Linked Questions
17 questions linked to/from Endogeneity in forecasting
38
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4
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3k
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Are inconsistent estimators ever preferable?
Consistency is obviously a natural and important property of estimators, but are there situations where it may be better to use an inconsistent estimator rather than a consistent one?
More ...
- 12k
12
votes
3
answers
2k
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T-consistency vs. P-consistency
Francis Diebold has a blog post "Causality and T-Consistency vs. Correlation and P-Consistency" where he presents the notion of P-consistency, or presistency:
Consider a standard linear regression ...
- 68.8k
11
votes
2
answers
32k
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Understanding and interpreting consistency of OLS
Many econometrics textbooks (e.g. Wooldridge, "Econometric analysis...") simply write something similar to: "If the population model is $y = xB + u$ and (1) $\text{Cov}(X,U) = 0$; (2) $X'X$ is full ...
- 113
20
votes
2
answers
809
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Minimizing bias in explanatory modeling, why? (Galit Shmueli's "To Explain or to Predict")
This question references Galit Shmueli's paper "To Explain or to Predict".
Specifically, in section 1.5, "Explaining and Prediction are Different", Professor Shmueli writes:
In explanatory ...
- 36.1k
9
votes
2
answers
2k
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What is the relationship between minimizing prediction error versus parameter estimation error?
With the advent of statistical learning techniques, people are talking a lot about prediction error, while in classical statistics, one is focusing on parameter estimation error. What is the ...
- 3,114
2
votes
3
answers
2k
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Endogeneity testing using correlation test
I am currently testing my linear model using OLS method. The last thing I have to test is endogeneity issue. Is it enough if I test each explanatory variable for correletion with error term? Than ...
- 31
0
votes
1
answer
3k
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OLS Assumption-No correlation should be there between error term and independent variable and error term and dependent variable
My question is that does endogeneity exists if there is correlation between dependent variable and error term, but not in between error term and independent variable. So for Ex, we know there should ...
- 161
3
votes
2
answers
1k
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What is the actual definition of endogeneity?
I've been learning about endogeneity but after looking around online I've gotten more and more confused about what the definition is.
Most pages say that in a model $y=X\beta+\epsilon$ the definition ...
- 406
3
votes
1
answer
505
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endogenous regressor and correlation
In a widely cited paper by Antonakis et al. (2010), they mention:
If the relation between x and y is due, in part, to other reasons,
then x is endogenous, and the coefficient of x cannot be ...
- 497
10
votes
1
answer
593
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About the meaning of ARMA parameters
I suppose that the main scope of an econometric models should be predictive or causal inference. Following this perspective it was shown that underspecified model can perform better than the correct ...
- 5,729
3
votes
2
answers
406
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Models with low variance but high bias
If we have a classification/regression problem, when would we generally prefer to use families of models with high bias and low variance like multiple regression (logistic regression for ...
- 81
3
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0
answers
1k
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Regression: Causation vs Prediction vs Description
In my experience it seems me that the interpretation about regression, its meaning and its scope, are debatable and great confusion exist about those things. It seems me that confusions are not go ...
- 5,729
2
votes
1
answer
356
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Clarification on the assumptions $E[u|x]=0$ and the $x_i$ being fixed in repeated samples in Wooldridge Introductory Econometrics
The author is writing on the assumption $E[u|x]=0$.
The part of the text which is not clear to me is this (the red lines emphasize where the critical portions are located) :
In the first piece I don'...
- 356
2
votes
3
answers
517
views
In regression analysis, data generate model or model generate data?
I am learning regression analysis and in starting of that I have encountered two statements:
S1: model generates data
S2: data generates model
Given that one is correct, so I picked up S2, thinking ...
- 221
2
votes
0
answers
594
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Machine Learning with few observations
Is common to say that Machine Learning techniques represent are purely data driven methods, and them are effective only if we have a large amount of data. I focused here on supervised/predictive ...
- 5,729