All Questions
3,083 questions
6
votes
1
answer
3k
views
Interaction between time-variant and time-invariant variable in FE model
I want to estimate the effect of several variables $x_{1,it}$, $x_{2,it}$, $\dots$ on $y_{it}$. All of these variables vary across countries $i$ and time $t$. I use OLS to estimate a model with ...
- 205
6
votes
1
answer
5k
views
How to assess multilevel model assumptions using residual plots
I am not clear as to how to assess if a multilevel model fit using lmer satisfies the assumptions of normality and homoscedasticity?
I have used the following <...
- 123
6
votes
1
answer
5k
views
Hausman test: the larger the sample the more significant the Hausman test statistic?
Hausman test statistic formula:
$$
H=(\beta_{f}-\beta_{r})' \left[\mathrm{Cov}(\beta_{f})-\mathrm{Cov}(\beta_{r})\right]^{-1}(\beta_{f}-\beta_{r} )
$$
where $\beta_{f}$ is the beta of fixed effects ...
- 63
6
votes
2
answers
3k
views
Correct specification of longitudinal model in lme4
I am trying to fit a multilevel longitudinal model and i have a question regarding how to specify it.
The data consist of about 8k observations collected from about 3k individuals at four time points. ...
- 1,039
6
votes
1
answer
446
views
Expected Fisher information isn't positive definite for truncated normal with heteroskedasticity
This question is about having a non-positive-definite expected Fisher information in a normal model in which observations have different dispersions.
Consider this simple normal model:
$$Y_i \sim N(\...
- 3,800
6
votes
1
answer
2k
views
Understanding Random Effects in Linear Mixed Models
I am trying to understand why random effects are useful (in Linear Mixed Models). Specifically, why are they necessary and why can't we just used fixed effect dummy variables instead?
For example, if ...
- 61
6
votes
1
answer
307
views
How to interpret my profile likelihood plots (three-level meta-analysis)?
I conducted a three-level meta-analysis to test effects of non-driving related tasks on take-over quality in highly automated driving. When it now comes to interpretation of the results, I struggle to ...
- 71
6
votes
1
answer
2k
views
Model not singular but doesn't converge what could be the reason (lme4 in R)
I'm following up on this great answer regarding running Principal Component Analysis (PCA) to uncover the reason behind lack of convergence and/or singularity for Mixed-Effects models.
My model below ...
- 4,056
6
votes
1
answer
7k
views
ICC in a multi level model with two random effects
My understanding is that intraclass correlation gives you an idea of how much variance your level two factor can explain in overall variance of the dependent variable. It is supposed to give an ...
- 163
6
votes
1
answer
4k
views
Acceptable values for the intraclass correlation coefficient (empty model)
I'm using xtmixed in Stata to test a Hierarchical Linear Model.
My problem is that variance at level 2 is about 4% of the total variance. So most of the variance is ...
- 955
6
votes
4
answers
6k
views
What regression analysis should I perform on my data and why?
I am a law student researching which factors influence the CSR (corporate social responsibility, GSE_RAW) behavior of companies. As my studies didn't offer any ...
- 798
6
votes
1
answer
9k
views
R: How to "control" for another variable in Linear Mixed Effects Regression model?
Essentially, I have two collinear variables which could be seen as either random or as fixed effects, a dependent variable I'm fitting the model to, and a variable that's assuredly a random effect.
...
- 811
6
votes
1
answer
178
views
Role of regression model fit in causal analysis
When analysing causal questions, we use DAGs that give us covariates needed for modelling. But another time we assess model fit to get the best prediction. These two approaches have different purposes ...
- 2,277
6
votes
1
answer
77
views
Doubt regarding mixed modeling format
Say, I have a dataset that looks at how many times my 5 babies chases a cat around the house . I'm trying to estimate 'y' which is the number of times the cat runs one complete round around the house ...
6
votes
2
answers
854
views
How do I interpret the coefficients of a mixed effects multilevel logistic regression differently from regular logistic model?
I am trying to wrap my head around mixed effects multilevel logistic regression. Have a look at my variables:
y: Popularity (0 = Not popular, 1 = Popular)
x1: Extraversion (Continuous)
x2: Teacher ...
- 263
6
votes
1
answer
2k
views
Where are the Wald p-values and where are the LRT ones in the resulf of mixed models? [closed]
I read, that there are many methods of determining the degrees of freedom, thus calculating the p-values for fixed effects in mixed models. I read, that the worst is the Wald test and the Log-...
- 173
6
votes
2
answers
8k
views
Fixed effects versus first difference in panel data
In panel data model, both fixed effects model and first difference remove unobserved heterogeneity. If this is the case, when which technique is more appropriate and under what circumstances?
- 96
6
votes
2
answers
3k
views
Why Generalized estimating equation (GEE) is not popular?
I am a newbie in econometric analysis.
When I was in Master and PhD course of public health field, I learned mainly about mixed model and GEE for panel data analysis.
I learned GEE is robust, does ...
- 61
6
votes
1
answer
20k
views
Difference between an "Ordinary Least Square (OLS) model" and a "Panel Fixed- Effects (FE) model"
I recently ran into a paper that described the following:
To test the robustness of each specification, we used a
difference-in-difference (DID) estimator to control for time invariant
factors ...
- 347
6
votes
1
answer
2k
views
FGLS and time fixed effects
Context: I am performing growth regressions on a panel data set in R, including individual- and time fixed effects. Estimating with OLS delivers results that seem to suffer form serial correlation. ...
6
votes
1
answer
6k
views
Coefficient sign changes in fixed effect and first-difference estimation
I have a large empirical panel, where I basically want to regress the standard deviation of (equity) returns ($y_{it}$) of firm i at time t, on leverage (equity/debt) of firm i at time t ($x_{it}$). ...
- 81
6
votes
1
answer
4k
views
Two-level hierarchical model using time-series cross sectional data?
A question from someone who is fairly new to hierarchical modeling, and I'm looking for the best approach within R, preferably with the package lme4, MCMCpack, or rjags using a BUGS document. I'm ...
- 301
6
votes
3
answers
11k
views
Creating ROC curve for multi-level logistic regression model in R
I used the functions from this link for creating ROC curve for logistic regression model. Since the object produced by glmer in ...
- 3,249
6
votes
2
answers
96
views
Appropriate regression model when variables were measured on different subjects?
Imagine the following study design: We have propagated $N$ plants. Each plant produces many seeds. From each plant, $M$ of the seeds are used to measure community composition of bacteria living within ...
- 305
6
votes
1
answer
401
views
Proper syntax for coding group level variables in mixed effect model using GLMER
I am running a multilevel log odds regression on a dataset that is at the individual level. Each individual belongs to a district, and there are both individual level variables (which vary by ...
- 63
6
votes
1
answer
375
views
Increasing multicollinearity in multilevel/hierarchical modeling?
I have a linear model with response variable $\textbf{y}$ and explanatory variable matrix $\textbf{X}$ for which coefficients $\textbf{b}$ are physically meaningful and worth estimating:
\begin{...
- 869
6
votes
1
answer
2k
views
Is it possible to use LASSO regression with multi-levlel data?
I have real-time monitoring data where participants report on a variety of variables four times per day for a month. Is it possible to use LASSO regression (e.g,. glmnet r package) with this data? I'm ...
- 63
6
votes
1
answer
2k
views
Including both individual and state fixed effects
Consider we have the following regression model:
$$y_{it}=x_{it}'\beta+\alpha_{i}+\upsilon_{it}$$
where we have data on $N$
individuals for $T$
time periods. Now, if we estimate $\beta$
by ...
- 949
6
votes
3
answers
2k
views
Whether to use a hierarchical linear model
I've been reading through Gelmans book: Data Analysis Using Regression and Multilevel/Hierarchical Models trying to learn more about how to implement hierarchal models. I have a dataset that I think ...
- 323
6
votes
1
answer
5k
views
group fixed-effects, not individual-fixed effects using plm in R
I am analyzing some data to evaluate the impact (causal effect) of a program that is delivered at group level (a village). The outcome of interest is measured at the individual level (individuals ...
- 270
6
votes
1
answer
3k
views
Difference-in-differences with no pre-treatment?
The typical difference-in-differences estimator (as fixed effects) fits a model of the form
$$
y_{it} = \alpha_i + \delta T_{it} + X_{it}'\beta + \epsilon_{it}
$$
where $T$ is some treatment that ...
- 61
6
votes
2
answers
5k
views
False discovery rate & q-values: how are q-values to be interpreted when rank of p-values is altered?
I am trying to wrap my head around False Discovery Rate, and its associated q-value; I am new to this technique, but it seems quite promising for my needs.
One sticking point I keep coming across and ...
- 452
6
votes
3
answers
477
views
Estimating correlated parameters with multi-level model
I would like to estimate a multi level model in Stata or R (using lmer) where the first level coefficients are the same for all observations, but the coefficients within observation are correlated.
...
- 958
6
votes
1
answer
2k
views
6
votes
1
answer
29k
views
Hausman Test interpretation is based on the p-value? - R output
I obtained the following output after running the Hausman test:
1) CASE 1 Hausman Test chisq = 13.943, df = 4, p-value = 0.007478 alternative hypothesis: one model is inconsistent
2) CASE 2 Hausman ...
- 61
6
votes
1
answer
2k
views
How many levels in multilevel modeling is too many?
This is pretty general, but what are the pros and cons of including additional levels in multilevel model (linear mixed model)?
I have a data containing information on multilevel administrative ...
- 141k
6
votes
1
answer
3k
views
Why is it necessary to use ML estimation instead of REML to compare multilevel linear models?
In Discovering Statistics Using SPSS 4e, Andy Field writes on p835 that:
SPSS gives you the choice of two methods for estimating the parameters in
the analysis: maximum likelihood (ML), which we ...
6
votes
1
answer
1k
views
Time varying predictors at higher aggregation levels in multilevel survival analysis
The case: I am trying to estimate event history models (also known as survival models) with time-varying predictors at two different levels of (geographical) aggregation. More precisely, I am using a ...
- 201
6
votes
1
answer
2k
views
Which model for panel data with dependent variables from [0,1]?
I'm stuck with a regression modeling problem. I have panel data where the dependent variable is a probability. Below is an excerpt from my data. The complete panel covers more countries and years, ...
- 61
6
votes
1
answer
844
views
Model for circular statistics
I am looking for advice on circular statistics. In particular, I'd like to know if any one had any advice/ references that deal with regression models for circular variables and whether it is possible ...
- 221
6
votes
0
answers
1k
views
What can I do whith this random effect conditional variance in lme4?
In the R package lme4, upon estimating a mixed-effects model I can retrieve the random effects and a corresponding variance using as.data.frame(ranef(model)). ...
- 71
6
votes
0
answers
433
views
Simple trend analysis with unbalanced & short panel data
I have the following (unbalanced) panel data: yearly sustainability ratings (ESG) of ca. 2000 individual firms over a 11-year period. The average observations per firm only covers 5.3 periods. These ...
- 61
6
votes
0
answers
2k
views
Hierarchical time-series forecasting with complex aggregation constraints
I'm trying to forecast multiple time-series with a hierarchical structure using the hts package by prof. Hyndman. However, the aggregation constraints are not sums ...
- 412
5
votes
1
answer
23k
views
5
votes
2
answers
247
views
Estimate random effect variance for power analysis in multilevel model
Is there a general convention on what variance to expect for a random intercept in a multilevel model? I need to provide an estimate for the power analysis. It is a within-subjects design. Thanks!
- 53
5
votes
1
answer
2k
views
What is the difference between mixed-effects modelling in the RStan and lme4 packages?
I've recently begun running some multilevel/hierarchical models. Initially I was using rstan/rstanarm, but then switched to the lme4 package.
Is the difference between these two packages only in the ...
- 215
5
votes
2
answers
17k
views
What is the correct way to deal with multiple fixed effects when dealing with a large number of observations in panel data regression?
I don't have much experience with panel data so I apologize in advance if this sounds ridiculous.
Let's say that I am trying to control for individual and temporal fixed effects when running a panel ...
- 53
5
votes
4
answers
552
views
Are these equivalent representations of the same hierarchical Bayesian model?
If $X$ is a categorical variable, and I am interested in the posterior distributions of $\beta_1$, where $\beta_1$ is a vector of coefficients, one for each level of X, are these equivalent models?
...
- 7,270
5
votes
2
answers
277
views
Clarification on Random Effects Structure in Linear Mixed Models in R
I am using linear mixed models to analyze a dataset with a hierarchical structure, where measurements over time (level 1) are clustered within individuals (level 2), and individuals are clustered ...
- 315
5
votes
3
answers
1k
views
Mixed models. Random slopes only, mean and group centering?
Are random intercepts a theoretical/practical prerequisite to random slopes? Why?
I have a three level (rep measures) mixed model where I wouldn't expect lvl 3 variation in initial status of outcome ...
- 71