zhentaoshi / c-lasso

The replication data and files for Liangjun Su, Zhentao Shi and Peter Phillips (2016, Econometrica): “Identifying Latent Structures in Panel Data”

C-Lasso

This is the Matlab code for the empirical applications and simulations of

• Liangjun Su, Zhentao Shi and Peter Phillips: “Identifying Latent Structures in Panel Data” (2016), Econometrica, Vol.84, No.6, 2215-2264.

Please contact Zhentao Shi ([email protected]) if you have any question about the code.

R users please check github.com/zhan-gao/classo.

A follow-up paper is composed to further investigate the computational speed of C-Lasso. Please refer to:

• Zhan Gao and Zhentao Shi (2020): "Implementing Convex Optimization in R: Two Econometric Examples", arXiv:1806.10423

Computation Environment

For the Matlab code, CVX must be installed to implement convex optimization. Mosek is recommended to facilitate CVX, but not necessary.

Generic Functions

We add a folder generic_functions for the estimation procedures. The functions are ready to take input and return output.

• SSP_PLS_est.m is a generic function to implement PLS.
• PLS_example.m is a minimum example of PLS.

Development Plan after Publication

In response to demand, we may further consider

• provide user-friendly Matlab interface for general use (currently working under generic_functions)

We welcome interested researchers to develop the code with us.

Note for v1.0: Replication Package

The results in the paper are generated under

• Matlab 8.5
• CVX 2.1 (http://cvxr.com/cvx/download/)
• Mosek 7.1 (https://www.mosek.com/resources/downloads).

CVX must be installed and linked with Matlab, and Mosek is invoked as the solver through the command cvx_solver mosek. Without Mosek, a user can still run the code with CVX if he comments out this line.

The empirical applications can be exactly replicated by the commented master.m in folders

• app_saving_PLS: for Section 5.1
• app_saving_PGMM: for Section 5.1
• app_civil_war: for Section 5.2
• app_democracy: for Section S4.3

Data are also provided in each folder.

The workhorse scripts that execute the iterative algorithm in Section 3.1 of the Supplementary Material are

• PLS_est.m: for PLS estimation
• PGMM_est.m: for PGMM estimation
• PNL_est.m: for the PPL (Panel Probit) estimation

The scripts in folders simulations generate the simulation results. The master files are either master_** or **_super. Super parameters, such as N, T and Rep, should be provided outside of the main function or script.