Panel in R - Revision history

Rb: /* Set-up of Panel */

2019-03-03T22:13:33Z

‎Set-up of Panel

2016-02-05T10:41:48Z

‎Set-up of Panel

2015-07-10T08:18:08Z

2015-07-10T08:16:41Z

‎Estimation Methods

2015-07-10T08:15:35Z

‎Set-up of Panel

2015-07-09T22:12:31Z

2015-07-09T22:11:47Z

2015-07-09T21:59:39Z

2015-07-09T21:53:58Z

2015-07-09T21:50:15Z

@@ Line 36: / Line 36: @@
 The distinctive Panel feature of this dataset is that we have several periods of observations (year) for each county. At this stage R does not yet know that these are Panel Data and now we need to let it know about this feature. This is what the following function does:
-      pdata <- plm.data(mydata, index = c("county","year")) # defines the panel dimensions
+      pdata <- pdata.frame(mydata, index = c("county","year")) # defines the panel dimensions
-The first input into the <source enclose=none>plm.data</source> function is the original data frame (here <source enclose = none>mydata</source>). The second input specifies which variable indexes the individual (here <source enclose = none>"county"</source>.) and the variable which indexes the time (here <source enclose = none>"year"</source>.). Both these are collected in a list and handed over to the function as <source enclose = none>index = c("county","year")</source>.
+The first input into the <source enclose=none>pdata.frame</source> function is the original data frame (here <source enclose = none>mydata</source>). The second input specifies which variable indexes the individual (here <source enclose = none>"county"</source>.) and the variable which indexes the time (here <source enclose = none>"year"</source>.). Both these are collected in a list and handed over to the function as <source enclose = none>index = c("county","year")</source>.
 == Estimation Methods ==

@@ Line 25: / Line 25: @@
       library(plm)
       setwd("X:/ECLR/R/PanelData")              # This sets the working directory
-      # Opens mroz.csv from working directory
+      # Opens crime4.csv from working directory
       # converts variables with "." entries to num with NA instead of "."
       mydata <- read.csv("crime4.csv",na.strings = ".")

@@ Line 44: / Line 44: @@
 This is not the right place to discuss the merits of the range of different estimation methods that exist for Panel Data sets. Let's assume we want to explain variation in the dependent variable <source enclose = none>lcrmrte</source> as a function of logs of all the other variables listed above (<source enclose = none>lprbarr, lprbconv, lprbpris, lavgsen and lpolpc</source>) and time dummy variables.
-A range of estimation methods exist to make use of the panel character of data
+A range of estimation methods exist to make use of the panel character of data. Here I will only introduce pooled estimation and first difference estimation. If you want to use any of the other available methods you should consult the [http://cran.r-project.org/web/packages/plm/vignettes/plm.pdf documentation] of the plm package.
 === Pooled OLS ===

← Older revision		Revision as of 08:16, 10 July 2015
Line 43:		Line 43:

	This is not the right place to discuss the merits of the range of different estimation methods that exist for Panel Data sets. Let's assume we want to explain variation in the dependent variable <source enclose = none>lcrmrte</source> as a function of logs of all the other variables listed above (<source enclose = none>lprbarr, lprbconv, lprbpris, lavgsen and lpolpc</source>) and time dummy variables.		This is not the right place to discuss the merits of the range of different estimation methods that exist for Panel Data sets. Let's assume we want to explain variation in the dependent variable <source enclose = none>lcrmrte</source> as a function of logs of all the other variables listed above (<source enclose = none>lprbarr, lprbconv, lprbpris, lavgsen and lpolpc</source>) and time dummy variables.
		+
		+	A range of estimation methods exist to make use of the panel character of data

	=== Pooled OLS ===		=== Pooled OLS ===

@@ Line 26: / Line 26: @@
       setwd("X:/ECLR/R/PanelData")              # This sets the working directory
       # Opens mroz.csv from working directory
-      # converts variables with "." to num with NA
+      # converts variables with "." entries to num with NA instead of "."
       mydata <- read.csv("crime4.csv",na.strings = ".")

@@ Line 103: / Line 103: @@
            + lprbarr + lprbconv + lprbpris + lavgsen + lpolpc <span style="color:red">- 1</span>,
            data = pdata, <span style="color:blue">model = "fd"</span>)
-      print(summary(fd2))
+      print(summary(fd3))
 Most of this function call is identical to the above call, but for two differences. First, the method now indicates that we need a first difference estimation <span style="color:blue">model = "fd"</span>. Second, as we are taking first differences we need to estimate the model without a constant, which is why we include the term <span style="color:red">- 1</span> into the model specification.

@@ Line 101: / Line 101: @@
       fd3 <- plm(lcrmrte ~ d82 + d83 + d84 + d85 + d86 + d87
-           + lprbarr + lprbconv + lprbpris + lavgsen + lpolpc <span style="color:red">-1</span>,
+           + lprbarr + lprbconv + lprbpris + lavgsen + lpolpc <span style="color:red">- 1</span>,
            data = pdata, <span style="color:blue">model = "fd"</span>)
       print(summary(fd2))
-Most of this function call is identical to the above call, but for two differences. First, the method now indicates that we need a first difference estimation <span style="color:blue">model = "fd"</span>. Second, as we are taking first differences we need to estimate the model without a constant, which is why we include the term <span style="color:red">-1</span> into the model specification.
+Most of this function call is identical to the above call, but for two differences. First, the method now indicates that we need a first difference estimation <span style="color:blue">model = "fd"</span>. Second, as we are taking first differences we need to estimate the model without a constant, which is why we include the term <span style="color:red">- 1</span> into the model specification.
 == Literature ==

@@ Line 98: / Line 98: @@
 One issue with simple models like the pooled model is that it is quite likely that there will be unobserved heterogeneity which is, while not explicitly modelled and hence contained in the error term, it to be expected that some of this county to county variation is correlated with some of the explanatory variables and consequently violating the zero-conditional mean assumption.
-The perhaps easiest way to deal with this is to estimate a model in (time-)differenced form, as this differencing will eliminate the (time-invariant) elements of this heterogeneity. To estimte the model in differenced form. This is done as follows
+The perhaps easiest way to deal with this is to estimate a model in (time-)differenced form, as this differencing will eliminate the (time-invariant) elements of this heterogeneity. To estimte the model in differenced form. This is done as follows:
 == Literature ==

@@ Line 96: / Line 96: @@
 === First Difference ===
-One issue with simple models like the pooled model is that the
+One issue with simple models like the pooled model is that it is quite likely that there will be unobserved heterogeneity which is, while not explicitly modelled and hence contained in the error term, it to be expected that some of this county to county variation is correlated with some of the explanatory variables and consequently violating the zero-conditional mean assumption.
 == Literature ==