For the assignment I will examine whether or not a linear regression model is suitable for estimating the relationship between Human development index (HDI) and its components. Linear Regression is a statistical technique that correlates the change in a variable to other variable/s, the representation of the relationship is called the linear regression model.
Variables are measurements of occurrences of a recurring event taken at regular intervals or measurements of different instances of similar events that can take on different possible values. A dependent variable is a variable whose value depends on the value of other variables in a model. Hence, an independent variable is a variable whose value is not dependent on other variables in a model. The dependent variable here is HDI and this will be regressed against the independent variables which include Life expectancy at birth, Mean years of schooling, expected years of schooling and Gross National Income per capita Hence we can model this into Yi = b0 + b1 xi + b2 xi + b3 xi + b4 xi + where Y is HDI, β0 is a constant, β1 β2 β3 β4 are the coefficients and denotes for random/error term.
R2 is how much your response variable (y) is explained by your explanatory variable (x). The value of R2 ranges between 0 and 1, and the value will determine how much of the independent variable impacts on the dependent variable. The R2 value will show how reliable the regression represents the actual data in forecasting population values of Human Development. R2=1(∑e2/∑y2) where ∑y2 is Total sum of squares (TSS) and ∑y2 is Residual sum of squares (RSS)
The closer the R2 value is to the 1 value the more reliable the regression line is as an index, and if it is equal to 1 it represents a perfect fit. For my data, I have regressed my dependent variable against all my independent variables and computed the R2 to be 0.9933 (99.33%), which shows a strong correlation between...
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ECONOMETRICS



First of all, I would like to apologize for showing the results in Spanish, but I couldn’t find the way to change Gretl’s language. However, all the explanations are in English, so I hope there is no problem to understand the results.
Secondly, I would just inform you that the timeseries data that I have used is “U.S. macro data, 19502000” from Greene Sample folder in Gretl.
Before building the model…
I would try to explain the...
...Econometrics is the application of mathematics and statistical methods to economic data and described as the branch of economics that aims to give empirical content to economic relations. [1] More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference."[2] An influential introductory economics textbook describes econometrics as allowing...
...ECON 140
Section 13, November 28, 2013
ECON 140  Section 13
1
The IV Estimator with a Single Regressor and a Single Instrument
1.1
The IV Model and Assumptions
Consider the univariate linear regression framework: Yi = β0 + β1 Xi + ui
Until now, it was assumed that E (ui Xi ) = 0, i.e. conditional mean independence.
Let's relax this assumption and allow the covariance between Xi and ui to be dierent from zero.
Our problem here is that ui is not observed.
...
...
30050 Applications for Economics Management and Finance
Applied research project and report
“Man is by nature a Political Animal”
FACTORS SHAPING INDIVIDUAL’S INTEREST IN POLITICS
INDEX:
I. ABSTRACT
II. INTRODUCTION
III. DATA DESCRIPTION: Dependent variable and Independent variables
IV. MULTIVARIATE ANALYSIS: Factor, Regression and ANOVA Analysis
V. CONCLUSIONS
VI. REFERENCES
I. ABSTRACT
The purpose of this project is to...
...format):
Regressor tradeshare Coefficient 1.34 (0.88) 0.56** (0.13) −2.15* (0.87) 0.32 (0.38) −0.00046** (0.00012) 0.626 (0.869) 1.59 0.29 0.23
yearsschool rev_coups assasinations rgdp60 intercept SER R2 R2
116
Stock/Watson  Introduction to Econometrics  Second Edition
The coefficient on Rev_Coups is −2.15. An additional coup in a five year period, reduces the average year growth rate by (2.15/5) = 0.43% over this 25 year period. This means the GPD...
...Significant
Significant
Significant
Significant
The coefficients are all significant at 1%, 5% and 10% levels.
Autocorrelation could be present. Rsquared could be overestimated at 60%, which is quite high. Standard errors are quite low. Econometric data has many factors so standard errors cannot be that low. The DurbinWatson dtest needs to be carried out to confirm the existence of autocorrelation in this example. GLS or NeweyWest method can be used to correct...
...UNIVERSITY OF MACAU FACULTY OF BUSINESS ADMINISTRATION BACHELOR'S DEGREE PROGRAMME
ECIF311 ECONOMETRICS II
Second Semester 20102011
Instructor Contacts P. S. Tam Office: L430 (Thursday 4:00 p.m.  7:00 p.m. Or By appointment.) Phone: 83974756 Email: pstam@umac.mo Friday 1:00 p.m.  4:00 p.m. J207 http://webcourse.umac.mo
Class Website
Description:
This course focuses on basic econometric techniques, emphasizing both technical derivations and practical...
...Descriptive Statistics
Mean
Variance
Standard Deviation
Sample Covariance
If it is greater than zero, upward sloping. This is scale dependent.
Sample Correlation
This is scale independent: between 1 and 1, close to 1 is upward, 0 is central, 1 is downward sloping.
Finding the regression
Regression formula with one regressor
Slope
Intercept
Finding R2
TSS=ESS+SSR
The Coefficient of Determination = R2
This gives the total fit of , between 0 (chance)...
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