Tagged Questions

0
votes
3answers
52 views

What is endogeneity and what does it mean substantively? As an extension what is exogeneity?

My apologies if this is an obtuse question, I am neither a statistician nor a econometrician but as a student is empirical methods this question plagues me. I understand that $$X'\epsilon=0$$ not ...
1
vote
0answers
34 views

Counterfactuals for Variables with Negative Values

Lets imagine I have estimated the following simple linear regression model: $y_{i} = 10 + 0.5x_{i} + \varepsilon_{i} $, and want to work out the counter-factual, or what would $ y_{i}$ be in the ...
2
votes
2answers
130 views

Causality, omitted variable bias

This might be a basic question, but I want to be sure that what I'm doing is right. I have a model that suggests that variable X causes both Y and Z. When I regress Y on X, or Z on X, I get positive ...
3
votes
1answer
84 views

I have GBs of Event-Based Data. How do I figure out causation?

I have a lot of event-based data about users of our website. For example, data in the format (verb, timestamp). There's about 10 or so different verbs (call them A, B, C, etc). I'm interested in ...
0
votes
2answers
84 views

Do we need Overlap/Common Support in case of a parametric regression?

If I want to make a causal statement based on selection on observables. One typically assumes "Common Support" (/"Overlap") - which means that for any value of the confounding variables X a unit i can ...
4
votes
1answer
107 views

Why arrange variables by causality in bivariate regression?

Suppose we have variables $(X,Y)$ and we have theory tell us that $X$ $\overset{\text{cause}}{\implies} Y$. Perhaps they're time-series variables and it would be common to see something like this: ...
1
vote
1answer
110 views

How to account for a regressand affecting a regressor?

I forget the terminology, but this happens when you regress, say, $Y$ on a list of variables, and you suspect that $Y$ affects, say, $x_3$ in addition to $x_3$ affecting $Y.$ I forget how this is ...
0
votes
2answers
638 views

Proving Causality with t-test/regression

Earlier today I was discussing statistical analysis software with a colleague of mine. My colleague had primarily used SPSS in previous work for performing t-tests, anovas, manovas, and other ...
3
votes
1answer
272 views

Heckman selection model with difference-in-differences specification

Following my question on Tobit with DiD specification I am wondering if it is possible to estimate a heckman sample selection model with a Difference in Differences specification? For example in ...
2
votes
0answers
90 views

Difference in Differences: id switches between treatment and control group

In my difference in differences model firms $> x$ belong to the treatment group whereas firms$< x$ act as control. I have a two period model: In $t_1$ firm $i$ is $> x$ and thus ...
1
vote
2answers
193 views

How do I estimate a differences in differences model when the dependent variable has many zeros?

Is there any way to run an OLS difference in differences model when the dependent variable (investment) has lots of observations which are truly zero? I don´t know how to add clarifications. My ...
0
votes
1answer
177 views

Clustered standard errors in 2-period Dif-in-Dif?

in order to rectify invalid t-stats because of autocorrelation in Difference-in-Differences (DnD) models, Duflo et al (2004) propose (among other solutions) to collapse data so as to have a ...
6
votes
4answers
800 views

Why use control variables in differences-in-differences?

I have a question on the differences-in-differences approach with the following standard equation: $$ y= a + b_1\text{treat}+ b_2\text{post} + b_3\text{treat}\cdot\text{post} + u $$ where treat is a ...
5
votes
3answers
2k views

X and Y are not correlated, but X is significant predictor of Y in multiple regression. What does it mean?

X and Y are not correlated (-.01); however, when I place X in a multiple regression predicting Y, alongside three (A,B, C) other (related) variables, X and two other variables (A,B) are significant ...
0
votes
2answers
247 views

How to control for industry effects in regression?

Right now I'm working on an analysis of influence of cultural aspects on investment mode preference. However I have to control for many other factors, for example industry, since some industries, for ...
3
votes
3answers
284 views

Identifying the time lag between cause and effect

What approaches exist to observe the time lag between two variables? I need to analyze the relationship between blood pressure and some other factor, such as exercise. The data set I am drawing from ...
1
vote
2answers
117 views

Is this a valid approach to testing a hypothesis about the relationship between two variables?

I am trying to test a hypothesis I have about consolidation in the real-world market for a certain machine. (My apologies in advance for obfuscating a bit here, but some of the data is proprietary and ...
2
votes
1answer
104 views

Reporting non-causal relationships

What is the appropriate way of reporting significant regression coefficients of a multiple regression when all variables have been obtained at the same measurement occasion? Specifically, do I imply a ...
1
vote
3answers
509 views

Correlation versus cause-effect regression

I know correlation does not imply causation. I have read it nth time. (i.e. weight does not cause height etc. etc.) However, to find the effect of a moderator variable on X-Y relationship, a ...
6
votes
2answers
5k views

Interpretation of positive and negative beta weights in regression equation

I received this elementary question by email: In a regression equation am I correct in thinking that if the beta value is positive the dependent variable has increased in response to greater ...
8
votes
4answers
4k views

Does simple linear regression imply causation?

I know correlation does not imply causation but instead the strength and direction of the relationship. Does simple linear regression imply causation? Or is an inferential (t-test, etc.) statistical ...
12
votes
4answers
2k views

How are propensity scores different from adding covariates in a regression, and when are they preferred to the latter?

I admit I'm relatively new to propensity scores and causal analysis. One thing that's not obvious to me as a newcomer is how the "balancing" using propensity scores is mathematically different from ...