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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

Please contact Zhentao Shi (zhentao.shi@cuhk.edu.hk) 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:

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.

Development Plan after Publication

In response to demand, we may further consider

We welcome interested researchers to develop the code with us.

Note for v1.0: Replication Package

The results in the paper are generated under

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

Data are also provided in each folder.

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

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.