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tests/testthat/_snaps/isativ.md
In ivgets: General to Specific Modeling and Indicator Saturation in 2SLS Models
ivisat() works correctly
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.04674 0.12750 8.2095 1.711e-10 ***
x1 1.66156 0.13146 12.6391 < 2.2e-16 ***
x2 -1.04996 0.12300 -8.5364 5.788e-11 ***
iis16 2.37449 0.88440 2.6849 0.01012 *
iis18 -2.31272 0.86844 -2.6631 0.01070 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 1.77243 1 0.1831
Ljung-Box ARCH(1) 0.95742 1 0.3278
SE of regression 0.83540
R-squared 0.87338
Log-lik.(n=50) -59.32075
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 iis16 iis18 x2
1.047 1.662 2.374 -2.313 -1.050
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.05156 0.14129 7.4427 1.749e-09 ***
x1 1.78997 0.14246 12.5644 < 2.2e-16 ***
x2 -1.05753 0.13956 -7.5779 1.094e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 0.087717 1 0.7671
Ljung-Box ARCH(1) 1.104955 1 0.2932
SE of regression 0.94471
R-squared 0.83088
Log-lik.(n=50) -66.55617
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 x2
1.052 1.790 -1.058
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.62505 0.30676 5.2975 4.569e-06 ***
x1 1.58185 0.12494 12.6604 1.420e-15 ***
x2 -1.06560 0.11541 -9.2329 1.832e-11 ***
tis8 -1.99978 0.69895 -2.8611 0.0066820 **
tis9 2.84891 0.95191 2.9928 0.0047197 **
tis12 -1.31233 0.48133 -2.7264 0.0094566 **
tis16 3.81242 1.14748 3.3224 0.0019148 **
tis17 -5.80749 1.44999 -4.0052 0.0002618 ***
tis19 4.35102 1.25749 3.4601 0.0012976 **
tis20 -1.87398 0.76825 -2.4393 0.0192469 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 0.21839 1 0.6403
Ljung-Box ARCH(1) 0.31993 1 0.5716
SE of regression 0.76702
R-squared 0.90512
Log-lik.(n=50) -52.10632
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 tis8 tis9 tis12 tis16 tis17 tis19 tis20 x2
1.625 1.582 -2.000 2.849 -1.312 3.812 -5.807 4.351 -1.874 -1.066
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.04674 0.12750 8.2095 1.711e-10 ***
x1 1.66156 0.13146 12.6391 < 2.2e-16 ***
x2 -1.04996 0.12300 -8.5364 5.788e-11 ***
my16 2.37449 0.88440 2.6849 0.01012 *
my18 -2.31272 0.86844 -2.6631 0.01070 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 1.77243 1 0.1831
Ljung-Box ARCH(1) 0.95742 1 0.3278
SE of regression 0.83540
R-squared 0.87338
Log-lik.(n=50) -59.32075
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 my16 my18 x2
1.047 1.662 2.374 -2.313 -1.050
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.04674 0.12750 8.2095 1.711e-10 ***
x1 1.66156 0.13146 12.6391 < 2.2e-16 ***
x2 -1.04996 0.12300 -8.5364 5.788e-11 ***
my16 2.37449 0.88440 2.6849 0.01012 *
my18 -2.31272 0.86844 -2.6631 0.01070 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 1.77243 1 0.1831
Ljung-Box ARCH(1) 0.95742 1 0.3278
SE of regression 0.83540
R-squared 0.87338
Log-lik.(n=50) -59.32075
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 my16 my18 x2
1.047 1.662 2.374 -2.313 -1.050
attr(,"class")
[1] "ivisat"
isativ() works correctly with argument 'fast'
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.04674 0.12750 8.2095 1.711e-10 ***
x1 1.66156 0.13146 12.6391 < 2.2e-16 ***
x2 -1.04996 0.12300 -8.5364 5.788e-11 ***
iis16 2.37449 0.88440 2.6849 0.01012 *
iis18 -2.31272 0.86844 -2.6631 0.01070 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 1.77243 1 0.1831
Ljung-Box ARCH(1) 0.95742 1 0.3278
SE of regression 0.83540
R-squared 0.87338
Log-lik.(n=50) -59.32075
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 iis16 iis18 x2
1.047 1.662 2.374 -2.313 -1.050
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.05156 0.14129 7.4427 1.749e-09 ***
x1 1.78997 0.14246 12.5644 < 2.2e-16 ***
x2 -1.05753 0.13956 -7.5779 1.094e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 0.087717 1 0.7671
Ljung-Box ARCH(1) 1.104955 1 0.2932
SE of regression 0.94471
R-squared 0.83088
Log-lik.(n=50) -66.55617
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 x2
1.052 1.790 -1.058
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.62505 0.30676 5.2975 4.569e-06 ***
x1 1.58185 0.12494 12.6604 1.420e-15 ***
x2 -1.06560 0.11541 -9.2329 1.832e-11 ***
tis8 -1.99978 0.69895 -2.8611 0.0066820 **
tis9 2.84891 0.95191 2.9928 0.0047197 **
tis12 -1.31233 0.48133 -2.7264 0.0094566 **
tis16 3.81242 1.14748 3.3224 0.0019148 **
tis17 -5.80749 1.44999 -4.0052 0.0002618 ***
tis19 4.35102 1.25749 3.4601 0.0012976 **
tis20 -1.87398 0.76825 -2.4393 0.0192469 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 0.21839 1 0.6403
Ljung-Box ARCH(1) 0.31993 1 0.5716
SE of regression 0.76702
R-squared 0.90512
Log-lik.(n=50) -52.10632
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 tis8 tis9 tis12 tis16 tis17 tis19 tis20 x2
1.625 1.582 -2.000 2.849 -1.312 3.812 -5.807 4.351 -1.874 -1.066
attr(,"class")
[1] "ivisat"
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ivgets documentation built on Sept. 11, 2024, 6:19 p.m.
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ivisat() works correctly
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.04674 0.12750 8.2095 1.711e-10 ***
x1 1.66156 0.13146 12.6391 < 2.2e-16 ***
x2 -1.04996 0.12300 -8.5364 5.788e-11 ***
iis16 2.37449 0.88440 2.6849 0.01012 *
iis18 -2.31272 0.86844 -2.6631 0.01070 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 1.77243 1 0.1831
Ljung-Box ARCH(1) 0.95742 1 0.3278
SE of regression 0.83540
R-squared 0.87338
Log-lik.(n=50) -59.32075
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 iis16 iis18 x2
1.047 1.662 2.374 -2.313 -1.050
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.05156 0.14129 7.4427 1.749e-09 ***
x1 1.78997 0.14246 12.5644 < 2.2e-16 ***
x2 -1.05753 0.13956 -7.5779 1.094e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 0.087717 1 0.7671
Ljung-Box ARCH(1) 1.104955 1 0.2932
SE of regression 0.94471
R-squared 0.83088
Log-lik.(n=50) -66.55617
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 x2
1.052 1.790 -1.058
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.62505 0.30676 5.2975 4.569e-06 ***
x1 1.58185 0.12494 12.6604 1.420e-15 ***
x2 -1.06560 0.11541 -9.2329 1.832e-11 ***
tis8 -1.99978 0.69895 -2.8611 0.0066820 **
tis9 2.84891 0.95191 2.9928 0.0047197 **
tis12 -1.31233 0.48133 -2.7264 0.0094566 **
tis16 3.81242 1.14748 3.3224 0.0019148 **
tis17 -5.80749 1.44999 -4.0052 0.0002618 ***
tis19 4.35102 1.25749 3.4601 0.0012976 **
tis20 -1.87398 0.76825 -2.4393 0.0192469 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 0.21839 1 0.6403
Ljung-Box ARCH(1) 0.31993 1 0.5716
SE of regression 0.76702
R-squared 0.90512
Log-lik.(n=50) -52.10632
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 tis8 tis9 tis12 tis16 tis17 tis19 tis20 x2
1.625 1.582 -2.000 2.849 -1.312 3.812 -5.807 4.351 -1.874 -1.066
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.04674 0.12750 8.2095 1.711e-10 ***
x1 1.66156 0.13146 12.6391 < 2.2e-16 ***
x2 -1.04996 0.12300 -8.5364 5.788e-11 ***
my16 2.37449 0.88440 2.6849 0.01012 *
my18 -2.31272 0.86844 -2.6631 0.01070 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 1.77243 1 0.1831
Ljung-Box ARCH(1) 0.95742 1 0.3278
SE of regression 0.83540
R-squared 0.87338
Log-lik.(n=50) -59.32075
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 my16 my18 x2
1.047 1.662 2.374 -2.313 -1.050
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.04674 0.12750 8.2095 1.711e-10 ***
x1 1.66156 0.13146 12.6391 < 2.2e-16 ***
x2 -1.04996 0.12300 -8.5364 5.788e-11 ***
my16 2.37449 0.88440 2.6849 0.01012 *
my18 -2.31272 0.86844 -2.6631 0.01070 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 1.77243 1 0.1831
Ljung-Box ARCH(1) 0.95742 1 0.3278
SE of regression 0.83540
R-squared 0.87338
Log-lik.(n=50) -59.32075
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 my16 my18 x2
1.047 1.662 2.374 -2.313 -1.050
attr(,"class")
[1] "ivisat"
isativ() works correctly with argument 'fast'
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.04674 0.12750 8.2095 1.711e-10 ***
x1 1.66156 0.13146 12.6391 < 2.2e-16 ***
x2 -1.04996 0.12300 -8.5364 5.788e-11 ***
iis16 2.37449 0.88440 2.6849 0.01012 *
iis18 -2.31272 0.86844 -2.6631 0.01070 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 1.77243 1 0.1831
Ljung-Box ARCH(1) 0.95742 1 0.3278
SE of regression 0.83540
R-squared 0.87338
Log-lik.(n=50) -59.32075
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 iis16 iis18 x2
1.047 1.662 2.374 -2.313 -1.050
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.05156 0.14129 7.4427 1.749e-09 ***
x1 1.78997 0.14246 12.5644 < 2.2e-16 ***
x2 -1.05753 0.13956 -7.5779 1.094e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 0.087717 1 0.7671
Ljung-Box ARCH(1) 1.104955 1 0.2932
SE of regression 0.94471
R-squared 0.83088
Log-lik.(n=50) -66.55617
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 x2
1.052 1.790 -1.058
attr(,"class")
[1] "ivisat"
$selection
Date:
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 50
Sample: 1 to 50
SPECIFIC mean equation:
coef std.error t-stat p-value
cons 1.62505 0.30676 5.2975 4.569e-06 ***
x1 1.58185 0.12494 12.6604 1.420e-15 ***
x2 -1.06560 0.11541 -9.2329 1.832e-11 ***
tis8 -1.99978 0.69895 -2.8611 0.0066820 **
tis9 2.84891 0.95191 2.9928 0.0047197 **
tis12 -1.31233 0.48133 -2.7264 0.0094566 **
tis16 3.81242 1.14748 3.3224 0.0019148 **
tis17 -5.80749 1.44999 -4.0052 0.0002618 ***
tis19 4.35102 1.25749 3.4601 0.0012976 **
tis20 -1.87398 0.76825 -2.4393 0.0192469 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 0.21839 1 0.6403
Ljung-Box ARCH(1) 0.31993 1 0.5716
SE of regression 0.76702
R-squared 0.90512
Log-lik.(n=50) -52.10632
$final
Call:
ivreg::ivreg(formula = as.formula(fml_sel), data = d)
Coefficients:
cons x1 tis8 tis9 tis12 tis16 tis17 tis19 tis20 x2
1.625 1.582 -2.000 2.849 -1.312 3.812 -5.807 4.351 -1.874 -1.066
attr(,"class")
[1] "ivisat"
Try the ivgets package in your browser
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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