Consider the earnings model: Wage; = B. + B,Exper; + B,Educ; + Ui, where Wage is measured in dollars per hour, Exper is work experience in years, and Educ is the number of years of schooling. Tables 1 shows the OLS regression results for N = 100 males in a given year. Use the tables to answer the following questions: 4 befine the term Stationarity". How would you asses the stationarity of the variable X. How is non-stationarity usually handled? Table 1: Regression Results Number of obs = 100 F( 2,97) = 16.47 Model Prob>F = 0.0000 R-squared = 0.2235 Adj R-squared = 0.2081 Root MSE = 7.9039 Std. Err. t Wage Educ Coef. 1.435782 P>It! 0.000 0.321546 4.47 (95% Conf. Interval) 0.7976026 2.073962 0.1978813 0.4591687 -21.34716 -2.49127 .0658247 4.99 Exper 0.328525 -11.91922 0.000 0.014 cons 4.750254 -2.51 Table 2: Durbin Watson Test Durbin Watson = 0.72 Wage Experience Education Wage 1.00 Experience 0.91 1.00 Education 0.78 0.88 1.00 Table 3: Pairwise correlation Table 4: Breusch Pagan Test .hettest Breusch-Pagan HO........... Variables: Fitted variables of Wage Chi2 14.78 Prob > chi2 0.0001 Consider the earnings model: Wage; = B. + B2Exper; + B2Educ; + ui, where Wage is measured in dollars per hour, Exper is work experience in years, and Educ is the number of years of schooling. Tables 1 shows the OLS regression results for N = 100 males in a given year. Use the tables to answer the following questions: 4efine the term Stationarity". How would you asses the stationarity of the variable X. How is non-stationarity usually handled? Table 1: Regression Results Number of obs = 100 F( 2,97) = 16.47 Model Prob > F = 0.0000 R-squared = 0.2235 Adj R-squared = 0.2081 Root MSE = 7.9039 Wage Coef. Std. Err. t P>It Educ 1.435782 0.321546 4.47 0.000 [95% Conf. Interval] 0.7976026 2.073962 0.1978813 0.4591687 -21.34716 -2.49127 .0658247 4.99 0.000 Exper 0.328525 cons -11.91922 4.750254 -2.51 0.014 Table 2: Durbin Watson Test Durbin Watson = 0.72 Wage Experience Education Wage 1.00 Experience 0.91 1.00 Education 0.78 0.88 1.00 Table 3: Pairwise correlation Table 4: Breusch Pagan Test hettest Breusch-Pagan H............ Variables: Fitted variables of Wage Chi2 14.78 Prob > chi2 0.0001 = Consider the earnings model: Wage; = B. + B,Exper; + B,Educ; + Ui, where Wage is measured in dollars per hour, Exper is work experience in years, and Educ is the number of years of schooling. Tables 1 shows the OLS regression results for N = 100 males in a given year. Use the tables to answer the following questions: 4 befine the term Stationarity". How would you asses the stationarity of the variable X. How is non-stationarity usually handled? Table 1: Regression Results Number of obs = 100 F( 2,97) = 16.47 Model Prob>F = 0.0000 R-squared = 0.2235 Adj R-squared = 0.2081 Root MSE = 7.9039 Std. Err. t Wage Educ Coef. 1.435782 P>It! 0.000 0.321546 4.47 (95% Conf. Interval) 0.7976026 2.073962 0.1978813 0.4591687 -21.34716 -2.49127 .0658247 4.99 Exper 0.328525 -11.91922 0.000 0.014 cons 4.750254 -2.51 Table 2: Durbin Watson Test Durbin Watson = 0.72 Wage Experience Education Wage 1.00 Experience 0.91 1.00 Education 0.78 0.88 1.00 Table 3: Pairwise correlation Table 4: Breusch Pagan Test .hettest Breusch-Pagan HO........... Variables: Fitted variables of Wage Chi2 14.78 Prob > chi2 0.0001 Consider the earnings model: Wage; = B. + B2Exper; + B2Educ; + ui, where Wage is measured in dollars per hour, Exper is work experience in years, and Educ is the number of years of schooling. Tables 1 shows the OLS regression results for N = 100 males in a given year. Use the tables to answer the following questions: 4efine the term Stationarity". How would you asses the stationarity of the variable X. How is non-stationarity usually handled? Table 1: Regression Results Number of obs = 100 F( 2,97) = 16.47 Model Prob > F = 0.0000 R-squared = 0.2235 Adj R-squared = 0.2081 Root MSE = 7.9039 Wage Coef. Std. Err. t P>It Educ 1.435782 0.321546 4.47 0.000 [95% Conf. Interval] 0.7976026 2.073962 0.1978813 0.4591687 -21.34716 -2.49127 .0658247 4.99 0.000 Exper 0.328525 cons -11.91922 4.750254 -2.51 0.014 Table 2: Durbin Watson Test Durbin Watson = 0.72 Wage Experience Education Wage 1.00 Experience 0.91 1.00 Education 0.78 0.88 1.00 Table 3: Pairwise correlation Table 4: Breusch Pagan Test hettest Breusch-Pagan H............ Variables: Fitted variables of Wage Chi2 14.78 Prob > chi2 0.0001 =