Unit 11 ECONF16 (assignment)

# 2 use the command xtset to formally declare the data

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(2) use the command xtset to formally declare the data as “panel data” listing first the variable name for the cross section and then the variable for time, for example in our murder data set use the command xtset id year Once you have identified your data as “panel” you can use the set of estimation commands found by Statistics -> Longitudinal/Panel Data which are extremely useful. (i) In general (not specific to this data set) define the purpose of FE (“fixed effects”) in panel data models. What is the general impact of using “fixed effects” on the R 2 of the estimated model? In general fixed effects in panel data models is a time invariant unobserved variable, a i (the variable missing 1 ii.), that captures all time-constant factors such as city location that affect the dependent variable, y it . Generally speaking the fixed effects are like a restricted version of a model disregarding the time component in panel data so it has a reducing effect on the R 2 . (ii) Use the STATA command xtset to identify the “murder” data as a panel data set. Copy/paste the command and the immediate response running the command produces. Summarize, with the help of this response, important characteristics of the “n” and “t” dimensions in your data set. You may also find it helps to look at the “Data Editor” so that you are clear about talking about these dimensions. Note that if you are using panel data analysis in your own work it is important to clearly identify these two dimensions of your data set. . xtset mrdrte year repeated time values within panel r(451); The t dimension, year, is important because it lets us identify each observation over time and see how or not a particular point of interest is changing over time. The n dimension is obviously just the number of observation in the data set. The reason why there was an error returned by using murder as the panel data set is because it is not assigning unique observations over time. So for example murder rate can be 1.8 in 87 multiple times. By having id as the panel data we are observing unique instances in time for a particular FE, e.g. state. (iii) on p. 494 [Table 14.2] you are given an example of a panel data regression in which estimates are compared for pooled, FE and RE. Note that as with the example in the book, you will include the time dummies for each regression. Please re-construct this table using the base model: mrdrte it = f ( timedummies,exec it ,unem it ) To run this model, note that the pooled model is obtained by directly running the model given above; the random effects and fixed effects models can be obtained in STATA using the Statistics -> Longitudinal/Panel Models -> Linear Models -> Linear Regression … Please use “GLS random effects.” 3

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