Panel Data – MSR Economics Perspective

Panel Data Nonlinear Simultaneous Equation Models with Two-Stage Least Squares using Stata

In this article, we will follow Woolridge (2002) procedure to estimate a set of equations with nonlinear functional forms for panel data using the two-stage least squares estimator. It has to be mentioned that this topic is quite uncommon and not used a lot in applied econometrics, this is due that instrumenting the nonlinear terms might be somewhat complicated.

Assume a two-equation system of the form:

Where the y’s represents the endogenous variables, Z represents the exogenous variables taken as instruments and u are the residuals for each equation. Notice that y₂ is in a quadratic form in the first equation but also present in linear terms on the second equation.

Woolridge calls this model as nonlinear in endogenous variable, yet the model still linear in the parameters γ making this a particular problem where we need to somehow instrument the quadratic term of y₂.

Finding the instruments for the quadratic term is a particular challenge than already it is for linear terms in simple instrumental variable regression. He suggests the following:

“A general approach is to always use some squares and cross products of the exogenous variables appearing somewhere in the system. If something like exper² appears in the system, additional terms such as exper³ and exper⁴ would be added to the instrument list.” (Wooldridge, 2002, p. 235).

Therefore, it worth the try to use nonlinear terms of the exogenous variables from Z, in the form of possible Z² or even Z³. And use these instruments to deal with the endogeneity of the quadratic term y₂. When we define our set of instruments, then any nonlinear equation can be estimated with two-stage least squares. And as always, we should check the overidentifying restrictions to make sure we manage to avoid inconsistent estimates.

The process with an example.

Let’s work with the Example of a nonlinear labor supply function. Which is a system of the form:

Some brief description of the model indicates that for the first equation, the hours (worked) are a nonlinear function of the wage, the level of education (educ), the age (age), the kids situation associated to the age, whether if they’re younger than 6 years old or between 6 and 18 (kidslt6 and kidsge6), and the wife’s income (nwifeinc).

On the second equation, the wage is a function of the education (educ), and a nonlinear function of the exogenous variable experience (exper and exper²).

We work on the natural assumptions that E(u|z)=0 therefore the instruments are exogenous. Z in this case contains all the other variables which are not endogenous (hours and wage are the endogenous variables).

We will instrument the quadratic term of the logarithm of the wage in the first equation, and for such instrumenting process we will add three new quadratic terms, which are:

And we include those in the first-stage regression.

With Stata we first load the dataset which can be found here.

https://drive.google.com/file/d/1m4bCzsWgU9sTi7jxe1lfMqM2T4-A3BGW/view?usp=sharing

Load up the data (double click the file with Stata open or use some path command to get it ready)

use MROZ.dta

Generate the squared term for the logarithm of the wage with:

gen lwage_sq=lwage *lwage

Then, get ready to use the following command with ivregress, however, we will explain it in detail.

ivregress 2sls hours educ age kidslt6 kidsge6 nwifeinc (lwage lwage_sq = educ c.educ#c.educ exper expersq age c.age#c.age kidsge6 kidslt6 nwifeinc c.nwifeinc#c.nwifeinc), first

Which has the following interpretation. According to the syntaxis of Stata’s program. First, make sure you specify the first equation with the associated exogenous variables, we do that with the part.

ivregress 2sls hours educ age kidslt6 kidsge6 nwifeinc

Now, let’s tell to Stata that we have two other endogenous regressors, which are the wage and the squared term of the wages. We open the bracket and put

(lwage lwage_sq =

This will tell to Stata that lwage and lwage_sq are endogenous, part of the first equation of hours, and after the equal, we specify ALL the exogenous variables including the instruments for the endogenous terms, this will lead to include the second part as:

(lwage lwage_sq = educ c.educ#c.educ exper expersq age c.age#c.age kidsge6 kidslt6 nwifeinc c.nwifeinc#c.nwifeinc)

Notice that this second part will have a c.var#c.var structure, this is Stata’s operator to indicate a multiplication for continuous variables, (and we induce the quadratic terms without generating the variables with another command like we did with the wage).

So notice we have c.educ#c.educ which is the square of the educ variable, and c.age#c.age which is the square of the age, and we also square the wife’s income with c.nwifeinc#c.nwifeinc. These are the instruments for the quadratic term.

The fact that we have two variables on the left (lwage and lwage_sq) indicates that the set of instruments will hold first for an equation for lwage and second for lwage_sq given the exact same instruments.

We include the option , first to see what were the regressions in the first stage.

ivregress 2sls hours educ age kidslt6 kidsge6 nwifeinc (lwage lwage_sq  = educ c.educ#c.educ exper expersq age c.age#c.age kidsge6 kidslt6 nwifeinc c.nwifeinc#c.nwifeinc), first

The output of the above model for the first stage equations is:

And the output for the two stage equation is:

Which yields in the identical coefficients in Woolridge’s book (2002, p- 236) also with some slightly difference in the standard errors (yet these slight differences do not change the interpretation of the statistical significance of the estimators).

In this way, we instrumented both endogenous regressors lwage and lwage_sq. Which are a nonlinear relationship in the model.

As we can see, the quadratic term is not statistically significant to explain the hours worked.

At last, we need to make sure that overidentification restrictions are valid. So we use after the regression

estat overid

And within this result, we cannot reject the null that overidentifying restrictions are valid.

Bibliography

Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. Cam-bridge, MA: MIT Press.

Please follow and like us:

Wooldridge Serial Correlation Test for Panel Data using Stata.

In this article, we will follow Drukker (2003) procedure to derive the first-order serial correlation test proposed by Jeff Wooldridge (2002) for panel data. It has to be mentioned that this test is considered a robust test, since works with lesser assumptions on the behavior of the heterogeneous individual effects.

We start with the linear model as:

Where y represents the dependent variable, X is the (1xK) vector of exogenous variables, Z is considered a vector of time-invariant covariates. With µ as individual effects for each individual. Special importance is associated with the correlation between X and µ since, if such correlation is zero (or uncorrelated), we better go for the random-effects model, however, if X and µ are correlated, it’s better to stick with fixed-effects.

The estimators of fixed and random effects rely on the absence of serial correlation. From this Wooldridge use the residual from the regression of (1) but in first-differences, which is of the form of:

Notice that such differentiating procedure eliminates the individual effects contained in µ, leading us to think that level-effects are time-invariant, hence if we analyze the variations, we conclude there’s non-existing variation over time of the individual effects.

Once we got the regression in first differences (and assuming that individual-level effects are eliminated) we use the predicted values of the residuals of the first difference regression. Then we double-check the correlation between the residual of the first difference equation and its first lag, if there’s no serial correlation then the correlation should have a value of -0.5 as the next expression states.

Therefore, if the correlation is equal to -.5 the original model in (1) will not have serial correlation. However, if it differs significantly, we have a serial correlation problem of first-order in the original model in (1).

For all of the regressions, we account for the within-panel correlation, therefore all of the procedures require the inclusion of the cluster regression, and also, we omit the constant term in the difference equation. In sum we do:

Specify our model (whether if it has fixed or random effects, but these should be time-invariant).
Create the difference model (using first differences on all the variables, therefore the difference model will not have any individual effects). We perform the regression while clustering the individuals and we omit the constant term.
We predict the residuals of the difference model.
We regress the predicted residual over the first lag of the predicted residual. We also cluster this regression and omit the constant.
We test the hypothesis if the lagged residual equal to -0.5.

Let’s do a quick example of this steps using the same example as Drukker.

We start loading the database.

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

Then we format the database for stata with the code:

xtset idcode year

Then we generate some quadratic variables.

gen age2 = age^2
gen tenure2 = tenure^2

We regress our model of the form of:

xtreg ln_wage age* ttl_exp tenure* south, fe

It doesn’t matter whether if it is fixed or random effects as long as we assume that individuals’ effects are time invariant (therefore they get eliminated in the first difference model).

Now let’s do the manual estimation of the test. In order to do this, we use a pooled regression of the model without the constant and clustering the regression for the panel variable. This is done of the form:

reg d.ln_wage d.age* d.ttl_exp d.tenure* d.south, noconst cluster(idcode)

The options noconst eliminates the constant term for the difference model, and cluster option includes a clustering approach in the regression structure, finally idcode is the panel variable which we identify our individuals in the panel.

The next thing to do is predict the residuals of the last pooled difference regression, and we do this with:

predict u, res

Then we regress the predicted residual u against the first lag of u, while we cluster and also eliminate the constant of the regression as before.

reg u L.u, noconst cluster(idcode)

Finally, we test the hypothesis whether if the coefficient of the first lag of the pooled difference equation is equal or not to -0.5

test L.u==-0.5

According to the results we strongly reject the null hypothesis of no serial correlation with a 5% level of significance. Therefore, the model has serial correlation problems.

We can also perform the test with the Stata compiled package of Drukker, which can be somewhat faster. We do this by using

xtserial ln_wage age* ttl_exp tenure* south, output

and we’ll have the same results. However, the advantage of the manual procedure of the test is that it can be done for any kind of model or regression.

Bibliography

Drukker, D. (2003) Testing for serial correlation in linear panel-data models, The Stata Journal, 3(2), pp. 168–177. Taken from: https://journals.sagepub.com/doi/pdf/10.1177/1536867X0300300206

Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. Cam-bridge, MA: MIT Press.

Please follow and like us:

Ramsey RESET Test on Panel Data using Stata

In regression analysis, we often check the assumptions of the econometrical model regressed, during this, one of the key assumptions is that the model has no omitted variables (and it’s correctly specified). In 1969, Ramsey (1969) developed an omitted variable test, which basically uses the powers of the predicted values of the dependent variable to check if the model has an omitted variable problem.

Assume a basic fitted model given by:

Where y is the vector of containing the dependent variable with nx1 observations, X is the matrix that contains the explanatory variables which is nxk (n are the total observations and k are the number of independent variables). The vector b represents the estimated coefficient vector.

Ramsey test fits a regression model of the type

Where z represents the powers of the fitted values of y, the Ramsey test performs a standard F test of t=0 and the default setting is considering the powers as:

In Stata this is easily done with the command

estat ovtest

after the regression command reg.

To illustrate this, consider the following code:

use https://www.stata-press.com/data/r16/auto
regress mpg weight foreign
estat ovtest

The null hypothesis is that t=0 so it means that the powers of the fitted values have no relationship which serves to explain the dependent variable y, meaning that the model has no omitted variables. The alternative hypothesis is that the model is suffering from an omitted variable problem.

In the panel data structure where we have multiple time series data points and multiple observations for each time point, in this case we fit a model like:

With i=1, 2, 3, …, n observations, and for each i, we have t=1, 2, …, T time periods of time. And v represents the heterogenous effect which can be estimated as parameter (in fixed effects: which can be correlated to the explanatory variables) and as variable (in random effects which is not correlated with the explanatory variables).

To implement the Ramsey test manually in this regression structure in Stata, we will follow Santos Silva (2016) recommendation, and we will start predicting the fitted values of the regression (with the heterogenous effects too!). Then we will generate the powers of the fitted values and include them in the regression in (4) with clustered standard errors. Finally, we will perform a significant test jointly for the coefficients of the powers.

use https://www.stata-press.com/data/r16/nlswork

xtreg ln_w grade age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure c.tenure#c.tenure 2.race not_smsa south, fe cluster(idcode)

predict y_hat,xbu

gen y_h_2=y_haty_hat gen y_h_3=y_h_2y_hat

gen y_h_4=y_h_3*y_hat

xtreg ln_w grade age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure c.tenure#c.tenure 2.race not_smsa south y_h_2 y_h_3 y_h_4, fe cluster (idcode)

test y_h_2 y_h_3 y_h_4

Alternative you can skip the generation of the powers and apply them directly using c. and # operators in the command as it follows this other code:

use https://www.stata-press.com/data/r16/nlswork

xtreg ln_w grade age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure c.tenure#c.tenure 2.race not_smsa south, fe cluster(idcode)

predict y_hat,xbu

xtreg ln_w grade age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure c.tenure#c.tenure 2.race not_smsa south c.y_hat#c.y_hat c.y_hat#c.y_hat# c.y_hat c.y_hat#c.y_hat# c.y_hat# c.y_hat , fe cluster (idcode)

test c.y_hat#c.y_hat c.y_hat#c.y_hat# c.y_hat c.y_hat#c.y_hat# c.y_hat# c.y_hat

At the end of the procedure you will have this result.

Where the null hypothesis is that the model is correctly specified and has no omitted variables, however in this case, we reject the null hypothesis with a 5% level of significance, meaning that our model has omitted variables.

As an alternative but somewhat more restricted, also with more features, you can use the user-written package “resetxt” developed by Emad Abd & Sahra Khaleel (2015) which can be used after installing it with:

ssc install resetxt, replace

This package however doesn’t work with factor-variables or time series operators, so we cannot include c. or i. and d. or L. operators for example.

clear all

use https://www.stata-press.com/data/r16/nlswork

gen age_sq=ageage gen ttl_sq= ttl_exp ttl_exp

gen tenure_sq= tenure* tenure

xtreg ln_w grade age age_sq ttl_exp ttl_sq tenure tenure_sq race not_smsa south, fe cluster(idcode)

resetxt ln_w grade age age_sq ttl_exp ttl_sq tenure tenure_sq race not_smsa south, model(xtfe) id(idcode) it(year)

however, the above code might be complicated to calculate in Stata, depending on how much memory do you have to do the procedure. That’s why in this post it was implemented the manual procedure of the Ramsey test in the panel data structure.

Bibliography

Emad Abd, S. E., & Sahra Khaleel, A. M. (2015). RESETXT: Stata Module to Compute Panel Data REgression Specification Error Tests (RESET). Obtained from: Statistical Software Components S458101: https://ideas.repec.org/c/boc/bocode/s458101.html

Ramsey, J. B. (1969). Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society Series B 31, 350–371.

Santos Silva, J. (2016). Reset test after xtreg & xi:reg . Obtained from: The Stata Forum: https://www.statalist.org/forums/forum/general-stata-discussion/general/1327362-reset-test-after-xtreg-xi-reg?fbclid=IwAR1vdUDn592W6rhsVdyqN2vqFKQgaYvGvJb0L2idZlG8wOYsr-eb8JFRsiA

Please follow and like us: