What model are you using? Many macro models are calibrated around national aggregates, but there are some other ways for proxies.

I have attached an article and I want to know if it can be calibrated for Pakistan's data. Also tell me what new variables can be introduced in it.

Thanks Nabya, this is interesting I am going to look it over for a few days and get back to you.

This is similar to some of the micro work by Findley and Caliendo.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2266170

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2266191

Thank you Rohnn

I am also looking at these papers. Please do tell me about the calibration thing. I will be looking forward for your reply.

Nabya

I have looked at the paper and the Pakistani data here:

http://www.census.gov.pk/Statistics.php

http://data.worldbank.org/country/pakistan#cp_wdi

http://www.pbs.gov.pk/

It appears that they have consumption data as well as percentages for population statistics. The only thing that I could not find was wage data. I would take the tax rate, debt and interest rate as given. What might be best is to use a model similar to Klein and Goldberger (1955) and use 2sls or 3sls to estimate the magnitudes of the of the coefficients.

With that I would then see how different the regression results are for equations 1 and 2 in the paper, if they are different I would the calibrate the models to equate the two by adjusting the q variable (amount of goods invested in child) to make the models match using solver in excel. I would think this should work as this is the one variable that would be difficult to measure. If we assume the model to be correct it could be interesting to see how sensitive human capital is to this. This would also allow you to get back to the CRRA utility that Zhang starts off with. I would also probably do a few countries for comparision since there was no empirical testing in the paper just to see how the numbers compare.

This is an interesting topic, I would be interested in helping, the biggest issue I see at the moment is finding some wage data that would span the consumption data from 1960 - present. Since I am not up on the Pakistani macro data perhaps you know of a source for which I am unaware.

I hope this helps and let me know.

Rohnn

So can we use per capita GDP as a measure of wage rate. What do you think about it?

I'm not sure that would be a good idea as there may be a divergence between wages earned and the income per capita. I am trying to back my way into it, if I use the data from 1960-1999, before they changed the accounting base I get the following in a 3SLS model:

C = constant + b1(GNP) + b2(C(t-1))

I = constant + b1(GNP(t)-GNP(t-1))

GNP = b1C + b2I + b3NX

they actually include government spending in net exports so the system should still be robust.

Equation system, 3slswithNX

Estimator: Three-Stage Least Squares

Equation 1: 3SLS, using observations 1961-1999 (T = 39)

Dependent variable: C

Instruments: C_1 const d_GNP NX

coefficient std. error z p-value

-----------------------------------------------------------

const 16086.3 12630.4 1.274 0.2028

GNP 0.870531 0.167848 5.186 2.14e-07 ***

C_1 −0.104563 0.221156 −0.4728 0.6364

Mean dependent var 282293.1 S.D. dependent var 157057.7

Sum squared resid 6.23e+10 S.E. of regression 39968.46

R-squared 0.933577 Adjusted R-squared 0.929887

Equation 2: 3SLS, using observations 1961-1999 (T = 39)

Dependent variable: I

Instruments: C_1 const d_GNP NX

coefficient std. error z p-value

-------------------------------------------------------

const 31203.4 8497.08 3.672 0.0002 ***

d_GNP 2.07156 0.482688 4.292 1.77e-05 ***

Mean dependent var 63427.82 S.D. dependent var 30565.14

Sum squared resid 2.40e+10 S.E. of regression 24829.10

R-squared 0.322759 Adjusted R-squared 0.304455

Equation 3: 3SLS, using observations 1961-1999 (T = 39)

Dependent variable: GNP

Instruments: C_1 const d_GNP NX

coefficient std. error z p-value

-------------------------------------------------------

C 1.57050 0.626783 2.506 0.0122 **

I −1.67497 2.89913 −0.5778 0.5634

NX −0.0918786 0.391882 −0.2345 0.8146

Mean dependent var 337158.8 S.D. dependent var 192532.1

Sum squared resid 1.57e+11 S.E. of regression 63486.72

At the moment this seems fairly robust, but I am going to play with it a little more. This would mean the MPC is .87 resulting in a multiplier of around 7.5, so Pakistan is pretty dependent on consumption, which should eventually effect the choices of children and education. What I want to do is see if I can work back now from the Aggregates to the Solow/New growth theory stuff so that we can develop a metric to calibrate the agent model to the Macro statistics.

Let me know if this triggers any ideas, I will be playing around with this next week and see where I get.

calibration mean that you have built a theoretical model, but its empirical has already done earlier.

if you see Romer's book then you simply find out that many a time in the start of the chapter of he discuss the model, then later on he puts value from different empirical.