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Selected Examples for:

Damodar Gujarati and Dawn Porter, Basic Econometrics, 5th Ed., McGraw Hill, 2008


Data Sets used in these examples.

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Chapter 2. Two-Variable Regression Analysis: Some Basic Ideas
ex_2.1.sha Creating a graph of mean wage against years of schooling
ex_2.2.sha Creating a graph of average maths scores against average family income
Chapter 3. Two-Variable Regression Model: The Problem of Estimation
ex_3.1.sha Modelling personal consumption expenditure using an OLS regression
ex_3.2.sha Modelling food expenditure in India using an OLS regression
ex_3.3.sha Modelling demand for mobile phones and personal computers in relation to per capita personal income using OLS
Chapter 5. Two-Variable Regression: Interval Estimation and Hypothesis Testing
ex_5.1.sha Modelling food expenditure in India using OLS regression, doing a hypothesis test and plotting residuals
Chapter 6. Extensions of the Two-Variable Linear Regression Model
ex_6.1.sha Modelling food expenditure in India using an OLS regression
ex_6.2.sha Modelling Gross Private Domestic Investment in the US using an OLS model and data in different units of measurement with and without an intercept
ex_6.3.sha Modelling expenditure on durable goods using an OLS regression with generated variables
ex_6.4.sha Modelling the rate of growth of expenditure in services expenditure on durable goods using an OLS regression with generated variables and creating a graph
ex_6.5.sha Modelling food expenditure in India using a linear-log OLS model and plotting a graph
ex_6.6.sha Modelling a relationship between child mortality and per capita GNP using an OLS regression and plotting a graph
ex_6.7.sha Modelling Phillips curve using an OLS regression and ploting a graph
Chapter 7. Multiple Regression Analysis: The Problem of Estimation
ex_7.1.sha OLS Multiple Regression using child mortality data
ex_7.2.sha Modelling coffee consumption in the US using an OLS regression on original and log data
ex_7.3.sha Modelling a Cobb-Douglas Production Function for the US using OLS regression
ex_7.4.sha Modelling a Total Cost Curve using nonlinear regression and plotting the data
Chapter 8. Multiple Regression Analysis: The Problem of Inference
ex_8.2.sha Modelling a Total Cost Curve using nonlinear regression and testing the hypothesis of equality of coefficients
ex_8.3.sha Modelling a Cobb-Douglas production function using log variable transformation, testing a restriction of constant returns to scale and testing a hypothesis of equality of coefficients
ex_8.4.sha Modelling the demand for chickens in the US using a log-log OLS regression and testing restrictions on coefficients
ex_8.5.sha Modelling the demand for roses in the Detroit metropolitan area using linear and log-log OLS regressions and constructing the MacKinnon, White and Davidson (MWD) test
Chapter 9. Dummy Variable Regression Modelling
ex_9.1.sha Estimating geographical differences in the average salaries of public school teachers using an OLS model with ANOVA output
ex_9.2.sha Modelling hourly wages using marital status and residence region dummy variables by using an OLS model with ANOVA output
ex_9.3.sha Estimating geographical differences in average salaries of public school teachers using an OLS model with ANOVA output
ex_9.4.sha Estimating the OLS model of structural differences in US savings and income over time using a dummy variable and an interaction term, thereby constructing a dummy variable alternative to a Chow test
ex_9.5.sha Modelling hourly wage using marital status and residence region dummy variables, interaction dummies and years of education by using an OLS model with ANOVA output
ex_9.6.sha Estimating OLS models of seasonality in the demand for refrigerators in the US using dummy variables with one being generated and creating a residual table
ex_9.7.sha Modelling the cost function using the piecewise OLS regression
ex_9.8.sha Modelling log of hourly wage using a gender dummy variable by estimating an OLS model with ANOVA output
ex_9.12.sha Modelling the wage equation of workers in a town in southern India with simple and interraction dummy variables using OLS regression
Chapter 10. Multicollinearity: What Happens If Regressors Are Correlated?
ex_10.1.sha Illustrating the problems of multicollinearity on a consumption-income OLS regression and giving an example of how to overcome these problems
ex_10.2.sha Modelling the consumption function in the US using OLS regression with potentially collinear regressors
Chapter 11. Heteroscedasticity: What Happens If the Error Variance Is Nonconstant?
ex_11.1.sha Testing for heteroskedasticity in an OLS model of average compensation
ex_11.2.sha Testing for heteroskedasticity in an OLS model of average compensation using Glejser's test
ex_11.3.sha Testing for heteroskedasticity in a model of average annual return of mutual funds, estimated using OLS, by a Spearman's Rank Correlation Coefficient test
ex_11.4.sha Running a Goldfeld-Quandt heteroskedasticity test on a preliminary sorted dataset
ex_11.5.sha Running a Breusch-Pagan-Godfrey (BPG) heteroskedasticity test on a linear OLS regression
ex_11.7.sha Using Weighted Least Squares in a model of compensation in the US nondurable manufacturing industries to solve the problem of heteroskedasticity
ex_11.9.sha Testing a model of child mortality for heteroskedasticity using Park's, Glejser's and White's tests and plotting residuals
ex_11.10.sha Testing a model of Research and Development expenditure for heteroskedasticity using Park's, Glejser's and White's tests, using an OLS specification with White's standard errors and plotting residuals
ex_11.11.sha Testing a model estimating the mean salary of classroom teachers in Northwest Ohio correcting for heteroskedasticity in the OLS specification using White's standard errors
Chapter 12. Autocorrelation: What Happens If Error Terms Are Correlated?
ex_12.13.sha Modelling a consumption function using OLS regression, testing the model for autocorrelation and correcting for it
Chapter 13. Econometric Modeling: Model Specification and Diagnostic Testing
ex_13.2.sha Illustrating biases caused by measurement errors in the OLS regressions
ex_13.4.sha Conducting a Davidson-MacKinnon J-test on two different specifications of a model of per capita personal consumption expenditure, estimated using OLS with distributed lags
ex_13.11-1.sha Conducting a variable inclusion, linearity of regressors, heteroskedasticity and normality tests on a model of hourly wage determination, estimated using OLS
Chapter 14. Nonlinear Regression Models
ex_14.1.sha Estimating an exponential regression model of fees paid by a US mutual fund to its investment advisors using a nonlinear method of estimation, computing R² and conducting Durbin-Watson autocorrelation test
ex_14.2.sha Estimating a Cobb-Douglas production function of the Mexican economy with linear additive errors using a nonlinear method of estimation, computing R² and conducting Durbin-Watson autocorrelation test
ex_14.3.sha Estimating a model of US population growth using nonlinear regression of the logistic form and an OLS linear regression
ex_14.4.sha Estimating a model of US population growth using Box-Cox transformations
Chapter 15. Qualitative Response Regression Models
ex_15.1.sha Estimating a Linear Probability Model of US house ownership and correcting it for heteroskedasticity
ex_15.4.sha Estimating a Linear Probability Model of debit card ownership and correcting it for heteroskedasticity
ex_15.5.sha Estimating a Logit Probability Model of the debit card ownership
ex_15.6.sha Estimating a Model of house ownership using grouped probit
ex_15.7.sha Estimating a Model of smoking behaviour using a Linear Probability; Logit and Probit specifications
Chapter 17. Dynamic Econometric Models: Autoregressive and Distributed-Lag Models
ex_17.7.sha Modelling US per capita personal consumption expenditure using a Koyck model, estimating mean and median lags
ex_17.10.sha Modelling Private Consumption Expenditure in Sri Lanka using a Koyck model and estimating the long run marginal propensity to consume
ex_17.11.sha Modelling Inventories in the US by estimating an OLS regression using Almon distributed lags
ex_17.13.sha Conducting Granger causality tests on quarterly Canadian GDP and money supply, modelled as a linear
relationship