index, FDI = Foreign Direct Investment; n = 38 observations. ***, **, * denotes 1%, 5% and 10% respectively. R-square = 0.604516, Adjusted R-square = 0.569620, F-statistic = 17.32350, Prob(F-statistic) = 0.0000 and Durbin-Watson statistic = 0.581446
Table 3 above shows that the result of an OLS regression of the unemployment with the independent variables (i.e. Real Gross Domestic Product, Consumer Price Index and Foreign Direct Investment).
From the regression output, all independent variables have the sign consistent with the empirical evidence provided by previous researchers. LRGDP and FDI have negative sign, indicating a negative relationship with unemployment. It is exactly same with the expected sign mentioned in previous chapter. However, CPI has a positive sign against unemployment, which indicate a positive relationship with unemployment rate, where an increase in the consumer price index will stimulate unemployment. The result of t-test for each independent variable shows that all independent variables are significant at all significant level except for foreign direct investment which only significant at the 5% and 10% significant level and hence, rejects the null hypothesis.
Besides that, the goodness of fit for the model is very high, as shown by the high R2 value, which is 0.65. It shows that 60.5% of the variation in the unemployment can be affected or explained by the variation in all the independent variables. Moreover, the probability of F-statistic is significant at 1% level. Thus, reject null hypothesis for F-test. This result proves that at least one independent variable is important in explaining the unemployment.
4. 5 Assumption test
The model is tested by adopting a few tests to check for econometric problems. There are multicollinearity, serial correlation, heteroscedasticity and misspecification error. Multicollinearity is used to test the correlation analysis. Breusch-Godfrey Serial Correlation LM Test is used to test the existence of serial correlation, Autoregression Conditional Heteroscedasticity Test is used for testing the heteroscedasticity variance of error of the model and Ramsey RESET Test is used to test the linearity and misspecification error.
Table 3: Diagnostic Checking
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Diagnostic Checking Result
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Multicollinearity – Correlation Analysis
CorrelationCPI, LNRGDP = 0.993480
R2CPI,LNRGDP = 0.987003
VIF =
=
= 76.9408 > 10 multicollinearity
Breusch-Godfrey Serial Correlation LM Test 23.90080
Autoregression Conditional Heteroscedasticity Test 14.12470
Ramsey RESE
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