Problem I: Construction of Panel Data Background The data set for this exercise comes from the paper by Baltagi and Khanti-Akom (1990) ”On Efficient Estimation with Panel Data: An Empirical Comparison of Instrumental Variables Estimators”, published in Journal of Applied Econometrics, vol. 5, p. 401-406. They demonstrate efficient estimation for a returns to schooling example based on a panel of 595 individuals observed over the period 1976-1982 drawn from the Panel Study of Income Dynamics (PSID). Use the data in psidw.csv for the following questions.
(i) Load psidw.csv data to Stata using import command.
(ii) Identify the types of data. Is it panel data or cross-section data? How many individuals are in the sample? How many years of data were collected for each individual?
(iii) Convert wide data to long form with id as cross-section and year as time.
(iv) Run OLS the following equation with t = 1. lwageit = β0 + β1educit + β2unionit + β3marriedit + β4experit + β5exper2 it (1) +β6blackit + β7femaleit + uit
(v) Perform hypothesis test on H0 : β5 = 0. What do you test with this null hypothesis?
vi) Compare equation (1) with lwageit = β0+β1educit+uit. What are the terms for OVBs in estimating this simple regression?
(vii) Define the dataset as panel data with id as cross-section and year as time. Our collected data start from 1976 to 1979. Replace year value to reflect this information. lwageit = β0 + β1educit + β2unionit + β3marriedit + β4experit + β5exper2 it (2) +β6blackit + β7femaleit + ft + ci + uit 1
(viii) Estimate the equation (2) by the pooled OLS. Compare this estimation results to OLS estimates from cross-section data.
(ix) Estimate the equation (2) by the random effects and the fixed effects estimators.
(x) Perform the Hausman test and choose between the random effects and fixed effects estimators. Provide a justification for your choice.
(xi) Why can’t we estimate the coefficients on black and female variables by the fixed effects estimators?
Problem II: Combining data sets Background Combining data sets: In many empirical research projects, the raw data to be utilized are stored in a number of separate files: separate ”waves” of panel data, timeseries data extracted from different databases, and the like. Stata only permits a single data set to be accessed at one time. How, then, do you work with multiple data sets? Several commands are available, including append, merge, and joinby. How, then, do you combine datasets in Stata? First of all, it is important to understand that at least one of the datasets to be combined must already have been saved in Stata format. Second, you should realize that each of Stata’ s commands for combining datasets provides a certain functionality, which should not confused with that of other commands. The append command combines two Stata-format data sets that possess variables in common, adding observations to the existing variables. The same variables need not be present in both files, as long as a subset of the variables are common to the ”master” and ”using” data sets. It is important to note that ”PRICE” and ”price” are different variables in Stata, and one will not be appended to the other.
(i) Use two datasets – data2a.dta and data2b.dta and combine into one dataset using append command. How many individuals are in the sample? Background Combining data sets: We now describe the merge command, which is Stata’s basic tool for working with more than one dataset. The merge command takes a first argument indicating whether you are performing a one-to-one, many-to-one, one-to-many or many-to-many merge using specified key variables. It can also perform a one-to-one merge by observation. Like the append command, the merge works on a “master” dataset the current contents of memory and a single ”using” dataset. One or more key variables are specified, and you need not sort either dataset prior to merging. The distinction between ”master” and ”using” is important. When the same variable is present in each of the files, Stata’s default behavior is to hold the master data inviolate and discard the using dataset’s copy of that variable. The rule for merge, then, is that if datasets are to be combined on one or more merge keys, they each must have one or more variables with a common name and datatype (string vs. numeric). In the example above, each dataset must have a variable named id. That variable can be numeric or string, but that characteristic of the merge key variables must match across the datasets to be merged. This is the simplest kind of merge: the one-to-one merge. Stata supports several other types of merges. But the key concept should be clear: the merge command combines datasets ”horizontally”, adding variables values to existing observations.
(ii) Use two datasets – data1.dta and data2.dta and combine into one dataset using merge command. How many variables are in the sample?
(iii) Use two datasets – data12.dta and data3.dta and combine into one dataset using merge command. How many variables are in the sample? How many individuals are in the sample?
(iv) Reshape the dataset to estimate the panel data model.
(v) Summarize the dataset using panel data command
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