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Stata Command for estimating tsls and iv regressions.
Equation to be estimated Stata command
y 1 = β 10 + β 12 y 2 + β 13 y 3 + α 12 x 2 + α 13 x 3 + α 15 x 5 MathType@MTEF@5@5@+=feaagyart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=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@5ACB@ .ivreg y1 x2 x3 x5 (y2 y3 = x1 x4)
y 2 = β 20 + β 21 y 1 + α 23 x 3 MathType@MTEF@5@5@+=feaagyart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyEamaaBaaaleaacaaIYaaabeaakiabg2da9iabek7aInaaBaaaleaacaaIYaGaaGimaaqabaGccqGHRaWkcqaHYoGydaWgaaWcbaGaaGOmaiaaigdaaeqaaOGaamyEamaaBaaaleaacaaIXaaabeaakiabgUcaRiabeg7aHnaaBaaaleaacaaIYaGaaG4maaqabaGccaWG4bWaaSbaaSqaaiaaiodaaeqaaaaa@486B@ .ivreg y2 x3 (y1 = x1 x2 x4 x5)

An example from Stata. The Stata manual offers the following example analysis. Assume that you want to use state level data from the 1980 census to estimate the following system of equations:

h s n g v a l = α 0 + α 1 f a i n c + α 2 r e g 2 + α 3 r e g 3 + α 4 r e g 4 + ε MathType@MTEF@5@5@+=feaagyart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiAaiaadohacaWGUbGaam4zaiaadAhacaWGHbGaamiBaiabg2da9iabeg7aHnaaBaaaleaacaaIWaaabeaakiabgUcaRiabeg7aHnaaBaaaleaacaaIXaaabeaakiaadAgacaWGHbGaamyAaiaad6gacaWGJbGaey4kaSIaeqySde2aaSbaaSqaaiaaikdaaeqaaOGaamOCaiaadwgacaWGNbGaaGOmaiabgUcaRiabeg7aHnaaBaaaleaacaaIZaaabeaakiaadkhacaWGLbGaam4zaiaaiodacqGHRaWkcqaHXoqydaWgaaWcbaGaaGinaaqabaGccaWGYbGaamyzaiaadEgacaaI0aGaey4kaSIaeqyTdugaaa@5FAD@

and

r e n t = β 0 + β 1 h s n g v a l + β 2 p c t u r b a n + ν , MathType@MTEF@5@5@+=feaagyart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamOCaiaadwgacaWGUbGaamiDaiabg2da9iabek7aInaaBaaaleaacaaIWaaabeaakiabgUcaRiabek7aInaaBaaaleaacaaIXaaabeaakiaadIgacaWGZbGaamOBaiaadEgacaWG2bGaamyyaiaadYgacqGHRaWkcqaHYoGydaWgaaWcbaGaaGOmaaqabaGccaWGWbGaam4yaiaadshacaWG1bGaamOCaiaadkgacaWGHbGaamOBaiabgUcaRiabe27aUjaacYcaaaa@55A7@

where hsngval is the median dollar value of owner-occupied housing; rent is the median monthly gross rent; fainc is family income; pcturban is the percent of the state population living in an urban area; and reg2 , reg3 , and reg4 are dummy variables that designate the region of the country where the state is located. In this example we focus on estimating (17).

We begin by loading the data set and describing the data.

. use http://www.stata-press.com/data/r8/hsng2

(1980 Census housing data)

.describe

Description of the Stata Data set used in the example.
Contains data from http://www.stata-press.com/data/r8/hsng2.dta
obs: 50 1980 Census housing data
vars: 16 3 Sep 2002 12:25
size: 3,600 (99.7% of memory free)
variable name storage type display format value label variable label
state str14 % 14s State
division int % 8.0g division Census division
region int % 8.0g region Region
pop long % 10.0g Population in 1980
popgrow float % 6.1f Pop. growth 1970-80
popden int % 6.1f Pop/sq. mile
pcturban float % 8.1f Percent urban
faminc long % 8.2f Median family inc., 1979
hsng long % 10.0g Hsng units 1980
hsnggrow float % 8.1f % housing growth
hsngval long % 9.2f Median hsng value
rent long % 6.2f Median gross rent
reg1 float % 9.0g
reg2 float % 9.0g
reg3 float % 9.0g
reg4 float % 9.0g
Sorted by: state

Now we estimate equation (17) using TSLS as shown in Figure 2.

Two-stages least square estimate of the example.

Stata output from the TSLS regression estimate.

The manual continues the example to include some testing of the model including the Hausman test. Students using TSLS and IV should read the discussion in the Stata manual thoroughly.

Exercises

Cigarette advertising and sales. A great deal of controversy exists over the issue of whether advertising expenditures affect sales. This controversy is particularly sharp when it affects policy decisions. An example of this phenomenon is the controversy over the impact of cigarette advertising on advertising sales. While many public policy experts advocate bans on cigarette advertising, a majority of economists caution against bans on cigarette advertising. The economists point out that there is little theoretical reasons to believe that cigarette advertising affects total demand for cigarettes. Instead, economists argue that cigarette advertising only affects brand choice and not the number of cigarettes that people smoke. Moreover, these economists point out that there is also little empirical evidence that supports the argument that cigarette advertising affects the demand for cigarettes. Given the negative impact advertising bans have on freedom of speech, most economists conclude that the negative effects of cigarette advertising bans outweigh the benefits of the bans.

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Source:  OpenStax, Econometrics for honors students. OpenStax CNX. Jul 20, 2010 Download for free at http://cnx.org/content/col11208/1.2
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