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。
ce Index).
y = β0 + β1Ln (RGDP) + β2 (CPI) + β3 (FDI) +
Econometric Model with Expected Sign:
= β0 + β1L (RGDP) + β2 (CPI) + β3(FDI)
(-ve) (-ve) (-ve)
Where +ve indicates that there is a postive relationship between the explanatory variable and dependent variable. On the other hand, -ve indicates that there is a negative relationship between the explanatory variable and dependent variable
3.3.2 Unit root
A unit root test is used to examine whether a time series variable is stationary. In the model, T-statistic, F-statistic and R-squared are used to determine to ensure the validity of the test statistics is stationary. The result will become spurious regression problem if the non-stationary series in the ordinary least square (OLS) regression is used. Spurious regression result in high significant T-statistic and highly value for the coefficient of determination R-squared, and the R-square is larger than Durbin Watson. Therefore, if the stationary does not hold, estimate is not consistent and result will be misleading. To avoid the spurious regression problem, the Augmented Dickey-Fuller test (ADF) is used to examine the stationary of the variable.
An Augmented Dickey-Fuller test (ADF) is used to test for a unit root in a time series sample. The Augmented Dickey-Fuller (ADF) statistic used in the test is a negative number. Therefore, the more negative value is, more power the rejection of the hypo
thesis that there is a unit root at some level of confidence.
The equation for Augmented Dickey-Fuller (ADF) test
Where α is a constant, β is the coefficient on a time trend and p is the lag order of the autoregressive process. α = 0 and β = 0 corresponds to modeling a random walk and β = 0 corresponds to modeling a random walk drift. By including lags of the order p, the ADF formulation allows for higher-order autoregressive processes. This means that the lag length p needs to be determined when applying in the test. One possible approach is to test from high orders and examine the t-value on coefficients. The criterion such as the Akaike information criterion (AIC), Schwarz-Bayesian information criterion (SBIC) or the Hannan-Quinn information criterion (HQIC) test is used to examine the lag length.
3.3.3 Granger Causality
The Granger Causality test indicates that a time series Y is said to be Granger caused by X if X helps the prediction of Y or equivalently if the coefficients on the lagged X are statistically significant. Granger Causality shows two-way causation in the case. X Granger causes Y and Y Granger causes X. It usually through a series of t-tests and F-tests on lagged values of X and lagged values of Y.
3.3.4 Multiple Regressions
The ordinary least squares (OLS) or linear least squares are a method to examine the unknown parameters in a linear regression model. It is used to assume the distribance, ui. According to Gujarati (2003), ui stands for the normal distribution representing zero mean and constant variance, σ2 in the multiple regression models. With the normality assumption, OLS estimators 1, and 2 are linear functions of ui. Therefore, if ui are normally distributed, so 1,and 2 will make hypothesis testing more straightforward. OLS estimators of the partial regression coefficients are identical with the maximum like
本论文由英语论文网提供整理,提供论文代写,英语论文代写,代写论文,代写英语论文,代写留学生论文,代写英文论文,留学生论文代写相关核心关键词搜索。