panel-data

问题 I am running a panel regression using 'plm' function using the following code: test_reg=plm(y~x1+x2+x3+x4*x7+x5*x7+x6*x7+x8+x9+x10+x11,DATA, index = c("year","id"),model ="within") summary(test_reg) Then I get the following error: Error in names(y) <- namesy : 'names' attribute [343] must be the same length as the vector [0] However, when I switch the y variable and x10 variable and run the same 'plm' function again, I do not get such an error and it works well like: test_reg=plm(x10~x1+x2+x3

问题 I'm using the linearmodels package to estimate a Panel-OLS. As an example see: import numpy as np from statsmodels.datasets import grunfeld data = grunfeld.load_pandas().data data.year = data.year.astype(np.int64) # MultiIndex, entity - time data = data.set_index(['firm','year']) from linearmodels import PanelOLS mod = PanelOLS(data.invest, data[['value','capital']], entity_effect=True) res = mod.fit(cov_type='clustered', cluster_entity=True) I want to export the regression's output in a .tex

问题 I have a DataFrame where rows represent time and columns represent individuals. I want to turn it into into long panel data format in pandas in an efficient manner, as the DataFames are rather large. I would like to avoid looping. Here is an example: The following DataFrame: id 1 2 date 20150520 3.0 4.0 20150521 5.0 6.0 should be transformed into: date id value 20150520 1 3.0 20150520 2 4.0 20150520 1 5.0 20150520 2 6.0 Speed is what's really important to me, due to the data size. I prefer it

问题 I have a time series and panel data data frame with a specific ID in the first column, and a weekly status for employment: Unemployed (1), employed (0). I have 261 variables (the weeks every year) and 1.000.000 observations. I would like to count the maximum number of times '1' occurs consecutively for every row in R. I have looked a bit at rowSums and rle(), but I am not as far as I know interested in the sum of the row, as it is very important the values are consecutive. You can see an

问题 I'm trying to estimate a logistic unit fixed effects model for panel data using R. My dependent variable is binary and measured daily over two years for 13 locations. The goal of this model is to predict the value of y for a particular day and location based on x. zero <- seq(from=0, to=1, by=1) ids = dplyr::data_frame(location=seq(from=1, to=13, by=1)) dates = dplyr::data_frame(date = seq(as.Date("2015-01-01"), as.Date("2016-12-31"), by="days")) data = merge(dates, ids) data$y <- sample(zero

问题 This question was migrated from Stack Overflow because it can be answered on Cross Validated. Migrated 4 days ago . I am doing a fixed effect model to research on support’s effect on reducing number of injured employees. I have a dataset on company level from 2012-2020: year average age total salary total number of employees Segment Industry Risk Index support total number of Injured employees company A ID 2012 45 5 Million 55 S IT 1 0 1 company B ID 2012 48 40M 500 B Service 3 0 20 Data

问题 This question was migrated from Stack Overflow because it can be answered on Cross Validated. Migrated 4 days ago . I am doing a fixed effect model to research on support’s effect on reducing number of injured employees. I have a dataset on company level from 2012-2020: year average age total salary total number of employees Segment Industry Risk Index support total number of Injured employees company A ID 2012 45 5 Million 55 S IT 1 0 1 company B ID 2012 48 40M 500 B Service 3 0 20 Data

问题 This question was migrated from Stack Overflow because it can be answered on Cross Validated. Migrated 10 days ago . I have got a panel data in R library(AER) data(Fatalities) # define the fatality rate Fatalities$fatal_rate <- Fatalities$fatal / Fatalities$pop * 10000 # mandadory jail or community service? Fatalities$punish <- with(Fatalities, factor(jail == "yes" | service == "yes", labels = c("no", "yes"))) I am observing beertax’s effect to their fatal_rate from 1982-1988 within 48 states

问题 I'm migrating from Stata to R ( plm package ) in order to do panel model econometrics. In Stata, panel models such as random effects usually report the within, between and overall R-squared. I have found that the reported R-squared in the plm Random Effects models corresponds to the within R squared. So, is there any way to get the overall and between R-squared using the plm package in R? See same example with R and Stata: library(plm) library(foreign) # read Stata files download.file('http:/

问题 I'm migrating from Stata to R ( plm package ) in order to do panel model econometrics. In Stata, panel models such as random effects usually report the within, between and overall R-squared. I have found that the reported R-squared in the plm Random Effects models corresponds to the within R squared. So, is there any way to get the overall and between R-squared using the plm package in R? See same example with R and Stata: library(plm) library(foreign) # read Stata files download.file('http:/