panel-data

问题 I'm trying to produce a predictive model where i performed multiple pooled regressions in each year (based on previous years) and thus allow coefficients to vary across time. (This might not make sense in the sample data provided, but it is done in practice for my sample). Here is what I came up so far: I adjusted my code to a reproducible sample from the plm package: The data is structured in the following way (panel) with firm, year indexed. > head(Grunfeld) firm year inv value capital 1 1

问题 I have a xls file with data organized in long format. I have four columns: the variable name, the country name, the year and the value. After importing the data in Python with pandas.read_excel, I want to plot the time series of one variable for different countries. To do so, I create a pivot table that transforms the data in wide format. When I try to plot with matplotlib, I get an error ValueError: could not convert string to float: 'ZAF' (where 'ZAF' is the label of one country) What's the

问题 I need to use 'PerformanceAnalytics' package of R and to use this package, it requires me to convert the data into xts data. The data can be downloaded from this link: https://drive.google.com/file/d/0B8usDJAPeV85elBmWXFwaXB4WUE/edit?usp=sharing . Hence, I have created an xts data by using the following commands: data<-read.csv('monthly.csv') dataxts <- xts(data[,-1],order.by=as.Date(data$datadate,format="%d/%m/%Y")) But after doing this, it looses the panel data structure. I tried to sort

问题 I have a time-series panel dataset which is structured in the following way: df <- data.frame( year = c(2012L, 2013L, 2014L, 2012L, 2013L, 2014L), id = c(1L, 1L, 1L, 2L, 2L, 2L), c = c(11L, 13L, 13L, 16L, 15L, 15L) ) #> year id c #> 1 2012 1 11 #> 2 2013 1 13 #> 3 2014 1 13 #> 4 2012 2 16 #> 5 2013 2 15 #> 6 2014 2 15 I would like to find the cross-correlation between values in column C given their id number. Something similar to this: #> 1 2 #> 1 1 0.8 #> 2 0.8 1 I have been using dplyr

问题 I am quite new to R, my problem is as follows: I have a set of panel data organised as time series like this(only part is shown): Week_Starting Team A Team B Team C Team D 2010-01-02 1 2 3 4 2010-01-09 2 40 1 5 2010-01-16 15 <NA> 4 11 2010-01-23 25 <NA> 7 18 2010-01-30 38 <NA> 9 29 2010-02-06 <NA> <NA> 12 34 2010-02-13 <NA> <NA> 16 40 2010-02-20 <NA> <NA> 20 <NA> 2010-02-27 <NA> <NA> 15 28 2010-03-06 <NA> <NA> 20 <NA> 2010-03-13 <NA> <NA> 24 <NA> 2010-03-20 <NA> <NA> 24 <NA> 2010-03-27 <NA>

问题 I have a data frame that look as follow: import pandas as pd d = {'decil': ['1. decil','1. decil','2. decil','2. decil','3. decil','3. decil'], 'kommune': ['AA','BB','AA','BB','AA','BB'],'2010':[44,25,242,423,845,962], '2011':[64,26,239,620,862,862]} df = pd.DataFrame(data=d) Printing decil kommune 2010 2011 1. decil AA 44 64 1. decil BB 25 26 2. decil AA 242 239 2. decil BB 423 620 3. decil AA 845 862 3. decil BB 962 862 My desired output is something like this kommune year 1. decil 2. decil

问题 I have a panel data set of 10 obs. and 3 variables. (# of obs. 30 = 10 rows (= countries) * 2 columns (= migration parameters) * 1col for the respective year. My data frame consists of 3 annual data frames, so to say. How can I apply stargazer on the whole period of time by taking into account that it is a panel data set (so max N=10)? That is, R should start over after every 11th row. I'd like to get the pretty table for descriptive statistics The data set for the first three years:

问题 I am trying to test a variety of conditions in a data.table that looks like this reproducible example set.seed(17) year <- 1980 + rnbinom(10000,3,0.35) event <- rep(LETTERS, length.out=10000) z <- as.integer(runif(10000,min = 0, max = 10)) dt <- data.table(event,year,z) setkey(dt, event,year) dt <- dt[,sum(z), by=c("event","year")] V1 (which emerges from the last command) represents a count of event occurences. So the data table is an ordered array and I need to execute a variety of functions

问题 I've created two regression models using a linear panel model with plm, and a generalized panel model using poisson with the pglm package. library(plm); library(pglm) data(Unions) # from pglm-package punions <- pdata.frame(Unions, c("id", "year")) fit1 <- plm(wage ~ exper + rural + married, data=punions, model="random") fit2 <- pglm(wage ~ exper + rural + married, data=punions, model="random", family="poisson") I now want to compare the two fits graphically by plotting the fitted values in a

问题 This question already has answers here : How to join (merge) data frames (inner, outer, left, right) (13 answers) Closed 3 years ago . I have a data frame that has aggregate data over every 5 days(called a week in the dataset). > dput(Sample3) structure(list(Firm = c("ENG", "ENG", "ENG", "ENG", "ENG", "AUS", "AUS", "AUS", "AUS", "AUS", "NZ", "NZ", "NZ", "NZ", "NZ"), Week = c(1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5), TotalSales = c(200, 155, 195, 150, 155, 140, 145, 150, 155, 160, 120, 125