0
votes
0answers
7 views

Choice of dependent variable: Differencing or Controlling?

I was running some analysis where I suspect that a treatment $D$, has opposite effects on two variables $Y^A$ and $Y^B$. To show that, I was thinking about two strategies: 1. Differencing Running ...
4
votes
1answer
107 views

Computing inverse probability weights — conditional (multivariate) density estimation?

The general version: I need to estimate $f(A | X)$ where $A$ and $X$ are continuous and multivariate. I'd rather do it nonparametrically because I don't have a good functional form in mind and ...
3
votes
2answers
58 views

Graphical and Statistical Tests for Robustness of Sharp RD

I'm doing sharp regression discontinuity design with my treatment variable $$ D_i = \begin{cases} 1 \enspace \quad \text{if $x_i \geq \overline{x}$} \\ 0 \quad \text{otherwise} \end{cases} $$ where ...
0
votes
1answer
48 views

Correlation and regression [duplicate]

Can there be negative correlation but the regression line has a positive change when there is an increase in the independent variable?
6
votes
1answer
124 views

How do instrumental variables address selection bias?

I'm wondering how an instrumental variable addresses selection bias in regression. Here's the example I'm chewing on: In Mostly Harmless Econometrics, the authors discuss and IV regression relating ...
2
votes
0answers
31 views

Under what circumstances is regression discontinuity preferred to differences in differences?

I am looking at the adoption of universal health care coverage for children age 6 and under in a country. The take-up rate was practically 100%. I want to know the impact of having coverage on ...
3
votes
0answers
52 views

Estimating a confidence interval around the average of an interacted treatment effect

I've got a causal inference situation with heterogeneous treatment effects. In addition to simply estimating coefficients, I'd like to get an average effect of the treatment at the covariate values ...
2
votes
3answers
81 views

How to rule out double causation

I have a model where I suspect (purely on theoretical grounds) that double causation might be an issue. How can I test this hypothesis? I.e. I have something like $Y_i = \beta_0 + \beta_1 X_i + ...
2
votes
1answer
67 views

Why is the conditional mean of the reduced form error zero?

For example, we have a simultaneous equation model of supply and demand: Supply: $$s(p)=\alpha_{s}+\beta_{s}p+\epsilon_{s}$$ Demand: $$d(p)=\alpha_{d}-\beta_{d}p+\epsilon_{d}$$ Market clearing ...
2
votes
1answer
102 views

Omitted Variable Bias, verification in Gretl

I am trying to verify the expression for Omitted Variable Bias (OVB) as given e.g. in Wooldridge: $\tilde{\beta_1} = \hat{\beta_1} + \hat{\beta_2} \cdot \tilde{\delta_1}$, where $\tilde{\delta_1}$ is ...
0
votes
1answer
92 views

Two highly correlated variables where both correlate with a third: Correlation and Causation

My question is certainly quite basic for statisticians! Let's suppose Var1 and Var2 are highly correlated with a poor $R^2$. ...
0
votes
0answers
39 views

Use of logit simulation for impact assessment or establishing causality

My question is that for establishing causality, omitted variable bias is important in regression specification. When relevant variables are omitted, the intervention effect or variable under ...
4
votes
2answers
424 views

Does regression analysis measure cause and effect?

Does regression analysis measure cause and effect? If yes, then how? If no, then what is done? Please describe with an example.
3
votes
2answers
101 views

What to do in logistic regression if you have a huge amount of variables?

I am dealing with logistic regression, trying to identify variables which have a causal relationship with a binary response. The way I usually do it is to try variables one by one and visualize the ...
8
votes
4answers
4k views

What do “endogeneity” and “exogeneity” mean substantively?

I understand that the basic definition of endogeneity is that $$ X'\epsilon=0 $$ is not satisfied, but what does this mean in a real world sense? I read the Wikipedia article, with the supply and ...
1
vote
0answers
69 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
207 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
93 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
403 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
123 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
208 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
2k 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
607 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
109 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
305 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
282 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
2k 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 ...
13
votes
3answers
7k 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
730 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
549 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
131 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
115 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 ...
2
votes
3answers
1k 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
10k 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 ...
11
votes
4answers
10k 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 ...
15
votes
4answers
5k 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 ...