Estimators
The Basics
Regression analysis assumes a model for the data generation process of some outcome. For example, the Mincer earnings function assumes that earnings are a function of schooling and experience.
\[\begin{equation}
\begin{aligned}
\ln(\text{wage}) &= \beta_{0} + \beta_{1}\text{ schooling} + \beta_{2}\text{ experience} + \beta_{3}\text{ (experience)}^{2} + \varepsilon\\\\
\varepsilon &\sim \mathcal{N}\left(\mu, \sigma^{2}\right)
\end{aligned}
\end{equation}\]
The response variable, earnings, is usually log-normal distributed (Clementi and Gallegati 2005). In addition, earnings are non-negative and assuming the sample has positive earnings a useful transformation is to take the natural log-normal distributed response. Schooling is commonly measured in years of schooling and experience as the number of years in the labor market. The model assumes a polynomial relationship between years of experience and the log of earnings of degree two.
How could we use Econometrics.jl to analyze such a model?
We can use the RDatasets package for accessing various example datasets.
The following line loads packages for the tutorial.
using CSV, RDatasets, EconometricsWe can load the PSID dataset from the Ecdat R package.
data = RDatasets.dataset("Ecdat", "PSID")
data = data[data.Earnings .> 0 .&
data.Kids .< 98,:] # Only those with earnings and valid number of kids3652×8 DataFrame. Omitted printing of 1 columns
│ Row │ IntNum │ PersNum │ Age │ Educatn │ Earnings │ Hours │ Kids │
│ │ Int32 │ Int32 │ Int32 │ Int32⍰ │ Int32 │ Int32 │ Int32 │
├──────┼────────┼─────────┼───────┼─────────┼──────────┼───────┼───────┤
│ 1 │ 4 │ 4 │ 39 │ 12 │ 77250 │ 2940 │ 2 │
│ 2 │ 4 │ 6 │ 35 │ 12 │ 12000 │ 2040 │ 2 │
│ 3 │ 4 │ 7 │ 33 │ 12 │ 8000 │ 693 │ 1 │
│ 4 │ 4 │ 173 │ 39 │ 10 │ 15000 │ 1904 │ 2 │
│ 5 │ 5 │ 2 │ 47 │ 9 │ 6500 │ 1683 │ 5 │
│ 6 │ 6 │ 4 │ 44 │ 12 │ 6500 │ 2024 │ 2 │
│ 7 │ 6 │ 172 │ 38 │ 16 │ 7000 │ 1144 │ 3 │
⋮
│ 3645 │ 9284 │ 1 │ 34 │ 12 │ 300 │ 357 │ 2 │
│ 3646 │ 9285 │ 4 │ 41 │ 8 │ 1800 │ 410 │ 98 │
│ 3647 │ 9286 │ 2 │ 45 │ 12 │ 20080 │ 2040 │ 2 │
│ 3648 │ 9292 │ 2 │ 41 │ 6 │ 13000 │ 2448 │ 5 │
│ 3649 │ 9297 │ 2 │ 42 │ 2 │ 3000 │ 1040 │ 4 │
│ 3650 │ 9302 │ 1 │ 37 │ 8 │ 22045 │ 2793 │ 98 │
│ 3651 │ 9305 │ 2 │ 40 │ 6 │ 134 │ 30 │ 3 │
│ 3652 │ 9306 │ 2 │ 37 │ 17 │ 33000 │ 2423 │ 4 │Baseline model
model = fit(EconometricModel, # Indicates the default model
@formula(log(Earnings) ~ Educatn + Age + Age^2), # formula
data)Continuous Response Model
Number of observations: 3652
Null Loglikelihood: -5696.23
Loglikelihood: -5671.17
R-squared: 0.0136
LR Test: 50.11 ∼ χ²(3) ⟹ Pr > χ² = 0.0000
Formula: log(Earnings) ~ 1 + Educatn + Age + (Age ^ 2)
Variance Covariance Estimator: OIM
────────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
────────────────────────────────────────────────────────────────────────────────────
(Intercept) 7.14834 0.957333 7.46693 <1e-12 5.27138 9.0253
Educatn 0.00370285 0.00111874 3.30984 0.0009 0.00150944 0.00589627
Age 0.0934537 0.0493985 1.89183 0.0586 -0.00339765 0.190305
Age ^ 2 -0.000918383 0.000628258 -1.46179 0.1439 -0.00215015 0.000313389
────────────────────────────────────────────────────────────────────────────────────Frequency weights
We can also specify observation weights,
model = fit(EconometricModel,
@formula(log(Earnings) ~ Educatn + Age + Age^2),
data,
wts = :PersNum, # frequency weights
)Continuous Response Model
Number of observations: 223174
Null Loglikelihood: -6746726.41
Loglikelihood: -349853.81
R-squared: 0.0064
LR Test: 12793745.20 ∼ χ²(3) ⟹ Pr > χ² = 0.0000
Formula: log(Earnings) ~ 1 + Educatn + Age + (Age ^ 2)
Variance Covariance Estimator: OIM
────────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
────────────────────────────────────────────────────────────────────────────────────
(Intercept) 8.50901 0.131474 64.7201 <1e-99 8.25132 8.7667
Educatn 0.00117042 0.000140001 8.36006 <1e-16 0.000896017 0.00144481
Age 0.0305687 0.00686287 4.45422 <1e-5 0.0171177 0.0440198
Age ^ 2 -0.000167677 8.8448e-5 -1.89576 0.0580 -0.000341032 5.67929e-6
────────────────────────────────────────────────────────────────────────────────────Categorical variables
Categorical variables can be passed through a contrast or if the feature is coded as a categorical variables it will be handled as categorical.
model = fit(EconometricModel,
@formula(log(Earnings) ~ Educatn + Age + Age^2 + Kids),
data,
wts = :PersNum,
contrasts = Dict(:Kids => DummyCoding()))Continuous Response Model
Number of observations: 223174
Null Loglikelihood: -6746726.41
Loglikelihood: -345253.18
R-squared: 0.0466
LR Test: 12802946.46 ∼ χ²(15) ⟹ Pr > χ² = 0.0000
Formula: log(Earnings) ~ 1 + Educatn + Age + (Age ^ 2) + Kids
Variance Covariance Estimator: OIM
────────────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
────────────────────────────────────────────────────────────────────────────────────────
(Intercept) 8.55057 0.131389 65.0782 <1e-99 8.29306 8.80809
Educatn 0.000538588 0.000138739 3.88203 0.0001 0.000266663 0.000810512
Age 0.0389003 0.0068679 5.66408 <1e-7 0.0254394 0.0523613
Age ^ 2 -0.000191192 8.86411e-5 -2.15692 0.0310 -0.000364926 -1.74575e-5
Kids: 1 -0.132069 0.00828577 -15.9392 <1e-56 -0.148309 -0.115829
Kids: 2 -0.290339 0.00727643 -39.9013 <1e-99 -0.304601 -0.276078
Kids: 3 -0.529931 0.00820589 -64.5793 <1e-99 -0.546014 -0.513848
Kids: 4 -0.710859 0.011204 -63.447 <1e-99 -0.732818 -0.688899
Kids: 5 -0.491404 0.0180217 -27.2673 <1e-99 -0.526726 -0.456082
Kids: 6 -1.47718 0.0381049 -38.7663 <1e-99 -1.55187 -1.4025
Kids: 7 -0.242236 0.0609217 -3.97619 <1e-4 -0.361641 -0.122831
Kids: 8 -1.53682 0.328191 -4.68271 <1e-5 -2.18007 -0.893577
Kids: 9 -0.209419 1.13675 -0.184226 0.8538 -2.43741 2.01858
Kids: 10 0.62745 0.50838 1.23421 0.2171 -0.368963 1.62386
Kids: 98 -0.71333 0.0186111 -38.3281 <1e-99 -0.749807 -0.676853
Kids: 99 -1.02093 0.0252724 -40.3971 <1e-99 -1.07046 -0.971397
────────────────────────────────────────────────────────────────────────────────────────Absorbing features
If one only care about the estimates of a subset of features and controls such as number of kids (as categorical), one can absorb those features,
model = fit(EconometricModel,
@formula(log(Earnings) ~ Educatn + Age + Age^2 + absorb(Kids)),
data,
wts = :PersNum)Continuous Response Model
Number of observations: 223174
Null Loglikelihood: -6746726.41
Loglikelihood: -345253.18
R-squared: 0.0466
Wald: 860.46 ∼ F(3, 223158) ⟹ Pr > F = 0.0000
Formula: log(Earnings) ~ 1 + Educatn + Age + (Age ^ 2) + absorb(Kids)
Variance Covariance Estimator: OIM
──────────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
──────────────────────────────────────────────────────────────────────────────────────
(Intercept) 8.23837 0.13135 62.721 <1e-99 7.98093 8.49581
Educatn 0.000538588 0.000138739 3.88203 0.0001 0.000266663 0.000810512
Age 0.0389003 0.0068679 5.66408 <1e-7 0.0254394 0.0523613
Age ^ 2 -0.000191192 8.86411e-5 -2.15692 0.0310 -0.000364926 -1.74575e-5
──────────────────────────────────────────────────────────────────────────────────────Variance Covariance Estimators
Various variance-covariance estimators are available for continous response models.
For example,
vcov(model, HC0)4×4 LinearAlgebra.Hermitian{Float64,Array{Float64,2}}:
0.000304618 -1.6099e-8 -1.60138e-5 2.07213e-7
-1.6099e-8 2.51577e-10 6.23515e-10 -8.35769e-12
-1.60138e-5 6.23515e-10 8.46239e-7 -1.10009e-8
2.07213e-7 -8.35769e-12 -1.10009e-8 1.4367e-10vcov(model, HC1)4×4 LinearAlgebra.Hermitian{Float64,Array{Float64,2}}:
0.00030464 -1.61001e-8 -1.6015e-5 2.07228e-7
-1.61001e-8 2.51595e-10 6.2356e-10 -8.35829e-12
-1.6015e-5 6.2356e-10 8.463e-7 -1.10017e-8
2.07228e-7 -8.35829e-12 -1.10017e-8 1.4368e-10vcov(model, HC2)4×4 LinearAlgebra.Hermitian{Float64,Array{Float64,2}}:
0.000305269 -1.62242e-8 -1.60486e-5 2.07673e-7
-1.62242e-8 2.53522e-10 6.28483e-10 -8.41935e-12
-1.60486e-5 6.28483e-10 8.48106e-7 -1.10257e-8
2.07673e-7 -8.41935e-12 -1.10257e-8 1.43999e-10vcov(model, HC3)4×4 LinearAlgebra.Hermitian{Float64,Array{Float64,2}}:
0.000305927 -1.63508e-8 -1.60838e-5 2.08138e-7
-1.63508e-8 2.55503e-10 6.3349e-10 -8.48145e-12
-1.60838e-5 6.3349e-10 8.49996e-7 -1.10508e-8
2.08138e-7 -8.48145e-12 -1.10508e-8 1.44333e-10vcov(model, HC4)4×4 LinearAlgebra.Hermitian{Float64,Array{Float64,2}}:
0.000307226 -1.66052e-8 -1.61533e-5 2.09058e-7
-1.66052e-8 2.59567e-10 6.43482e-10 -8.60518e-12
-1.61533e-5 6.43482e-10 8.53731e-7 -1.11003e-8
2.09058e-7 -8.60518e-12 -1.11003e-8 1.44993e-10Longitudinal Data
Longitudinal data is when a sample contains repeated measurements of the unit of observation. This kind of data allows for various estimators that make use of such a structure.
Example,
data = RDatasets.dataset("Ecdat", "Crime") |>
(data -> select(data, [:County, :Year, :CRMRTE, :PrbConv, :AvgSen, :PrbPris]))630×6 DataFrame
│ Row │ County │ Year │ CRMRTE │ PrbConv │ AvgSen │ PrbPris │
│ │ Int32 │ Int32 │ Float64 │ Float64 │ Float64 │ Float64 │
├─────┼────────┼───────┼───────────┼──────────┼─────────┼──────────┤
│ 1 │ 1 │ 81 │ 0.0398849 │ 0.402062 │ 5.61 │ 0.472222 │
│ 2 │ 1 │ 82 │ 0.0383449 │ 0.433005 │ 5.59 │ 0.506993 │
│ 3 │ 1 │ 83 │ 0.0303048 │ 0.525703 │ 5.8 │ 0.479705 │
│ 4 │ 1 │ 84 │ 0.0347259 │ 0.604706 │ 6.89 │ 0.520104 │
│ 5 │ 1 │ 85 │ 0.036573 │ 0.578723 │ 6.55 │ 0.497059 │
│ 6 │ 1 │ 86 │ 0.0347524 │ 0.512324 │ 6.9 │ 0.439863 │
│ 7 │ 1 │ 87 │ 0.0356036 │ 0.527596 │ 6.71 │ 0.43617 │
⋮
│ 623 │ 195 │ 87 │ 0.0313973 │ 1.67052 │ 13.02 │ 0.470588 │
│ 624 │ 197 │ 81 │ 0.0178621 │ 1.06098 │ 11.35 │ 0.356322 │
│ 625 │ 197 │ 82 │ 0.0180711 │ 0.545455 │ 6.64 │ 0.363636 │
│ 626 │ 197 │ 83 │ 0.0155747 │ 0.480392 │ 7.77 │ 0.428571 │
│ 627 │ 197 │ 84 │ 0.0136619 │ 1.41026 │ 10.11 │ 0.372727 │
│ 628 │ 197 │ 85 │ 0.0130857 │ 0.830769 │ 5.96 │ 0.333333 │
│ 629 │ 197 │ 86 │ 0.012874 │ 2.25 │ 7.68 │ 0.244444 │
│ 630 │ 197 │ 87 │ 0.0141928 │ 1.18293 │ 12.23 │ 0.360825 │The Between estimator
model = fit(BetweenEstimator, # Indicates the between estimator
@formula(CRMRTE ~ PrbConv + AvgSen + PrbPris),
data,
panel = :County)Between Estimator
County with 630 groups
Balanced groups with size 7
Number of observations: 90
Null Loglikelihood: 239.56
Loglikelihood: 244.40
R-squared: 0.1029
Wald: 3.29 ∼ F(3, 86) ⟹ Pr > F = 0.0246
Formula: CRMRTE ~ 1 + PrbConv + AvgSen + PrbPris
Variance Covariance Estimator: OIM
───────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
───────────────────────────────────────────────────────────────────────────────────
(Intercept) -0.00464339 0.0186571 -0.24888 0.8040 -0.0417325 0.0324457
PrbConv -0.00354113 0.0018904 -1.87322 0.0644 -0.00729911 0.00021685
AvgSen 0.000848049 0.00116477 0.728086 0.4685 -0.00146743 0.00316353
PrbPris 0.0730299 0.0332057 2.19932 0.0305 0.0070191 0.139041
───────────────────────────────────────────────────────────────────────────────────The Within estimator (Fixed Effects)
model = fit(EconometricModel,
@formula(CRMRTE ~ PrbConv + AvgSen + PrbPris + absorb(County)),
data)Continuous Response Model
Number of observations: 630
Null Loglikelihood: 1633.30
Loglikelihood: 2273.95
R-squared: 0.8707
Wald: 0.16 ∼ F(3, 537) ⟹ Pr > F = 0.9258
Formula: CRMRTE ~ 1 + PrbConv + AvgSen + PrbPris + absorb(County)
Variance Covariance Estimator: OIM
──────────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
──────────────────────────────────────────────────────────────────────────────────────
(Intercept) 0.0314527 0.00204638 15.3699 <1e-43 0.0274328 0.0354726
PrbConv 6.65981e-6 0.0001985 0.0335507 0.9732 -0.000383272 0.000396592
AvgSen 7.83181e-5 0.000127904 0.612318 0.5406 -0.000172936 0.000329572
PrbPris -0.0013419 0.00405182 -0.331185 0.7406 -0.00930126 0.00661746
──────────────────────────────────────────────────────────────────────────────────────Absorbing the panel and temporal indicators
model = fit(EconometricModel,
@formula(CRMRTE ~ PrbConv + AvgSen + PrbPris + absorb(County + Year)),
data)Continuous Response Model
Number of observations: 630
Null Loglikelihood: 1633.30
Loglikelihood: 2290.25
R-squared: 0.8775
Wald: 0.23 ∼ F(3, 531) ⟹ Pr > F = 0.8767
Formula: CRMRTE ~ 1 + PrbConv + AvgSen + PrbPris + absorb(County + Year)
Variance Covariance Estimator: OIM
──────────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
──────────────────────────────────────────────────────────────────────────────────────
(Intercept) 0.0319651 0.00206378 15.4886 <1e-44 0.027911 0.0360193
PrbConv 7.90362e-5 0.000196089 0.403062 0.6871 -0.00030617 0.000464242
AvgSen -9.4788e-5 0.000134924 -0.702531 0.4827 -0.000359838 0.000170262
PrbPris 0.000979533 0.0040432 0.242267 0.8087 -0.00696309 0.00892216
──────────────────────────────────────────────────────────────────────────────────────The random effects model à la Swamy-Arora
model = fit(RandomEffectsEstimator, # Indicates the random effects estimator
@formula(CRMRTE ~ PrbConv + AvgSen + PrbPris),
data,
panel = :County,
time = :Year)One-way Random Effect Model
Longitudinal dataset: County, Year
Balanced dataset with 90 panels of length 7
individual error component: 0.0162
idiosyncratic error component: 0.0071
ρ: 0.8399
Number of observations: 630
Null Loglikelihood: 2225.79
Loglikelihood: 2226.01
R-squared: 0.0007
Wald: 0.15 ∼ F(3, 626) ⟹ Pr > F = 0.9291
Formula: CRMRTE ~ PrbConv + AvgSen + PrbPris
Variance Covariance Estimator: OIM
──────────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
──────────────────────────────────────────────────────────────────────────────────────
(Intercept) 0.0309293 0.00266706 11.5968 <1e-27 0.0256918 0.0361668
PrbConv -3.96634e-5 0.000198471 -0.199845 0.8417 -0.000429413 0.000350086
AvgSen 8.2428e-5 0.000127818 0.644887 0.5192 -0.000168575 0.000333431
PrbPris -0.000123327 0.00404272 -0.030506 0.9757 -0.00806226 0.00781561
──────────────────────────────────────────────────────────────────────────────────────Instrumental Variables
Instrumental variables models are estimated using the 2SLS estimator.
model = fit(RandomEffectsEstimator,
@formula(CRMRTE ~ PrbConv + (AvgSen ~ PrbPris)),
data,
panel = :County,
time = :Year)One-way Random Effect Model
Longitudinal dataset: County, Year
Balanced dataset with 90 panels of length 7
individual error component: 0.0413
idiosyncratic error component: 0.0074
ρ: 0.9691
Number of observations: 630
Null Loglikelihood: 2268.01
Loglikelihood: 2248.34
R-squared: NaN
Wald: 0.03 ∼ F(2, 626) ⟹ Pr > F = 0.9671
Formula: CRMRTE ~ PrbConv + (AvgSen ~ PrbPris)
Variance Covariance Estimator: OIM
───────────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
───────────────────────────────────────────────────────────────────────────────────────
(Intercept) 0.037747 0.0241684 1.56183 0.1188 -0.00971405 0.085208
PrbConv 1.39581e-5 0.000200244 0.0697054 0.9445 -0.000379273 0.000407189
AvgSen -0.000688924 0.00266514 -0.258494 0.7961 -0.00592262 0.00454478
───────────────────────────────────────────────────────────────────────────────────────Nominal Response Models
data = joinpath(dirname(pathof(Econometrics)), "..", "data", "insure.csv") |>
CSV.read |>
(data -> select(data, [:insure, :age, :male, :nonwhite, :site])) |>
dropmissing |>
(data -> categorical!(data, [:insure, :site]))615×5 DataFrame
│ Row │ insure │ age │ male │ nonwhite │ site │
│ │ Categorical… │ Float64 │ Int64 │ Int64 │ Categorical… │
├─────┼──────────────┼─────────┼───────┼──────────┼──────────────┤
│ 1 │ Indemnity │ 73.7221 │ 0 │ 0 │ 2 │
│ 2 │ Prepaid │ 27.8959 │ 0 │ 0 │ 2 │
│ 3 │ Indemnity │ 37.5414 │ 0 │ 0 │ 1 │
│ 4 │ Prepaid │ 23.6413 │ 0 │ 1 │ 3 │
│ 5 │ Prepaid │ 29.6838 │ 0 │ 0 │ 2 │
│ 6 │ Prepaid │ 39.4689 │ 0 │ 0 │ 2 │
│ 7 │ Uninsure │ 63.102 │ 0 │ 1 │ 3 │
⋮
│ 608 │ Prepaid │ 64.4216 │ 0 │ 1 │ 3 │
│ 609 │ Prepaid │ 69.9877 │ 0 │ 0 │ 1 │
│ 610 │ Prepaid │ 71.5209 │ 0 │ 0 │ 3 │
│ 611 │ Prepaid │ 63.4168 │ 0 │ 0 │ 2 │
│ 612 │ Prepaid │ 40.3532 │ 0 │ 1 │ 1 │
│ 613 │ Prepaid │ 55.3237 │ 1 │ 0 │ 3 │
│ 614 │ Prepaid │ 27.9836 │ 1 │ 0 │ 3 │
│ 615 │ Prepaid │ 30.59 │ 1 │ 0 │ 3 │The model automatically detects that the response is nominal and uses the correct model.
model = fit(EconometricModel,
@formula(insure ~ age + male + nonwhite + site),
data,
contrasts = Dict(:insure => DummyCoding(base = "Uninsure")))Probability Model for Nominal Response
Categories: Uninsure, Indemnity, Prepaid
Number of observations: 615
Null Loglikelihood: -555.85
Loglikelihood: -534.36
R-squared: 0.0387
LR Test: 42.99 ∼ χ²(10) ⟹ Pr > χ² = 0.0000
Formula: insure ~ 1 + age + male + nonwhite + site
───────────────────────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
───────────────────────────────────────────────────────────────────────────────────────────────────
insure: Indemnity ~ (Intercept) 1.28694 0.59232 2.17271 0.0302 0.123689 2.4502
insure: Indemnity ~ age 0.00779612 0.0114418 0.681372 0.4959 -0.0146743 0.0302666
insure: Indemnity ~ male -0.451848 0.367486 -1.22957 0.2193 -1.17355 0.269855
insure: Indemnity ~ nonwhite -0.217059 0.425636 -0.509965 0.6103 -1.05296 0.618843
insure: Indemnity ~ site: 2 1.21152 0.470506 2.57493 0.0103 0.287497 2.13554
insure: Indemnity ~ site: 3 0.207813 0.366293 0.56734 0.5707 -0.511547 0.927172
insure: Prepaid ~ (Intercept) 1.55666 0.596327 2.61041 0.0093 0.385533 2.72778
insure: Prepaid ~ age -0.00394887 0.0115993 -0.340439 0.7336 -0.0267287 0.018831
insure: Prepaid ~ male 0.109846 0.365187 0.300793 0.7637 -0.607343 0.827035
insure: Prepaid ~ nonwhite 0.757718 0.419575 1.80592 0.0714 -0.0662835 1.58172
insure: Prepaid ~ site: 2 1.32456 0.469789 2.81947 0.0050 0.401941 2.24717
insure: Prepaid ~ site: 3 -0.380175 0.372819 -1.01973 0.3083 -1.11235 0.352001
───────────────────────────────────────────────────────────────────────────────────────────────────Ordinal Response Models
data = RDatasets.dataset("Ecdat", "Kakadu") |>
(data -> select(data, [:RecParks, :Sex, :Age, :Schooling]))
data.RecParks = convert(Vector{Int}, data.RecParks)
data.RecParks = levels!(categorical(data.RecParks, ordered = true), collect(1:5))
data1827×4 DataFrame
│ Row │ RecParks │ Sex │ Age │ Schooling │
│ │ Categorical… │ Categorical… │ Int32 │ Int32 │
├──────┼──────────────┼──────────────┼───────┼───────────┤
│ 1 │ 3 │ male │ 27 │ 3 │
│ 2 │ 5 │ female │ 32 │ 4 │
│ 3 │ 4 │ male │ 32 │ 4 │
│ 4 │ 1 │ female │ 70 │ 6 │
│ 5 │ 2 │ male │ 32 │ 5 │
│ 6 │ 3 │ male │ 47 │ 6 │
│ 7 │ 1 │ male │ 42 │ 5 │
⋮
│ 1820 │ 3 │ female │ 32 │ 4 │
│ 1821 │ 4 │ male │ 21 │ 5 │
│ 1822 │ 3 │ male │ 21 │ 3 │
│ 1823 │ 3 │ male │ 52 │ 4 │
│ 1824 │ 2 │ female │ 21 │ 3 │
│ 1825 │ 5 │ female │ 21 │ 2 │
│ 1826 │ 1 │ female │ 21 │ 4 │
│ 1827 │ 4 │ male │ 21 │ 5 │The model automatically detects that the response is ordinal and uses the correct model.
model = fit(EconometricModel,
@formula(RecParks ~ Age + Sex + Schooling),
data)Probability Model for Ordinal Response
Categories: 1 < 2 < 3 < 4 < 5
Number of observations: 1827
Null Loglikelihood: -2677.60
Loglikelihood: -2657.40
R-squared: 0.0075
LR Test: 40.42 ∼ χ²(3) ⟹ Pr > χ² = 0.0000
Formula: RecParks ~ Age + Sex + Schooling
──────────────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
──────────────────────────────────────────────────────────────────────────────────────────
Age 0.00943647 0.00254031 3.71469 0.0002 0.00445424 0.0144187
Sex: male -0.0151659 0.0846365 -0.179188 0.8578 -0.181161 0.150829
Schooling -0.103902 0.0248742 -4.17711 <1e-4 -0.152687 -0.0551174
(Intercept): 1 | 2 -2.92405 0.191927 -15.2352 <1e-48 -3.30047 -2.54763
(Intercept): 2 | 3 -1.54922 0.171444 -9.03632 <1e-18 -1.88547 -1.21297
(Intercept): 3 | 4 -0.298938 0.166904 -1.79108 0.0734 -0.626281 0.0284051
(Intercept): 4 | 5 0.669835 0.167622 3.9961 <1e-4 0.341083 0.998587
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