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This issue lays out the structure of this calibration. The steady-state parameter section of the code in execute.py should have a parameter named calibrate_n. This parameter should be a boolean that equals True if you want to calibrate the S disutility of labor level parameters chi^n_s. Its value should be set to False if one does not want to estimate those parameters and just take them as exogenous inputs.

If calibrate_n = True, then the code must perform two steps.

1. Use the household Euler equations for labor supply to calculate chi^n_s_hat. Use CPS data for labor supply. @hayleefay and I wrote some nice code for this in the CPS_hrs_age repository. Get consumption data from the Consumer Expenditure Survey. The chi^n_s_hat values are related to the chi_n_s values in the model by the equation chi_n_s = (factor ** (sigma - 1)) * chi_n_s_hat.

2. Given the chi^n_s_hat values calculated from the Euler equations and a guess for the factor, we can solve the model. We solve for the factor by including it in the outer loop of the steady-state solution method with the interest rate r, such that factor = (data avg hh inc) / (model avg ss hh inc).

If calibrate_n = False, then solve the model with just r in the outer loop of the steady-state computation.

@SophiaMo

In calibrating this model, we have to incorporate consumption data. The best source for consumption expenditures in the United States is the Consumer Expenditure Survey (CEX). We need data on household consumer expenditure by age of the primary respondent (head of household). I see two ways that we can do this.

1. The CEX has summary tables of consumption by broad age categories for each year that are precomputed (PDF and Excel). These summary data look like the light blue bars in the following figure. One thing we could do to get consumption expenditure by age--which is more fine than the course age bins in the summary data--is to fit a curve to the summary data such that the average consumption expenditure across the ages corresponding to the summary data equals the value of the summary data.

2. The more accurate thing we could do is to use the CEX survey microdata (PUMD) itself to calculate average consumption expenditure for each age group. There is a good paper here by Jesus Fernandez-Villaverde and Dirk Krueger,
   "Consumption over the Life Cycle: Facts from Consumer Expenditure Survey Data" (REStat, 89:3, Aug. 2007). This paper calculates exactly the lifecycle consumption profiles by age that we are interested in using the CEX microdata. For our calibration, we would probably want to average data from two or three of the most recent surveys in order to get rid of any noise that comes with the fine granularity of one-year age bins.

Each of these methods has unique advantages and disadvantages. Method 1 is less precise but significantly easier than method 2. Although it is tricky to estimate a smooth curve whose integral over a particular portion (or even average across discrete one-year age bins) equals the average in the summary data. Method 2 is more accurate, although might be significantly harder than method 1 due to the need to access, manipulate, and clean the source data. Method 2 also includes more noise in the averages from year to year.