README.md

In jcpernias/ec1027: Data and Functions for the course EC1027 - Econometrics I

ec1027

This package provides datasets and some functions to be used in the course EC1027 - Econometrics I.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("jcpernias/ec1027")

Datasets

The ec1027 package provides some datasets taken from Jeffrey M. Wooldridge (2006:) Introductory econometrics : a modern approach, 3rd ed., Thomson South-Western:

• bwght: Birth weight and cigarette smoking.

• earns: Earnings, productivity and hours (macro).

• gpa1: Gollege GPA and its predictors.

• hprice1: House prices and characteristics.

• hseinv: Housing investment and prices.

• intdef: Interest rates and Federal budget balance.

• rdchem: R & D and sales, chemical industry.

• traffic2: Speed limits and highway safety.

Note that the package wooldridge provides access to many more datasets. Also note that derived variables (logarithms, lags, etc) are not included in the ec1027 data sets.

Example

Load the ec1027 package and use data to bring one of the included data sets to the global environment.

library(ec1027)

## data on house prices
data(hprice1)

## Show the first observations
head(hprice1)
#>     price assess bdrms lotsize sqrft colonial
#> 1 300.000  349.1     4    6126  2438        1
#> 2 370.000  351.5     3    9903  2076        1
#> 3 191.000  217.7     3    5200  1374        0
#> 4 195.000  231.8     3    4600  1448        1
#> 5 373.000  319.1     4    6095  2514        1
#> 6 466.275  414.5     5    8566  2754        1

Code

Robust standard errors

Several functions have an vce parameter that allows the use of variance covariance estimators consistent in the presence of heteroskedasticity or autocorrelation.

Output from regression models is usually examined with the summary function:

mod <- lm(price ~ sqrft + bdrms + colonial, data = hprice1)

summary(mod)
#> 
#> Call:
#> lm(formula = price ~ sqrft + bdrms + colonial, data = hprice1)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -129.793  -40.167   -5.588   30.447  227.011 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -21.55241   31.21022  -0.691    0.492    
#> sqrft         0.12985    0.01395   9.310 1.41e-14 ***
#> bdrms        12.48749   10.02366   1.246    0.216    
#> colonial     13.07755   15.43591   0.847    0.399    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 63.15 on 84 degrees of freedom
#> Multiple R-squared:  0.635,  Adjusted R-squared:  0.622 
#> F-statistic: 48.72 on 3 and 84 DF,  p-value: < 2.2e-16

But summary only shows the OLS standard errors of estimates. The coef_table function produces output similar to summary but allows other covariance matrix estimators:

# Heteroskedasticity consistent errors
coef_table(mod, vce = "HC")
#> 
#> Call:
#> lm(price ~ sqrft + bdrms + colonial, data = hprice1)
#> 
#> Covariance matrix estimate: HC.
#> 
#> Coefficients: 
#>               Estimate Std. Error t value  Pr(>|t|)    
#> (Intercept) -21.552414  45.840628 -0.4702    0.6395    
#> sqrft         0.129849   0.021651  5.9973 4.876e-08 ***
#> bdrms        12.487493  11.018019  1.1334    0.2603    
#> colonial     13.077547  19.126077  0.6838    0.4960    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 63.1498 on 84 degrees of freedom
#> Multiple R-squared:  0.635,  Adjusted R-squared:  0.622 
#> F-statistic: 16.75 on 3 and 84 DF,  p-value: 1.2893e-08

Also, the se function computes standard errors robust to heteroskedasticity and autocorrelation. See more details in the documentation of this function.

Heteroskedasticity tests

The function white_test computes White’s test for heteroskedasticity:

white_test(mod)
#> 
#>  White's test for heteroskedasticity
#> 
#> data:  Auxiliary regression of squared residuals from:
#>   lm(price ~ sqrft + bdrms + colonial, data = hprice1)
#> on all covariates, their squares and cross-products.
#> 
#> 
#> F = 2.6942, df1 = 8, df2 = 79, p-value = 0.01128

The function het_test computes LM tests for heteroskedasticity that allow the user to specify the variables related to hetroskedasticity.

# Using all covariates
het_test(mod)
#> 
#>  LM test for heteroskedasticity
#> 
#> data:  Auxiliary regression of squared residuals from:
#>   lm(price ~ sqrft + bdrms + colonial, data = hprice1)
#> on all covariates.
#> 
#> 
#> F = 4.7726, df1 = 3, df2 = 84, p-value = 0.004035

# Using only sqrft and colonial
het_test(mod, ~ sqrft + colonial)
#> 
#>  LM test for heteroskedasticity
#> 
#> data:  Auxiliary regression of squared residuals from:
#>   lm(price ~ sqrft + bdrms + colonial, data = hprice1)
#> on: ~sqrft + colonial.
#> 
#> 
#> F = 5.9392, df1 = 2, df2 = 85, p-value = 0.003852

Hypotheses tests

The drop_test function performs a Wald test of the null joint hypotheses that the parameters of some variables are 0. The vce argument allows the use of alternative covariance matrix estimators.

# Joint significant test with the OLS covariance matrix estimator
drop_test(mod)
#> 
#>  Wald test for redundant variables
#> 
#> data:  Test for redudancy of all covariates.
#> 
#> Call:
#>   lm(price ~ sqrft + bdrms + colonial, data = hprice1)
#> 
#> 
#> F = 48.72, df1 = 3, df2 = 84, p-value < 2.2e-16

# Now using a heteroskedasticity consistent covariance matrix estimator
drop_test(mod, vce = "HC")
#> 
#>  Wald test for redundant variables
#> 
#> data:  Test for redudancy of all covariates.
#> 
#> Call:
#>   lm(price ~ sqrft + bdrms + colonial, data = hprice1)
#> 
#> Covariance matrix estimate: HC.
#> 
#> 
#> F = 16.749, df1 = 3, df2 = 84, p-value = 1.289e-08

# Heteroskedasticity robust rest test on the joint signifinace of 
# bdrms and colonial
drop_test(mod, ~ bdrms + colonial, vce = "HC")
#> 
#>  Wald test for redundant variables
#> 
#> data:  Test for redudancy of ~bdrms + colonial.
#> 
#> Call:
#>   lm(price ~ sqrft + bdrms + colonial, data = hprice1)
#> 
#> Covariance matrix estimate: HC.
#> 
#> 
#> F = 1.3769, df1 = 2, df2 = 84, p-value = 0.258

jcpernias/ec1027 documentation built on Dec. 20, 2021, 10:03 p.m.