6
votes
Accepted
What is a good proxy for government quality?
Using corruption is part of it but a bit restrictive way to measure government "quality". You may use aggregate indicators as the one developed by the Worldwide Governance Indicators (WGI) project ...
- 6,990
5
votes
Robust Standard Errors in Fixed Effects Model (using Stata)
Use -areg- in Stata, and the standard errors will come out as in the textbook. Specifically, the command
...
- 151
5
votes
Accepted
Why we need to add firm and year relating independent variables in two-way fixed effect model?
Consider the following regression specification where, $t$ is time, $c$ is the firm, $y$ is an outcome and $x$ is a variable of interest.
$$
y_{c,t} = \alpha + \beta x_{c,t} + \varepsilon_{c,t}
$$
...
- 12.5k
4
votes
Accepted
Difference-in-differences with long time horizon and repeated treatments
This question is related to a post I addressed on CrossValidated. The "generalized" difference-in-differences (DiD) estimator is amenable to settings with multiple groups and multiple ...
- 459
4
votes
Accepted
How to justify the treatment and control groups for Difference-In-Difference with staggered implementation of laws?
[W]hy do they need to write down "adopted a leniency law at some later point of time"? Because in Korea case, the word "our sample period" means "1995-2002" already.
...
- 459
4
votes
Accepted
Fixed effects vs first difference
I do not think the premise is correct. Following Brüderland and Volker in Best & Wolf The SAGE Handbook of Regression Analysis and Causal Inference [square brackets have my remarks]:
Both ...
- 57.7k
4
votes
How to use panel data for a time series machine learning problem?
This is a question that crosses all over the place, each of these techniques are different. Here are some very loose guidelines.
As a baseline, recall that in econometrics you may have performed ...
- 3,477
3
votes
Accepted
Linear Probability Model Instead of Logit in Fixed Effects Regression
FE logit requires the idiosyncratic errors to be IID across $i$ and $t$, quite a strong assumption. Also the regressors should be strictly exogenous, but it's the same for linear FE models. In your ...
- 2,114
3
votes
How to perform unbalanced panel data regression in R?
Here the solution would depend on what you want to accomplish. Note the problem is not just that the series is unbalanced, for an ordinary unbalanced panel data-set where firms have different number ...
- 57.7k
3
votes
A question about Fixed effects estimation
Having read up on your question it seems the fixed effect is fixed. If this is indeed the case it will have zero variance and hence zero covariance with any variable.
- 29.6k
3
votes
In panel data application, when using Fama and MacBeth regression is preferable over the fixed or random effect model? thought
I cannot precisely answer your questions because I do not know which exactly regressions you want to perform as @jmbejara says and which papers are you referring to that use Fama-MacBeth regression. ...
- 405
3
votes
Accepted
When does the "stationary" problem become and issue with panel data?
The data sample is so small that formal testing for stationarity would be essentially worthless. Inspect visually your individual series for any obvious trend. This would be the case where even with a ...
- 33.9k
3
votes
Robust Standard Errors in Fixed Effects Model (using Stata)
To understand the issue let's review what is the so call robust variance-covariance matrix estimates (VCE) and the implied "robust" standard errors. The robustness is meant to allow for violations of ...
3
votes
Accepted
model design - fixed effects model for paired differences
The constant term in your final FE model has no specific meaning without further restrictions. For Stata, it is only chosen such that the (sample) mean of the estimated individual effects add up to 0. ...
- 2,114
3
votes
Up-to-date survey of panel data models
As rightly pointed out by @1muflon1@ "Panel data is nowadays quite a big field - usually you will have separate chapters for panel IV, panel logit/probit, panel time series etc".
But if you ...
- 6,990
3
votes
Accepted
How to choose between fixed and random effects using economic intuition?
Here is an example where just from an economic perspective fixed effects are better than random effects.
Suppose you have panel data and you want to regress earnings $y$ on some observable ...
- 6,990
3
votes
Can I add a variable that varies only with time in Least Squares Regression model with a time-fixed effects term?
Your model has $\beta_3 * t$, which is a linear time trend, not time dummies. If that's correct, you are controlling for only a linear trend. Because oil prices do not have a perfect linear trend, you ...
- 2,114
3
votes
Replicate Blundell and Bond (2000) results using R
3. How do we recover parameters from production function estimates (INCOMPLETE ANSWER - will be updated with how to do this in R once I have time to figure it out, or if somebody else knows...)
...
- 101
3
votes
Accepted
Converting monthly data to quarterly
I've seen them summed somewhere but I cannot exactly remember where. Ultimately I don't think that it makes much difference. The quarterly sum is just the average multiplied by three. Since local ...
- 1,278
3
votes
Accepted
Interpretation of coefficients in a regression with a lagged dependent variable
tldr If $\mathbb{E}[\varepsilon_{i,t}|X_{i,t-1}, Y_{i,t-1}] = 0$ then the coefficient $\beta_2$ is equal to:
$$
\frac{\partial \mathbb{E}[\ln Y_{i,t}|X_{i,t-1}= x_{i,t-1}, Y_{i,t-1} = y_{i,t-1}]}{\...
- 12.5k
3
votes
Accepted
Clustering of standard errors in Fixed Effects models
Consider the following specification:
$$
Y_{i,g} = X_{i,g}\beta + u_{i,g}
$$
Where the residuals have different mean across groups and have within group correlation:
$$
\begin{align*}
&\mathbb{E}(...
- 12.5k
3
votes
Pooled OLS, fixed effects and random effects yield very similar results
$$y_{it} =\beta_0 +\beta_1 x_{1it} + ...+\beta_k x_{k_it}+\alpha_i +u_{it} $$
The random effects assumption is that $E[\alpha_i +u_{it}|X]=0$ where $X$ denotes all independent variables at all time ...
- 3,768
3
votes
Accepted
What is the difference between fixed effect regression and (panel) fixed effect regression?
Fixed effects regression is one of a broader class of panel econometric methods which take advantage of panel data to difference out unobserved variables which would cause biased estimators in a naive ...
- 147
3
votes
Accepted
Diagnostic tests on models using panel data
A generic representation of the cross-country growth regression is given by
\begin{equation}
\Delta y_{it} = \beta y_{i,t-1} + x_{i,t} \psi + \alpha_i + \delta_t + \varepsilon_{i,t}
\label{...
- 1,262
2
votes
Interpreting correlation between fixed effect and explanatory variable
You have done two different things.
Your fixed-effects model captures the within-group over-time functional relationship between $debt_{it}$ and $y_{it}$ (that is, how much average difference in $y_{...
- 2,114
2
votes
Fixed Effects Estimation and Inconsistency
You can't identify the effect of oil price when Year FE are applied, since the world oil price is perfectly correlated with year Fixed Effects.
You can't identify the democracy indicator if you ...
- 1,325
2
votes
Testing for heteroskedasticity in panel data vs time series?
You can regress residual squares (from RE or FE depending on your estimation) on $X_{it} \hat\beta$ and its square using the clustered standard errors (the ...
- 2,114
2
votes
How to perform unbalanced panel data regression in R?
It would be helpful to provide a reproductible example. In the paper Panel Data Econometrics in R: The plm Package, the authors explicitly mention that economic panel datasets often happen to be ...
- 6,990
2
votes
Dealing with Missing Values in Diff-in-Diff Estimation
Just to make sure I understand:
You have a daily panel (with missing values), probably weekday only, running from 2013 to mid-2017 with $n=3$ cross-sectional units.
You believe that after 2015, ...
- 16.3k
2
votes
Accepted
How to interprete independence between treatment indicator and outcomes?
Note that this section in the book starts with the counterfactual that each agent has an outcome $y_0$ without treatment and an outcome $y_1$ with treatment. You observe only the $y_0$ of the ...
- 7,119
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