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Results tagged with regression
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user 270156
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
4
votes
Accepted
What are the pitfalls of including a continuous variable and a discretized version of the sa...
I would say that in principle there is no problem at all. You are simply making a different set of assumptions regarding the functional form.
For instance, rather than modeling as:
$$y=a_0+ a_1 * x + …
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2
votes
Interpretation of interaction terms of continuous variables with quadratic regressors
Unfortunately, you cant. And I think you already understand the intuition why. Namely, there are multiple things moving to have a "clean" interpretation of a single coefficient.
Consider the following …
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4
votes
Is it possible to use generated non-normal errors with a linear regression model
Not sure what is the question here. First of all, yes, you can simulate data using any data generating process. However, if what you want is to compare the scenario to data simulated from a normal dis …
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0
votes
Accepted
Why doesn't swapping male to female as the reference category in earnings regression change ...
Other two explanations for your problem.
When estimating models with dummies, there is a very close relationship between the "omitted/base" category, the controlled category, and the constant.
For e …
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1
vote
Is O.K to use the correlation between the values predicted by NLS and the actual values for ...
I disagree with Dshirodkar.
If you revise any econometric book, the general formulation of R2 is the squared correlation between the actual $y$ and predicted values $\hat{y}$:
$R^2=\frac{[\sum{(y-E(y) …
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1
vote
Simulating a logistic regression in R
well the problem to me seems to be related to your "latent" index.
if you create a variable ystar=b0+b1*x and look at its distribution, you will see that it will have a very large value, so that P is …
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0
votes
Accepted
Does wealth index created in stata (command: pca and predict) considers all the components w...
Not sure about your second question. You are still at the limit of KMO test, on the other hand, if those new variables make theoretical sense, I would go ahead and include them.
Regarding the first qu …
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2
votes
Accepted
Interpreting the log-likelihood in ordinary least squares linear regression
No there is nothing from that output that could have told you about the possibility of model misspecification.
The formal way to see if there is misspecification (test if the model is indeed nonlinear …
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1
vote
Accepted
Deriving marginal effects for a bivariate model: ordered probit + linear regression
Simple answer:
$$\frac{\partial y}{\partial x} = 0 $$
because y is not a function of X on your structural equation.
If you go through the reduced form (and I ll take some liberties of notation here, a …
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1
vote
Categorical variable as independent variable
you do not need to that.
simply type
regress yvar xvar i.educvar
where y is your dependent variable,
x are all other independent variables, and
educvar is your education variable in categories.
If yo …
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1
vote
Rectify Heteroskedasticity in R?
The problem you are going through is the same one I had when reading about this topic in most introductory books.
Most books indicate that WLS will address heteroskedasticity by "eliminating" it from …
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4
votes
Accepted
How does the probability weight, called a pweight in Stata, work?
For further details on how exactly weights enter the estimation, look in the helpfile for regress, go to the PDF (manual), methods and formulas, and finally weighted regression. …
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8
votes
Understanding the assumptions of Linear Regression
This assumption basically imposes restrictions on how the error affects the model and allows us to estimate a Linear Regression model, usually via OLS. … The rest of the assumptions do not have to do with the estimation of the coefficient in a Linear regression, but with the estimation of the standard errors of those coefficients, and the efficiency of …
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