16 votes
Accepted

Is it allowed to use averages on a dataset to improve correlation?

Let's have a look at two vectors, the first being 2 6 2 6 2 6 2 6 2 6 2 6 and the second vector being ...
Ferdi's user avatar
  • 5,179
10 votes

Difference between Multivariate Time Series data and Panel Data

In short, there is no such thing as Multivariate Time Series data. The only classic data types out there are: Cross Sections, Time Series, Pooled Cross Sections, and Panel data. Panel data is ...
ColorStatistics's user avatar
10 votes

Is it allowed to use averages on a dataset to improve correlation?

Averaging can be attractive or convenient. It can also be a source of deception, at worst deceit, so tread carefully even when there is a clear rationale for averaging. Here is a situation it which ...
Nick Cox's user avatar
  • 56.5k
5 votes

Can I use the fixed effects model on cross-sectional data?

The basic fixed effect model is something like: $$y_{ij} = \boldsymbol{\beta} \cdot \mathbf{x}_{ij} + u_j + \epsilon_{ij}$$ Typically, either $j$ or $i$ is indexing over time, but there's no reason ...
Matthew Gunn's user avatar
  • 22.4k
4 votes

Panel Regression vs. XGBoost Time Series Features

When you transform the data as you describe, the problem is that the rows in your data matrix no longer represent independent samples. While users may plausibly be assumed to be independent samples, ...
mloning's user avatar
  • 518
4 votes

How I can compare three models (nested) using lavaan with R?

SEM is (mean- and) covariance-structure analysis, so models are only comparable when fitted to the same data (i.e., the covariance matrix that the model reproduces must include all the same variables)....
Terrence's user avatar
  • 2,138
4 votes

Books reference for econometrics for PhD that focus on the programming aspect

A very popular Econometrics textbook is Econometric Analysis by W. Greene, now in its 8th edition, ISBN-13: 9781292231150. It is written in a crystal clear style and is essentially complete in terms ...
utobi's user avatar
  • 11.8k
3 votes
Accepted

Fitting Beta Distribution Parameters to Y conditional on X

Beta regression as proposed by Ferrari & Cribari-Neto (2004, Journal of Applied Statistics) can be used to model the parameters of a beta regression by linear predictors. Specifically, the beta ...
Achim Zeileis's user avatar
3 votes
Accepted

Cross sectional regression in excel

There is probably some built-in way to do it, but you can always do linear regression step by step with matrices. If your dependent variable is a column $Y$ and your independent variables are a set of ...
suckrates's user avatar
  • 1,086
3 votes

Choosing the right stats textbook - graduate level

A general advise would be going online on university statistics department websites and on individual class pages and looking at their recommended textbooks. Here is the graduate level probability ...
3 votes

a high correlation coefficient between the dependent variable and a control variable

If it's highly correlated with the outcome and not correlated with the other predictors, you should almost certainly include it, as it increases the power to detect the effect of other other ...
Jeremy Miles's user avatar
  • 17.9k
3 votes
Accepted

Study seems to show inconsistencies in study population

I believe the explanation is this (haven't read the whole paper): The "Statistical Analysis" section starts with "To represent the general Korean population with minimal bias, sampling ...
Christian Hennig's user avatar
2 votes

When is cross-sectional data better than panel data?

Never, you can always get the same information from a panel as from cross sectional data (just by discarding the additional years). I can think of no situation where I would prefer CS data over panel ...
Repmat's user avatar
  • 3,562
2 votes
Accepted

How do I calculate sample size and What is the is the meaning of power in a cross-sectional study?

Firstly: My calculations yield a required sample size of $239$ (see below for more details). Secondly: In this context, the concept of power is not needed at all. Power is only needed in the context ...
elmo's user avatar
  • 559
2 votes
Accepted

How do I interpret the slope coefficient of a variable expressed in percentage terms?

It depends on how $X$ variable is entered in the model - since it must have been entered as a number. If it has been entered as 1 unit = 1% (that is, 28% appears as 28 in your dataset), the slope is ...
Pere's user avatar
  • 6,633
2 votes
Accepted

durbinWatsonTest() in R for non time-series data

Autocorrelation is only meaningful when the data is ordered, such as in time series that are naturally ordered along the time scale, or when the distance between the observations is meaningful, such ...
Richard Hardy's user avatar
2 votes

What's the difference between an ecological study and a cross-sectional study?

An ecological study is one where you take values of a variable for an entire population, the outcome from that population, and use that to draw inference. A cross-sectional study is where you look at ...
Fomite's user avatar
  • 23.2k
2 votes

Does the sum of correlations make sense?

No, I recommend against that. Correlation coefficients are not on an interval scale, so summing, say, .5+.5 will reflect something different from summing .2 + .8, much less 0+1.0. If you want to do ...
Patrick Malone's user avatar
2 votes

"Matching" with cross-sectional studies: are the samples dependent?

Matching creates complexity in analyses, which is why you see many researchers actually ignore the matching when the analysis is done. Not good. You can see from your question that matching creates ...
Frank Harrell's user avatar
2 votes

longitudinal vs. cross-sectional vs. panel analyses

Panel data and longitudinal data are the same thing - the former terminology is more common in econometrics. Cross-sectional data are all collected at the same time. Therefore you have longitudinal /...
Robert Long's user avatar
  • 60.9k
2 votes
Accepted

Why does SUR improve efficiency of parameter estimation over OLS?

Your fallacy is thinking that "efficiency" merely boils down to number of observations. SUR does not "utilize more observations" than OLS. The relative efficiency of two estimators is the ratio of ...
AdamO's user avatar
  • 62.7k
2 votes

How many observations should a dummy variable encompass?

There is no minimum sample size requirement, although problems can ensue with smaller sample sizes. For example, normality becomes more and more important for inference as the sample size reduces. ...
BigBendRegion's user avatar
2 votes

How can I control for unobserved heterogeneity with cross-sectional data?

You are looking at a cross-sectional regression with non-time related subgroups, say, linear $$y_{is} = \mathbf x_{si}'\beta + \mathbf z_{si} + \mathbf g_s+ v_{si}$$ where $s$ indicates the subgroup (...
Alecos Papadopoulos's user avatar
2 votes
Accepted

Entropy balancing what are the gains in applying the technique?

Entropy balancing is a method of equating two groups of units on a specified set of background variables. Conceptually, it is the same thing as matching or inverse probability weighting; indeed, it's ...
Noah's user avatar
  • 33.5k
2 votes
Accepted

Pooled cross-section or very highly unbalanced panel data?

This is not panel data, as a longitudinal panel design implies to follow the same individuals over time. For example, in An Overview of Longitudinal Research Designs in Social Sciences, Jyoti Bala ...
J-J-J's user avatar
  • 4,243
2 votes
Accepted

Books reference for econometrics for PhD that focus on the programming aspect

I am not aware of any book that is rigorous as well as deals with the programing aspect. That doesn't mean they are complementary. Hamilton deals with the time series from econometrics point of view. ...
User1865345's user avatar
  • 8,327
1 vote
Accepted

Significance of a variable - Regression Model alternatives

Actually regression analysis is generally not a good approach to say anything about influence. It is never free of omitted variable problem. Regression analysis might tell only about correlation, and ...
cure's user avatar
  • 1,844
1 vote

Constrained Linear Regression with multiple factors in R

You can use the pcls() function in the mgcv package. pcls stands for partially constrained ...
Stephan Kolassa's user avatar
1 vote

An Auto-correlated Cross-section?

Actually, there is a priori always a metric space whose distances can be used to model an autocovariance structure. In time series, the metric space is time, one-dimensional anisotropic. In your ...
keepAlive's user avatar
  • 1,019
1 vote
Accepted

Adjust for one factor in time series analysis

Just to be clear are you collecting repeated measures on the same individuals? If so you could create a variable for each season (let's say you measured BP four times a season). Now let's say you had ...
Matt Barstead's user avatar

Only top scored, non community-wiki answers of a minimum length are eligible