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Dear Community I have a large dataframe x as follows with common ids but different names: > x <- data.frame(ID = c(1,1,2,2,2,3,3), + Name = c("B Branch A Firm ","A Firm","B Firm","B Firm","B Firm C Branch","C Firm","C Firm A Branch") + ) > x ID Name 1 1 B Branch A Firm 2 1 A Firm 3 2

Hi all, Quick question for STI. With the following setup: class Company < AR::Base; end class Firm < Company; end Why does Firm.find(:all) return all Companies, not just those that have type==''Firm''? -- Alex

Hi, all! Anyone know an easy way to specify the following model. Panel dataset, with stock through time, by firm. I want to run a model of y on a bunch of explanatory variables, and one dummy for each firm, which is 1 for observations that come from firm i, and 0 everywhere else. I have over 200 firms (and a factor variable that contains a firm identifier). Any easy way of going about

I R-helpers #I have a data panel of thousands of firms, by year and industry and #one dummy variable that separates the firms in two categories: 1 if the firm have an auditor; 0 if not #and another variable the represents the firm dimension (total assets in thousand of euros) #I need to create two separated samples with the same number os firms where #one firm in the first have a corresponding

Hi everyone: I need to count the number of banks of each firm in my data. The firm is identified by the fiscal number. The banks of each firm appears like this: Firm Banks 500600700 Citybank 500600700 CGD 500600700 BES 500600800 Citybank 500600800 Bank1 500600900 CGD I want to obtain the following dataframe: Firm

I have a dataframe with many firm-year observations and many variables. Not all firms have information for all the years. I want another dataframe with only those firms that have information all years. This is, I want a balanced panel data, but with the maximum number of years. In my reprocucible example I want to keep firms 1,2 and 3 (period 2000 to 2004). I need your help to create a

I need help in subseting a dataframe: data1<-data.frame(year=c(2001,2002,2003,2004,2001,2002,2003,2004, 2001,2002,2003,2004,2001,2002,2003,2004), firm=c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4),x=c(11,22,-32,25,-26,47,85,98, 101,14,87,56,12,43,67,54), y=c(110,220,302,250,260,470,850,980,1010,140,870,560,120,430,670,540)) data1 I want to keep the firms where all x>0 (where there are

In a belongs_to association, is there a way to tell if the associated object was newly created? Hopefully this will explain my question: A Firm class declares has_many<http://api.rubyonrails.com/classes/ActiveRecord/Associations/ClassMethods.html#M000530>:clients and a client class declares

Hi everyone! Thank you for the help you have been given to me, and here I'm with another problem with my dataframes: I have two dataframes (with much more observations), like these: Dataframe1 Firm Year cash 500400200 2007 100 500400200 2006 200 500400200 2005 400 500400300 2007 300 500400300 2006 240 500400300 2005 120 500400400

Taking the Grunfeld data, which is built-in in R, for example, (1)How can I construct a dataset (or dataframe) that consists of the data of all firms in 1951? (2)How can I calculate the average capital in each form over the period 1951-1954? What I can imagine is to categorize the data by firm, and then select the data between 1951 and 1954 for each firm, but how can I do it? Thanks, Miao

dear R wizards:? I am wrestling with reshape.? I have a long data set that I want to convert into a wide data set, in which rows are firms and columns are years. > summary(rin) firm fyear sim1 Min. :1004.00 Min. :1964.0 Min. : -1.00000 1st Qu.:1010.00 1st Qu.:1979.0 1st Qu.: -0.14334 Median :1016.00 Median :1986.0 Median : 0.00116 Mean

Hi together I'm pretty new to R, so excuse me if it is a basic question. I have a big dataset (extract of it found in the attachment) of returns from firms. I'd like to compute the Pearson correlation of each firm with the "Market" and the corresponding p-Value. So I thought of making a list of 'cor.test's and then extract the needed values with a for loop. What I did so

Dear R users, I wish to manually demean a panel over time and entities. I tried to code the Wansbeek and Kapteyn (1989) transformation (from Baltagi's book Ch. 9). As a benchmark I use both the pmodel.response() and model.matrix() functions in package plm and the results from using dummy variables. As far as I understood the transformation (Ch.3), Q%*%y (with y being the dependent variable)

Dear R-user, a few weeks ago I consulted the list-serve with a similar question. However, my task changed a little but sufficiently to get lost again. So I would appreciate any help on the following issue. I use the plm package and work with firm-level data in a panel. I would like to eliminate all firms that do not fulfill the requirement of having an observation in every variable used for at

Hi there, I have a huge data set with multiple firms years and other firm characteristics. I want to run a regression on the dependent variable and other explanatory variables and calculate the residual terms by grouping the firms in same year and same industry. What I want to do is to divide my obseravtion into sub sample that contains the observation with same fiscal year(FYEAR=1990) and same

Hi everyone! I have a question about data processing efficiency. My data are as follows: I have a data set on quarterly institutional ownership of equities; some of them have had recent IPOs, some have not (I have a binary flag set). The total dataset size is 700k+ rows. My goal is this: For every quarter since issue for each IPO, I need to find a "matched" firm in the same

I have an elementary programming question. Could someone please point me in the right direction? I have a function which will run for thousands of companies. At each invocation, it returns 2 numbers. I plan to do something like: think_one_firm <- function(filename) { # Do stuff return(list(x=x,y=y)) } So for each of the firms in my dataset, I will call

Dear R users I get different F-statistic results for a "within" model, when using "time" or "twoways" effects in plm() [1] and when manually specifying the time control dummies [2]. [1] vignette("plm") [2] http://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf Two examples below: library("AER") data("Grunfeld", package =

Hi R-helpers, My real data is a panel (unbalanced and with gaps in years) of thousands of firms, by year and industry, and with financial information (variables X, Y, Z, for example), the number of firms by year and industry is not always equal, the number of years by industry is not always equal. #reproducible example firm1<-sort(rep(1:10,5),decreasing=F) year1<-rep(2000:2004,10)

Dear R users. I have zoo object "size_june" containing market-capital values: > dim(size_june) # market-cap data of 625 firms for 20 years [1] 20 625 > class(size_june) [1] "zoo" > size_june # colnames = "size.firmcode" size.34020 size.4710 size.11050 size.10660 size.9540 size.8060 size.16160 size.8080 size.9280 1988-06-30 NA