Econ fin

BASIC ECONOMETRICS - A2

Determinants of HDI individual Report

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DETAILED INSTRUCTION

A/ ASSIGNMENT RECAP

      Write a 3000-word report analyze determinants of the Human Development Index (HDI) for a sample of countries, including reviewing academic literature, running multiple regressions, interpreting results, testing hypotheses, and proposing policy recommendations

 

Suggested Structure:

 

  1. Part 1: Overview and Data Description
  1. Overview of Topic (Suggested 150 words)
  2. Data description (Suggested 250 words)

  II.      Part 2: Initial estimation

  1. Linear Regression Model (Suggested 250 words)
  2. Model Estimation Using OLS (Suggested 250 words)

 III.      Part 3: Interpretation

  1. Interpret R-Squared (Suggested 250 words)
  2. F-test Interpretation (Suggested 250 words)
  3. T-test Interpretation (Suggested 250 words)
  4. Expectation & Actual Results Comparison (Suggested 250 words)
  5. Models Comparison (Suggested 250 words)

IV.      Part 4: Further Estimation

  1. Dummy variables  (Suggested 250 words)
  2. Interaction term (Suggested 250 words)
  3. Alternate model Estimation (Suggested 250 words)

V.       Part 5: Conclusion

  1. Findings Summary (Suggested 150 words)
  2. Policies Proposal  (Suggested 250 words)

 

B/ KEYWORD EXPLANATIONS

  1. Regression

A statistical method used to estimate the relationship between a dependent variable and one or more independent variables based on observed data.

 

  1. Single Linear Regression

A regression model with one independent variable used to estimate its linear effect on a continuous dependent variable. It takes the form:

 

Y = β0 + β1X + ε

 

Where Y is the dependent variable, X is the single independent variable, β0 is the intercept, β1 is the slope coefficient on X, and ε is the error term.

 

  1. Multiple Linear Regression

A regression model with two or more independent variables used to estimate their linear effects on a continuous dependent variable. It takes the form:

 

Y = β0 + β1X1 + β2X2 + ... + βnXn + ε

 

Where Y is the dependent variable, X1 to Xn are the multiple independent variables, β0 is the intercept, β1 to βn are the slope coefficients, and ε is the error term.

 

  1. Coefficient

The estimated parameter values from a regression model that quantify the effect of each independent variable on the dependent variable.

 

  1. Statistical significance

A measure indicating whether a regression coefficient or test result is unlikely to have occurred by chance, determined by the p-value and significance level chosen.

 

  1. Goodness-of-fit

Goodness-of-fit - Statistics like R-squared that indicate how well a regression model fits and explains the variation in the dependent variable based on the predictors.

 

  1. Hypothesis testing

The use of sample data to determine whether to reject a hypothesis about a population parameter at a specified significance level based on statistical evidence.

 

  1. Mean

A measure of central tendency calculated as the sum of all values divided by the number of values in a sample or population distribution.

 

  1. Standard Regression Format

The conventional structure for presenting regression results including coefficient estimates, standard errors, and diagnostic statistics.

 

  1. Descriptive statistics

A statement of no statistical significance or effect that is tested and either supported or rejected based on evidence from a sample.

 

  1. Ordinary Least Squares (OLS)

A common method for estimating the coefficients in a linear regression model by minimizing the sum of squared residuals.

 

  1. Adjusted R-squared

A modified version of R-squared that accounts for the number of predictors in the model. Used to assess goodness-of-fit.

 

  1. F-test

A statistical test used to determine if the regression model as a whole has a statistically significant relationship with the dependent variable.

 

  1. Significance level

The probability threshold used to determine statistical significance, most commonly 0.01, 0.05 or 0.10.

 

  1. Dummy Variable

A binary categorical variable coded as 1 or 0 used to represent a qualitative characteristic

 

  1. Interaction term

A variable created by multiplying two predictors to estimate their combined effect and test moderation effects.

 

  1. T-test

A statistical test used to determine if a regression coefficient is significantly different from zero based on its t-statistic.

 

 

D/ DETAILED OUTLINE

 

  1. Part 1: Overview and data description
  1.  Overview of topic (4-5 fist sentences)

-        Search Google Scholar, EconLit, JSTOR for recent papers on "determinants of human development index", "drivers of HDI", "HDI empirical analysis" etc.

 

-        Scan articles to identify key variables examined that influence HDI.

 

-        Briefly summarize the main variables and expected relationships found in each paper.

Examples:

Lee (2022) found higher health expenditures and education levels positively affected HDI across developing countries.

 

-        Compile a list of determinants frequently found to impact HDI based on reviewing 3-5 articles.

-        Consider how these align with variables available in your dataset.

-        Select 4-6 potential predictors to focus on in your analysis based on data availability and consistency in the literature.

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