Principles of Econometrics - Essay Example

ASB 3303 – Principles of Econometrics – Assignment By Question Economic Growth Part The Excel results are  = - 0.248, β = 0.751 as shown below.   Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -0.2479 0.4445 -0.5576 0.5779 -1.1261 0.6303 -1.1261 0.6303 investment 0.7509 0.1415 5.3065 3.8E-07 0.4714 1.0305 0.4714 1.0305 Part 2: The STATA results are  = - 0.248, β = 0.751 (same as those from Excel) as shown below: ------------------------------------------------------------------------------ growth | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- investment | .7509942 .1415228 5.31 0.000 .471432 1.030557 _cons | -.2479032 .4445778 -0.56 0.578 -1.126116 .63031 ------------------------------------------------------------------------------ Part 3: ----------------------------------------------------------------------------- growth | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- investment | .7616264 .1389075 5.48 0.000 .4872022 1.036051 gdp_1970 | -.1652869 .0584313 -2.83 0.005 -.2807231 -.0498507 population | -.3451208 .0822328 -4.20 0.000 -.5075791 -.1826626 _cons | -.6526536 .50358 -1.30 0.197 -1.647521 .3422142 The results show that the starting GDP and population have negative effects on growth while investment has a positive effect on growth. Higher growth rate of investments mean that the country is investing more in various sectors of production and therefore real GDP growth rates rise. Studies on investment have largely focused on Foreign Direct Investments (FDIs) and most of the studies are unanimous that FDI lead to higher economic growths (Alfaro et al., 2006). Solow had also intimated that technological progress grows with the economy. Investments could therefore be considered as part of the technological progress. The results are regards the effect of investment on economic growth are therefore consistent with literature. Sustaining higher growth rate for a country is not an easy task therefore countries with higher starting GDPs have lower growth rates than those with lower starting GDPs. Therefore, as the results show, higher population growths lead to lower economic growths. Solow’s model noted that in a stead state, the higher population growth reduces per capita income, but has no impact on per capita income growth. Given that the sample was drawn from a number of countries most of which are in transition states, the results are consistent with Solow’s model which had opined that population growth has a negative impact on economic growth because such population growths force countries to spend their meager savings on capital widening instead of capital deepening. These results are also consistent with recent findings of Klasen and Lawson (2007) in Uganda who found a negative impact of population growth on per capita growth using both cross-sectional and panel data and employing Harrod-Domar Model as well as the Solow Model. Part 4: The results show that are more open to international trade do not have higher growth rates as the effect was not significant at 5% level. The test results presented below the table also show that indeed openness was not a significant factor that influenced growth rates. The hypothesis is therefore not supported. ------------------------------------------------------------------------------ growth | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- investment | .6895645 .1446365 4.77 0.000 .4038071 .975322 gdp_1970 | -.1801158 .0587595 -3.07 0.003 -.2962066 -.0640251 population | -.3591867 .0821812 -4.37 0.000 -.5215516 -.1968218 openness | .170063 .1014977 1.68 0.096 -.0304653 .3705913 _cons | -1.091889 .5651131 -1.93 0.055 -2.20838 .0246011 ------------------------------------------------------------------------------ . test openness (1) openness = 0 F (1, 152) = 2.81 Prob > F = 0.0959 Part 5 i. Investment is the only determinant of growth at least one of . qui reg growth investment gdp_1970 population openness . test gdp_1970 population openness (1) gdp_1970 = 0 (2) population = 0 (3) openness = 0 F (3, 152) = 7.16 Prob > F = 0.0002 The tests show that the null hypothesis is rejected and the alternative hypothesis accepted. Therefore, investment is not the only determinant of growth. ii. The relationship between the population growth rate and economic growth is equal to 0.2 . qui reg growth investment gdp_1970 population openness . test population = 0.2 (1) population = .2 F (1, 152) = 46.30 Prob > F = 0.0000 The null hypothesis is rejected as the estimate clearly distinguishes the estimated coefficient from 0.2. Therefore, the relationship between population growth rate and economic growth is not equal to 0.2. iii. GDP in 1970 and population growth have the same effect on economic growth (1) gdp_1970 - population = 0 F (1, 152) = 5.85 Prob > F = 0.0168 Null hypothesis is rejected. Therefore, GDP in 1970 and population growth do not have the same effect on economic growth. Question 2 Part 1: The results below indicate that exports and R&D expenditure have positive and significant effects on firm profits. This is because firms that export more get more revenues hence more profitability. Further, firms that invest more in R&D are able to manufacture superior and new products in the market which can benefit from early adoption hence higher sales and therefore higher profits. ------------------------------------------------------------------------------ profits | Coef. Std. Err. t P>|t| Beta -------------+---------------------------------------------------------------- exporter | .1656126 .0370054 4.48 0.000 .1754707 rd | .0441306 .0066857 6.60 0.000 .2588029 _cons | .815282 .0303779 26.84 0.000 . ------------------------------------------------------------------------------ Part 2: A 1 standard deviation increase in R&D expenditure leads to a 0.259 standard deviation increase in firm profits. Part 3: In order to test this, a new variable called rd_exporter is created which is the interaction between exporter and rd variables. A regression is then run after which the test for rd_exporter variable is made. This tests the null hypothesis that rd_exporter does not affect returns. In other words, the hypothesis is as given below: The results of the regressions lead to the rejection of the null hypothesis that returns do not differ according to whether firms export. Thus, the alternate hypothesis that returns differ according to whether firms export is accepted. ------------------------------------------------------------------------------ profits | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- exporter | -.2002007 .0939031 -2.13 0.033 -.3845653 -.015836 rd | .0275885 .0076777 3.59 0.000 .0125146 .0426624 rd_exporter | .0637463 .0150717 4.23 0.000 .0341554 .0933373 _cons | .8796609 .0336579 26.14 0.000 .8135787 .945743 . test rd_exporter (1) rd_exporter = 0 F (1, 702) = 17.89 Prob > F = 0.0000 Part 4: (i) The results of the regression shown below reveal that wages and import penetration negatively affect firm profits. Higher wages usually mean higher costs of production (expenses) and therefore reduce the firm profits. On the other hand, firms with higher import penetration mean that they increasingly import more than they export hence get lower revenues from the industry and therefore lower profits. ------------------------------------------------------------------------------ profits | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- exporter | .1086071 .036519 2.97 0.003 .0369073 .1803068 rd | .0338282 .0068034 4.97 0.000 .0204708 .0471856 wage_firm | -.8450672 .1157769 -7.30 0.000 -1.072378 -.6177561 impen | -.0012132 .0005281 -2.30 0.022 -.0022501 -.0001764 _cons | 1.115564 .0506337 22.03 0.000 1.016152 1.214976 Part 4 (ii) The results of the tests on whether profits are unaffected by wages and import penetration lead to the rejection of the null hypothesis. Wages and import penetration significantly affect firm profits. . test wage_firm impen (1) wage_firm = 0 (2) impen = 0 F (2, 701) = 27.97 Prob > F = 0.0000 Part 5: . qui reg profits exporter rd wage_firm impen industry . test industry (1) industry = 0 F (1, 700) = 1.38 Prob > F = 0.2406 The null hypothesis that industry does not affect earnings is accepted. Therefore, firms in the automotive industry do not earn significantly different profits compared to firms in the semiconductor industry. Question 3: Part 1: ------------------------------------------------------------------------------ price | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- number_firms | -.3963393 .0198159 -20.00 0.000 -.4352033 -.3574752 pop_density | -.0049359 .0086159 -0.57 0.567 -.0218339 .0119621 cons_emp | .0284787 .0049504 5.75 0.000 .0187698 .0381877 _cons | 180.9784 4.786289 37.81 0.000 171.5913 190.3656 ------------------------------------------------------------------------------ . test number_firms (1) number_firms = 0 F( 1, 1833) = 400.04 Prob > F = 0.0000 The test results lead to the rejection of null hypothesis that competition affects prices. Therefore, competition does not affect prices within a market. This can be because of the fact that prices are set on the basic of margins above the costs of production and therefore no firm can just reduce the prices just because of increased competition but on efficiency or reduced costs of production. Part 2: . qui regress price number_firms pop_density cons_emp . hettest Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of price chi2(1) = 53.49 Prob > chi2 = 0.0000 . whitetst Whites general test statistic: 49.62109 Chi-sq (9) P-value = 1.3e-07 Both tests indicate presence of a significant degree of heteroskedasticity related to the scale in the model. Part 3: | price number~s pop_de~y cons_emp -------------+------------------------------------ price | 1.0000 number_firms | -0.4053 1.0000 pop_density | -0.0003 -0.0208 1.0000 cons_emp | -0.0035 0.2949 0.0202 1.0000 . qui regress price number_firms pop_density cons_emp . test number_firms pop_density cons_emp (1) number_firms = 0 (2) pop_density = 0 (3) cons_emp = 0 F (3, 1833) = 133.36 Prob > F = 0.0000 Both the correlation and the F test results show no presence of multicollinearity. Multicolineaeity is therefore not a problem in this model. References Alfaro, L., Chanda, A., Kalemli-Ozcan, S., and Sayek, S., 2006. How Does Foreign Direct Investment Promote Economic Growth? Exploring the Effects of Financial Markets on Linkages. NBER Working Paper No. 12522, [e-journal] September. Abstract only. Available through NBER website [Accessed 14 April 2013]. ASB-3303 Principles of Econometrics – Tutorial 2, 2013. Class Notes. ASB-3303 Principles of Econometrics – Tutorial 3, 2013. Class Notes. Baum, C.F., 2010. Regression Techniques in Stata. Class notes, Boston College and DIW Berlin, http://economics.adelaide.edu.au/research/seminars/Stata_Lecture2.pdf Klasen, S., and Lawson, D., 2007. The impact of population growth on economic growth and poverty reduction in Uganda, Working Paper No. 133, [e-journal] Available though Econstor website [Accessed 14 April 2013]. McGowan, D., 2013. Principles of Econometrics – ASB 3303 Class notes, Bangor Business School UCLA Statistical Consulting Group. Regression with Stata [online] Available at: [Accessed 14 April 2013]. Read More