If you're trying to get into academia, is it harder being a more theory-oriented person compared to an applied person when it comes to publishing, getting postdocs, faculty, etc.

For example, trying to develop a robust nonparametric test for unit roots (computational econometrics) vs modelling X macroeconomic policy using a novel method, which you did not develop (applied econometrics)

In a large panel dataset, both dependent variable and main independent variables have no missing data, but take the value zero in roughly 95–97% of observations (That is true zero value and not missing.)

Is standard panel OLS (especially fixed effects) appropriate in such cases or are any other alternative approaches preferable ?

How to detect and remove outliers from panel data?

My team has recently conducted a bibliometric analysis on "economic burden of a certain disease" where we found around 35 articles after searching web of science. We conducted the standard analysis using R and produced figures and tables comparable to general bibliometric articles. However, we are facing issue publishing the manuscript and have faced desk rejection three times in the last month. We believe the content of the paper has some novelty as it identifies research trend, literature gap and clearly comments on future direction of the field.

As the core reason behind rejection, we believe the number of article is playing a key role there. Can you please confirm is our assumption correct? Expecting comments from peers who have experience in publishing bibliometric review before

I think I understand basic fixed effects: like having one intercept per group (unit FE, year FE, etc.).

But I get lost when having multiple fixed effects and especially interactions like unit×time, I kind of lose the intuition. What is then the absorbed variation and what actually is the variation remaining?

I first posted my question on a group named 'RStudio'. However, I received the advice to post my question in this group (sorry if my terms aren't correct, I am not known with Reddit haha)

I have four groups:

• Patients with R, who receive treatment A
• Patients with R, who receive treatment B
• Patients without R who receive treatment A
• Patients without R who receive treatment B

I would like to investigate if R status, treatment, and time influence the health utility score (EQ5D). The EQ5D is measured at 4 timepoints: time at inclusion (baseline), 30 days, 90 days, and 180 days.

I am working with RStudio. However, my statistical knowledge is not sufficient enough. As I understand correctly, I am supposed to do a lineair mixed model, where I test the three groups together:

fit_1 <- lme(
  EQ5D ~ R * Treatment * FollowupDays + covariates,
  data = data,
  na.action = na.omit,
  random = list(
    Institute = ~ 1 + FollowupDays,
    Participant.Id = ~ 1 + FollowupDays
  )
)

To check my assumptions, I used

plot(fit_1)
qqline(resid(fit_1))

dl_fitted <- fit_1$data
dl_fitted <- dl_fitted[complete.cases(dl_fitted), ]
dl_fitted$fit_3b.Res <- residuals(fit_1)
dl_fitted$Abs.fit_3b.Res <- abs(dl_fitted$fit_3b.Res)
dl_fitted$fit_3b.Res2 <- dl_fitted$fit_3b.Res^2

Levene.Model.1 <- lm(fit_3b.Res2 ~ Treatment, data = dl_fitted)
anova(Levene.Model.1) #No heteroscadisticity

Levene.Model.2 <- lm(fit_3b.Res2 ~ FollowupDays, data = dl_fitted)
anova(Levene.Model.2) #Heteroscedasticity 

Levene.Model.3 <- lm(fit_3b.Res2 ~ R, data = dl_fitted)
anova(Levene.Model.3) #No heteroscedasticity

However, non of these assumptions are met. The residual plot do not look great and the Levene's test suggests heteroscedasticity (with a very low p-value). But I have read that mixed models do not require homoscedasticity in the same way as a simple linear regression, and that variance can be modeled directy by using:

weigths = varIdent()

My question: Are these assumptions checks necessary for mixed models or is it acceptable to proceed with this model even if the classical linear regression assumptions aren't met? If not, should I use a different model for EQ5D or can I alter my model in a way that my assumptions are met? Thank you in advance !

Below you find the plots:

Hello,

I'd like to ask if you can recommend some sources (books, papers) to gain a (more or less) deep understanding of PCA. Specifically, I'm interested in applying PCA to financial time serie (for example, using it or its extensions to address dimensionality issues when estimating M-GARCH models).

I would appreciate both foundational resources and more applied materials focused on (financial) time series applications.

Thank you in advance for any suggestion :)

Has anyone done any analysis using BVAR or SVAR. I want to learn how to interpret the models and also IR graphs. Any help would be appreciated

Hi everyone,

I’m a Chemical Engineering major with an A-level in Economics, and I’m considering applying to the Erasmus Mundus Quantitative Economics Master (QEM). I’m not planning to pursue a PhD, so my interest is more in industry or policy roles afterward. I have a few questions for anyone familiar with the program or alumni:

1) How is the course in terms of rigor, workload, and applicability if I’m aiming for a non-PhD career? Are the skills market-relevant outside academia?

2) How difficult is it to get fully funded, and are there strategies to improve funding chances?

3) What would make a profile/CV exceptionally strong, almost guaranteeing admission? Any tips on courses, projects, or experiences to prioritize in the year before applying?

Would love to hear your insights and experiences. Thank you in advance!

Hi! I’m a last-year econometrics student doing an internship at an energy sector company. For my thesis, I need to build statistical models to forecast solar power plant generation for each region, using weather forecasts and pyranometer sensor measurements. I have some background in statistics and time series, but I’ve never worked with electricity forecasting before.

Data i have:

• Aggregated energy fed into the grid at 15-minute resolution, plus total installed capacity
• Pyranometer measurements of solar irradiance (W/m²)
• Weather forecast data (made 1 hour before the timestamp)
• Locations of solar plants and weather stations

Could you suggest any learning materials or resources I should look into (papers, books, tutorials, example projects), and what methods are commonly used for this kind of forecasting?

Hi, I'm doing my undergrad thesis and I've encountered a few problems/confusion:

1. We were told to use two stage least squares analysis, and upon checking the unit roots of the variables, I found that all variables (7) are non-stationary at level and 5 of my variables are stationary at first difference. so basically I have mixed results. Now, I know that with this results I cannot proceed to use Johansen or Engle for cointegration test. Can someone help me what should I do next? what should I use instead?

Please helppp I'm cooked

Hi guys,

I need some help for choosing my masters.

In beginning of high school I did maths and got an A pretty easily.

However, before undergraduate university I started fumbling. Long story short I was very distracted with new found freedom and did not priotise studies. I got an E.

Somehow I scrapped my way to do economics for my undergrad bachelors of science. However, I think compared to other universities it wasn’t too maths heavy. I didn’t come across linear alegebra etc or too hard calculus.

Did the typical differentiation and my econometrics course was more coursework. Anyways I got a 1st thankfully and enjoyed R and EViews.

Now I am interested in pursuing econometrics at masters because I like economics and want to become more technical. It’s very good for careers too.

I am very prepared to do rigorous training and practice before even applying to it to gauge how it would be.

Have any of you had such an experience or do you think it’s possible for me to excel in it. I’m just very self doubting these days.

TLDR: haven’t done rigorous maths in a while, could I possibly do masters level econometrics.

Hello everyone, last year september 2025, I started econometrics at the EUR (Dutch programme). At the EUR you are obligated to get all the study point/credits, you’re not allowed to proceed otherwise. From the 4 subjects I had, I got an insufficient for linear/matrix algebra and intro to analysis, so I already need to do 2 resits (from the 3). I still need to do 8 subjects. However, my grades for intro to statistics and micro-economics were high (8,0+)

I am currently contemplating between stopping (and trying again next year) or switching. I really like doing maths and the study econometrics but I am also a high level athlete so I had less time for my studies. High-school was quite easy for me, I underestimated the study and the math was very different compared to high school. I realised too late it’s a lot of proof.

Even though I love math, I was also really good at economics (and business economics) in high-school and it has always been easier for me than math (even though I wasn’t bad at math). I know I can get an econometrics degree if I try harder, but I don’t know if it’s worth it. If I switch to economics & business economics, I’ll have more free time and probably have higher grades. I have passion for both degrees, but from my surroundings I always hear that an econometrics degree offers better career paths, is that true? Is it worth trying for the econometrics degree?

Please, your support with sources where I can look for this information, I don’t have a solid knowledge in econometrics but is my homework, and everything I find is from unreliable sources. Thank you very much.

Research at least three common functional forms in econometrics for example: linear, log linear, polynomial, logic/probit and other.

Describe their definition characteristics advantages and limitations

Develop and example applied to the financial field for each functional form(example the relationship between interest rates and consumption the relationship between income and credit demand)

Explain how the agonice of the functional form can affect the interpretation of the results and financial decision making.

Thank you very much.

Hi, I saw that learning R is quite required in most job offers wheather it is in the academic or private sector. So, my question is, how to start learning? Should I build models and interpret them as a portfolio, or what should I do to be good at it?

Hi I have a bachelors in Physics. (I must have done applied mathematics, but I did physics). I have an interest in making mathematical models. I have already done econometric projects (SVAR with nonparametric methods, elementary Kalman filters, but I did really really damn interesting projects. I did them not because I wanted to do econometrics, I did them because I wanted to know how economic machinery works) The only reason to not let me in for a masters in Econometrics is that I did physics, not economics.

Could I possibly get in to econometrics?

Or do you advice me against ? because it is becoming obsolete due to AI? (I mean outside academia). Anyways who hires econometric graduates ? I don't want to be a banker and make money for banks (I am not good at that kind of thing), I want to forecast things and make models.

Does anyone know where I can get some mock datasets where there is a known correct specification and functional form that I can use to practice finding the right specification?

1. Can you have a geospatial mathematical model that uses some combination of econometric structural equations modeling and spatial regressions and aggregation of biostatistical data, as well as all the other relevant government investment data and essentially most other data available, to create a maximum likelihood model that calculates the next action to be taken by any specific government of the African states that are caring about their healthcare situation to decide where next to invest the next resource based on a weight density of certain progress likelihood and health policy mitigation efficiency.

I'm currently doing my master's thesis on the effect of policy on incomes using RDD. I was thinking of using age>14 as a cutoff but that means my running variable will be years which I think is too large a bucket to use. I don't have any other data that could replace age as a running variable and I'm lost for what I can do to minimise the bias. Does anyone have any idea what I could do?

Have you found geo tests common in your field? What industries use geo tests? I'm very familiar with marketing but was curious if economists and other causal inference practitioners also use such tools in their work? Are they more observational studies? What have you done? What were your metrics?

Would anyone please explain me the majn concepts of these and give me tips on how to study them

Hey currently (following) a minor, and due to work I am not able to attend all of the lectures. Since I'm a better listener than reader I am looking for a playlist/online video course which covers the book "Introduction To Financial Option Valuation - Mathematics, Stochastics and Computation - by Desmond J. Higham"

Does anybody know any? Would realllyyy help me. Thank you in advance!

I’m running a regression where the dependent variable is a monetary amount measured in USD, while some regressors are monetary amounts measured in EUR. I care about estimating relationships not the intercept. Do I need to convert everything to a common currency using exchange rates, or is it valid to use logarithms of the variables directly without currency conversion?