Hi everybody,

I've just have a question for you. I'm developing a project where I need to create a model which can help the firm to decide whether to invest or not in some clients. The clients are other firms btw.

I've some financial indipendent variables and more or less 12k firms. The outcome is 1 (invest) or 0 (not invest). I was thinking the classical logistic regression could be useful, but it's maybe to simple. Do you have any suggestions?

Also, do I need to scale the data throughout a Normalization/Standardization? Are there any kaggle competions that maybe are similar to my project?

Thanks

Hi there, I'm currently writing my bachelor thesis and would really appreciate some guidance here. It was suggested to me to include Country-Fixed effects into my GLS Regression, however I am finding it quite suspicious that the R library nlme (that i use for GLS) does not have a direct way for assessing Country FE. So far in my GLS regression I have specified an autoregressive correlation structure of 1 (corAR1) and thats about it.
My probably very naive question is:
How do I take into consideration time-invariant country-specific effects in my GLS?
Using R, do i need to manually demean my variables or is there a simpler way?
Should I even consider introducing Country fixed effects in a gls in the first place?
I have 42 countries observed for 10 years.
thank you for the time you spent reading this : )

I am going to have an exam in 3 days(monday) and its about eviews and econometrics and I have 0 knowledge about them , where should I start to learn the basics and I actually mean the basics. I want to learn both econometrics and eviews . Especially eviews. Where should I start?

I want to read some interesting papers that use cointegration, simultaneous equations model, vector autoregression model, arch/garch, or arima. I want to learn hands-on how to use these models and in which situations should one use such models, for which replicating interesting papers will be helpful. Thanks in advance.

Really dumb question, but I'm so confused. I want to predict monthly inflation rate, I got all the data stationary even the covariates? Is it alright or not?

My understanding, ARX is similar to linear regression and if we transformed all the data into stationary it will be flat? In the sense that everything will revolved around the same mean? thanks... 🥲

Hello guys!

Hope this is the right place to ask this.

I am a PhD candidate in environmental economics, and last summer with a colleague of mine we ran an experiment in behavioral economics. Taking inspiration from some papers in the literature, we tried to test two nudges to see if they reduced cigarette littering in beaches. We collected the cigarettes on the ground and in the bins/ashtrays and from that we computed the dependent variable we are interested in, i.e. the daily ratio between the cigarettes on the ground vs the total. We also gathered data on some other variables affecting cigarette littering.

We applied the two treatments (the two nudges) in this way: we gathered data for a week on all the beaches without any treatment (so week 1 = pre-treatment/control for all). In the second week, we applied the first treatment on 1/3 of our beaches, the second treatment to 1/3 of our beaches, and the remaining 1/3 was the control group. On the third week, we rotated the treatments and control among the beaches. The same happened on the fourth week. In this way, we had data for both treatments and the control on every beach.

Now, looking at other studies, we could just perform an OLS regression to see if the treatments had a significant effect on littering. I was wondering however if I could apply a DiD and test in this way as well if the treatments were significant.

What do you guys think about this? Is there any other kind of analysis that I should run?

Thanks in advance :D

I was given data for three variables; X, and two explanatory variables, Y and Z. I am having trouble constructing an ADL model, as the Dickey-Fuller test seems to indicate that X and Y are non-stationary while Z is stationary. How should I proceed?

Can somebody explain to me the differences between the approach proposed by Bertrand & Mullainathan (2003) and Callway & Sant‘Anna (2021)?

Hello, I've been assigned some of my first econometrics work and trying to figure something out.

In Choudhri & Hakura (2001), they determine the exchange rate pass-through to CPI using panel data. In Deniz, Tekce & Yilmaz (2016), they also determine the marginal effect of a change in the exchange rate on inflation (basically the same thing). However, in the former they use the nominal effective exchange rate, in the later the real effective exchange rate.

I have read through both methodologies and data sections, I cannot find an explanation for why they prefer one over the other. Is there any reason for a preference? I think that using the nominal rate when explaining inflation could introduce some spurious correlation and simulteneity problems, but I am new to this and, more importantly, this explanation flies in the face of the 2001 paper (which would be a bit presumptuous of me..!)

So, is there a reason?

Hi, I have 28 months of data on sales. I need to forecast sales for the next four months. This is my first time working on something like this and because of the data limitations I'm not sure if the models and tools I use have been compromised. My questions are:

• is this enough data points for forecasting?
• how do I do a test/train split here? Is a split of 25 test, 3 train a good idea?
• are there any caveats to fitting ETS, SARIMA etc to this time series? anything else I should check out?
• are the results from applying tests of stationarity and seasonal decomposition to this data reliable?

I'd appreciate if anyone could point me to resources that could help with this, especially in python.

Thank you!

Edit: what is a good number of observations for a decent forecast? Just to get an idea of the minimum data points needed

I know it's not ideal but I have less than 30 time series observations. I'm trying to figure out what model best suits my data but before that I was checking stationarity and found that ADF test in Python tells me that the series is stationary. KPSS confirms the same, but for a similar series (ie similar movement and less than 30 sample size) they are giving contradicting results.

Are these tests reliable in such situations? What is the best way to ascertain stationarity here? Any other tips for working with a small sample size - are there any other potential problems I could encounter?

Can somebody explain to me why using TWFEDD may lead to incorrect estimates when treatment is implemented across units at different times?🙃

Hi everyone,

For a class at university, I am analyzing an article regarding credit and climate change (Lane, G. (2024). Adapting to climate risk with guaranteed credit: evidence from Bangladesh. Econometrica, 92(2), 355-386. https://doi.org/10.3982/ECTA19127) that lasts 2 years. the author attributed one credit score in 2016, and in 2017 the credit scores changed (the criteria to determine them didn't). This credit score determines the eligibility of the participants, and therefore there were different participants in 2016 and in 2017. I am unsure how to analyze this, can anyone help? Thanks

In my previous post, I said that I was using an ologit model to see how education level affects the importance people give to democracy (categorical variable). The problem is that the parallel regression assumption (using Brant test) was violated, hence I should use a generalized ologit model, which is harder to interpret (I am merely an undergraduate econ student). Should I continue with my ologit model, knowing that this assumption has been violated or should I use generalized ologit model? Thanks in advance!

Hi,

For my thesis I am estimating a model for house prices and credit constraints.

I have the following I(1) unit root variables.

Log real house prices: hp User cost: uc Proxy for credit constraints: cc

If i try to estimate a cointegration relationship in just the same period I find no cointegration. However if I start with an ADL(3,3) model I find a cointegration relationship. My problem is that when I try to rewrite it to an Error correction model, the error correction term that the software I use, do not include the lagged values and I therefore get a non stationary error correction term. If I just use the lagged residuals from the cointegration as an ecm term I find them stationary and error correcting. I am however a bit nervous that I am breaking some fundamentals by doing this as it is not the same output as the software.

I hope it makes sense and that someone can help me.

Thanks

So I have some questions, as someone who has just started my degree and know next to nothing.

Context:

The running variable is the distance from the minimum GPA score for admission. The distances are given in intervals (exception being +-0.1 margins and 0) with corresponding outcomes. The main focus of our analysis is earnings after 10 years. However, we are also given socioeconomic data (age, gender, parents earnings and education etc.). The are two main groups of applicants, one which has graduated with the degree, and one who hasnt (post 10 years). However the ones who havent, have a very high probability of being admitted to their second choice degree.

1. We are only given data points for 10 groups, 7 of which are intervals, the rest being -0.1, 0, 0.1

In actual economic research using RDD, the data points are distributed evenly along the running variable, not by intervals. Since we have so few data points, does regression even make sense? Is there any "trick" to fix this without manipulating the data?

2. We're weighing all outcomes based on the subgroups with differing distances from cutoff, respective populations. Why not also use the socioeconomic outcomes as weights for analysing future earnings for example? Is that even possible (and if so how?), would it even make sense?

- We have analysed the (population) weighted socioeconomic factors, where all but one dont have any "jumps". The one factor (parental earnings) which does, has a significant negative correlation, but just after the cutoff (so the marginal groups (+-0.1) vary a lot). Does this invalidate our assumption that the marginal groups grades are assigned "randomly"? If so, what now?

Hope this is enough info, I'm completely new to all of this, so sorry for my ignorance in advance.

I am looking for some advice with my first econometrics project that I will work on this semester. I want to keep the project manageable but also aim to make it stand out. My idea is to examine the correlation between temperature changes and the agricultural sector’s performance in wealthier vs. poorer countries over the past decades. Do you think this is feasible? I assume I’ll need to get familiar with panel regression and fixed effects models. I’m also aware there are existing studies in this area, so if things get too complex, I could use some of them as guidance. To find or create a dataset should hopefully not be too challenging.

I am currently in first year of college and I am pursuing Bachelor's degree in Economics. So my college is offering an additional course to study with Economics, and I am confused between should I choose Geography or Economics. If anyone of you could help me, it would be really great..

Number of Observations

Hello, I'm going to conduct a count data panel regression but I'm not sure if the number of observations is sufficient for good estimation. Do some of you know the general rule of thumb for number of observations in a panel data set up? I only have 390.

Also in some related studies, I happen to find that they only have around 100-150 observations. But I haven't encountered articles that states specifications for number of observations.

Pls help me out thank youu

I'm thinking about doing the undergraduate econometrics degree at Erasmus, what are the prospects after the degree and is Erasmus a good university for this undergraduate program?

Thanks in advance

I am running a lagged time series regression on petrol price data. The petrol prices have a 14 day cycle where prices steadily decrease for 13 days and then shoot back up on the 14th day. This is constant throughout the period. Underlying this is movements in the price of oil and taxes that are captured in the variable cost(t).

I want to model how prices would change with the increase to taxes, but fitting a simple lagged model would not work as there are two regimes: cutting and spiking.

When I run the lagged regression the coefficient is about 0.75 on price (t-1) and 0.25 on cost (t). Clearly rheee coefficients would not be appropriate to model the relationship in the price spiking period.

Given I know perfectly when the two regimes are, do I need to run a markov switching model? Are these models only used when the timing of regime changes are unknown? If not, do I run an error correction model instead? Or any other alternative

Thanks!

Hi, im having a few troubles beginning my econometric assignment. I need to choose a topic and elaborate a relation between variables, but the question is: Wtf am I going to study ? should I start reading papers and follow some unfinished theory or take a data set and do a correlation with some variables I like ...

Hello! Im an undergraduate student doing an ologit model about how education level can affect the importance people give to democracy. It has been very difficult, full of problems but I finally ran my regression (ologit imp_democ i.educ_level...). The problem is that I know I cannot interpret the coefficients directly, so I was thinking about using odds ratio or marginal effects. Which one is better or more convenient? I have run both of them and honestly found odds ratio to be easier to interpret, but I still have my doubts. Also, which test should I use to test heteroscedasticity? I have used White test and did not work. Thanks!

Hello! I was tasked at school with creating an econometric model of my choosing and I went with “the impact of income on household food spending” , but unfortunately I’ve run into a major issue. I am unable to find any statistical institution that publishes raw microdata sets. I dont want any study overviews or means just a data regarding an individual. My variables are: -Income -Number of people in the household -Number of children -area of residence (village-0,city-1) -Education (hs-0 bsc or higher-1) Are there any publicly available datasets that could be useful?

Hey,

I was wondering whats the best way to know what model(log lin, log log and lin log) to use when doing tests. I know log log is used for elasticity, but what about the other two?

Our instructor left the Semester mid-way and a bunch of us are pretty much left without help. Would really appreciate any help!