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Linear Probability Models and Big Data: Prediction, Inference and Selection Bias
Suneel ChatlaGalit Shmueli
Institute of Service ScienceNational Tsing Hua University Taiwan
Outline Introduction to binary outcome models
Motivation : Rare use of LPM
Study goals
o Estimation and inference
o Classification
o Selection bias
Simulation study
eBay data – in paper
Conclusions2
3
Binary outcome models
Logit
Probit
LPM
OLS Regression:
Standard normal cdf
The purpose of binary-outcome regression models?
Inference
and estimation
Selection Bias
Prediction (Classificatio
n)
5
Summary of IS literature (MISQ,JAIS,ISR and MS: 2000~2016)
• Inference and estimation60
• Selection bias31• Classification and
prediction5
Only 8 used LPM 3 are from this year alone
6
”Implementing a campaign fixed effects model with Multinomial logit is challenging due to incidental parameter problem so we opt to employ LPM …” – Burtch et al. (2016)”The LPM is simple for both estimation and inference. LPM is fast and it allows for a reasonable accurate approximation of true preferences.” – Schlereth & Skiera (2016)
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Statisticians don’t like LPMEconometricians love LPM
Researchers rarely use LPM
WHY?
Criticisms
Non normal error
Non constant error varianceUnbounded predictions
Functional form
Logit
✔
✔
✔
✔✖
Probit
✔
✔
✔
✔✖
LPM
✖
✖
✖
✖
Comparison of three models in terms their theoretical properties
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Advantages
Convergence issues
Incidental parameters
Easier interpretation
Computational speed
Logit
✖
✖
✔
✔✖
Probit
✖
✖
✖
✔✖
LPM
✔
✔
✔
✔
Comparison in terms of practical issues
9
The Questions that Matter to Researchers?
Logit Probit LPMInference & Estimation
Classification
Selection Bias
10
Inference and
estimation• Consistency
• Marginal effects
11
Latent Framework
𝑍={ 1, 𝑖𝑓 𝒀>00 , h𝑜𝑡 𝑒𝑟𝑤𝑖𝑠𝑒
Latent continuous (not observable)
12
Inference and
estimation
𝑙𝑜𝑔𝑖𝑠 (0,1) • Logit model
𝑁 (0,1)• Probit model
1)• Linear
probability model
The MLE’s of both logit and probit are consistent.�̂� 𝑝→𝛽
LPM estimates are proportionally and directionally consistent (Billinger, 2012) .
�̂�𝑙𝑝𝑚𝑝→𝑘𝛽
n
𝑘 𝛽
𝛽�̂�
�̂�𝑙𝑝𝑚
13
Inference and
estimation
Marginal effects for interpreting effect size For LPM
ME for = =
For logit model
ME for = =
For probit model
ME for = = ()
14
Easy Interpretation
No direct Interpretation
Inference and
estimation
Simulation study• Sample sizes {50,500,50000}• Error distribution {Logistic, Normal, Uniform}
• 100 Bootstrap samples
15
Inference and
estimation
Comparison of Standard Models
16
True Logit Probit LPMIntercept 0 0 0 0.5
1 0.99 1 0.47-1 -1 -1.01 -0.430.5 0.5 0.5 0.21-0.5 -0.5 -0.5 -0.21
k=0.4
Inference and
estimation
1.02-1.070.52-0.52
Non-significance results are identical
coefficient significance results are identical
Comparison of significance
17
Inference and
estimation
Comparison of marginal effects
distributions of marginal effects are identical
18
Inference and
estimation
Classification and
prediction• Predictions
beyond [0,1]
19
Is trimming appropriate?
Replace with 0.99, 0.999
Replace with 0.001, 0.0001
Logi
t Pre
dict
ions
20
Classification and
prediction
Classification
21
Classification accuracies are
identical
Classification and
prediction
Selection Bias
22
Quasi-experiments
Like randomized experimental designs that test causal hypotheses but lack random assignment
Treatment Assignment
● Assigned by experimenter
● Self selection
23
Selection Bias
Selection BiasTwo-Stage (2SLS) Methods
Stage 1:Selection model (T)
AdjustmentStage 2:Outcome model (Y)
𝐸 [𝑇∨𝑋 ]=Φ (𝑋 𝛾) 𝐼𝑀𝑅=𝜙 (𝑋𝛾)Φ (𝑋 𝛾)
(Heckman, 1977)
𝐸 [𝑇∨𝑋 ]=𝑋 𝛾 𝜆=𝑋𝛾−1(Olsen, 1980)
Probit
LPM24
Selection Adjustment
Olsen is simpler
Selection BiasOutcome model coefficients (bootstrap)
Both Heckman and Olsen’s methods perform similar to the MLE
25
Selection Bias
Bottom lineInference and
Estimation• Use LPM with
large sample; otherwise logit/probit is preferable
• With small-sample LPM use robust standard errors
Classification
• Use LPM if goal is classification or ranking
• Trim predicted probabilities
• If probabilities are needed, then logit/probit is preferable
Selection Bias
• Use LPM if the sample is large
• If both selection and outcome models have the same predictors, LPM suffers from multicollinearity
26
Thank you!
Suneel Chatla, Galit Shmueli, (2016), An Extensive Examination of Linear Regression Models with a Binary Outcome Variable, Journal of the Association for Information Systems (Accepted).
27