Suppose that there's a causal effect between Ai,t & Bi,t: In other words, Ai causes effects on Bi during different time.
Ai,t = Bi,t β + ...
I employed fixed effect to run an investigation on the causal relationship among A & B , but the challenge is B has endogenous effect on A (In other words, B may coincide with other transition that affect A). As you know in such a case, ATT would be more accurate.
My question is how can I employ ATT alongside fixed effect?
If that possible, how can I code that into R?
Related
Detailed business problem:
I'm trying to solve a production scheduling business problem as below:
I have two plants producing FG A and B respectively.
Both the products consume the same Raw Material x
I need to create a 30 day production schedule looking at the Raw Material availability.
FG A and B can be produced if there is sufficient raw material available on the day.
After every 6 days of production the plant has to undergo maintenance and the production on that day will be zero.
Objective is to maximize the margin looking at the day level Raw material available and adhere to the production constraint (i.e. shutdown after every 6th day)
I need to build a linear programming to address the below problem:
Variable y: (binary)
variable z: cumulative of y
When z > 6 then y = 0. I also need to reset the cumulation of z after this point.
Desired output:
How can I build the statement to MILP constraint. Are there any techniques for solving this problem. Thank you.
I think you can model your maintenance differently. Just forbid any sequences of 7 ones for y. I.e.
y[t-6]+y[t-5]+y[t-4]+y[t-3]+y[t-2]+y[t-1]+y[t] <= 6 for t=1,..,T
This is easier than using your accumulator. Note that the beginning needs some attention: you can use historic data for this. I.e., at t=1, the values for t=0,-1,-2,.. are known.
Your accumulator approach is not inherently wrong. We often use it to model inventory. An inventory capacity is a restriction on how large the accumulated inventory can be.
I have 2 columns of data in columns AX and AY. Each value in the cell represents a categorical value, namely "Strong", "Good", "Moderate", "Weak", arranged in decreasing order of strength of evidence, i.e. Weak evidence to support... Strong evidence to support etc.
I want to create a new column that chooses the lower strength of evidence category. For e.g, if AX2 = Weak, and AY2 = Strong, AZ2 = Weak (where AZ is the new column I want the new values to fall into). Similarly, if AX3 = Good, and AY3 = Moderate, then AZ3 = "Moderate".
If there was some way for me to set a hierarchy, similar to spatial thinking concepts in GIS where the minimal value is selected, whereby the value in AZ will be the minimum value (i.e. of lower strength of evidence) between AX and AY, i.e. choosing one of the 2 cell values.
In Excel, I tried doing this using the IF, AND, OR statements, and was thinking of doing e.g. If AX = Weak and AY is any of the four, AZ = Weak. And I was thinking of repeating this for the other scenarios e.g. If AX = Moderate and AY is any of the 3 (Moderate, Good, Strong), AZ = Moderate.
(My code)
=IF(AND(AX4="Weak",OR(AY4="Weak",AY4="Moderate",AY4="Good",AY4="Strong")),"Weak"," ")
Then I realized, while currently I am attempting to fix the value for AX, while using OR functions for AY, I would have to repeat the same thing in the other direction, i.e. fix a value for AY, then using OR functions for AX, to prevent myself from excluding certain scenarios
My current code only works for creating AZ values = Weak, and when I attempted to have multiple OR functions at the start to define different scenarios, I received the error message telling me that was too many arguments.
I have come to the conclusion that the way I am attempting to perform this task is very inefficient, and would greatly value any and all advice.
Yes, often inefficient to apply Excel, designed for numbers, to text. However, possible (with 'conversion' on the fly and back again):
=CHOOSE(MAX(MATCH(AX2,{"Strong","Good","Moderate","Weak"},0),MATCH(AY2,{"Strong","Good","Moderate","Weak"},0)),"Strong","Good","Moderate","Weak")
(cross-posted at http://www.statalist.org/forums/forum/general-stata-discussion/general/1370770-margins-plot-of-treatment-effect-rather-than-y-for-values-of-a-covariate)
I'm running a multivariate regression (outcome variable is continuous, happens to be GPA). The covariate of interest is a dummy variable for treatment status; another of the covariates is a pre-score. We want to look at how the treatment effect differs at various values of pre-score. The structure of the model is not complicated:
regress GPA treatment pre_score X3 X4 X5...
What I want is a graph that shows what the treatment effect is (values of Beta1) at various values of pre-score (X2). It's straightforward to get a graph with values of the OUTCOME at various values of X2:
margins, at(pre_score= (1(0.25)5)) post
marginsplot
I have consulted an array of resources and tried alternatives using marginscontplot, coefplot with recast, the dy/dx option, and so forth. I remain unsuccessful. But this seems like something that there must be a way to do; wanting to know if a treatment effect varies for values of a control (say, income) must be common.
Can anyone direct me to the right command, or options for Margins, to output values of Beta1 (coefficient on treatment dummy), rather than of Y (GPA), at values of the pre_score?
Question was resolved at Statalist. Turns out that Margins alone can't do what I was trying to; the model needs to be run with an interaction term. Then it's simple.
I'm using Heckman Selection Model which are two consist of 2 equation. i'm using Probit as a selection equation and multiple regression as a result equation.
how can put in dummy variables in those equation ?
Do we have to make the variables into logaritmic form ?
How can I make logaritmic variables with stata ?
Thank you..
Here's an example of how you might do what you ask. The example looks at the effect of being a union member on log wages:
webuse union3
gen log_wage = ln(wage)
etregress log_wage age grade i.smsa i.black tenure, treat(union = i.south i.black tenure) twostep
etregress estimates an average treatment effect of an endogenous binary-treatment variable. In plain English, that means the "first-stage" is a probit. Estimation is by either full maximum likelihood or a two-step consistent estimator, as above.
The dummies are created on the fly by putting an i. in front of the covariates. This is called factor variable notation, and it also makes interactions a breeze. You can also do tab race, gen(d_) to create d_1, d_2, and d_3 (3 race dummies, one of which you can drop).
I am running Logit Regression in Stata.
How can I know the explanatory power of the regression (in OLS, I look at R^2)?
Is there a meaningful approach in expanding the regression with other independent variables (in OLS, I manually keep on adding the independent variables and look for adjusted R^2; my guess is Stata should have simplified this manual process)?
The concept of R^2 is meaningless in logit regression and you should disregard the McFadden Pseudo R2 in the Stata output altogether.
Lemeshow recommends 'to assess the significance of an independent variable we compare the value of D with and without the independent variable in the equation' with the Likelihood ratio test (G): G=D(Model without variables [B])-D(Model with variables [A]).
The Likelihood ratio test (G):
H0: coefficients for eliminated variables are all equal to 0
Ha: at least one coefficient is not equal to 0
When the LR-test p>.05 do not reject H0, which implies that, statistically speaking, there is no advantage to include the additional IV's into the model.
Example Stata syntax to do this is:
logit DV IV1 IV2
estimates store A
logit DV IV1
estimates store B
lrtest A B // i.e. tests if A is 'nested' in B
Note, however, that many more aspects have to checked and tested before we can conclude whether or not a logit model is 'acceptable'. For more detauls, I recommend to visit:
http://www.ats.ucla.edu/stat/stata/topics/logistic_regression.html
and consult:
Applied logistic regression, David W. Hosmer and Stanley Lemeshow , ISBN-13: 978-0471356325
I'm worried that you are getting the fundamentals of modelling wrong here:
The explanatory power of a regression model is theoretically determined by your interpretation of the coefficients, not by the R-squared. The R^2 represents the amount of variance that your linear model predicts, which might be an appropriate benchmark to your model, or not.
Identically, the presence or absence of an independent variable in your model requires substantive justification. If you want to have a look at how the R-squared changes when adding or subtracting parts of your model, see help nestreg for help on nested regression.
To summarize: the explanatory power of your model and its variable composition cannot be determined just by crunching the numbers. You first need an adequate theory to build your model onto.
Now, if you are running logit:
Read Long and Freese (Ch. 3) to understand how log likelihood converges (or not) in your model.
Do not expect to find something as straightforward as the R-squared for logit.
Use logit diagnostics on your model, just like you should be after running OLS.
You might also want to read the likelihood ratio Chi-squared test or run additional lrtest commands as explained by Eric.
I certainly agree with the above posters that almost any measure of R^2 for a binary model like logit or probit shouldn't be considered very important. There are ways to see how good of a job your model does at predicting. For example, check out the following commands:
lroc
estat class
Also, here's a good article for further reading:
http://www.statisticalhorizons.com/r2logistic