This repository is a comprehensive resource accompanying the paper "When Failure is an Option: Fragile Liquidity in Over-the-Counter Markets" by Terrence Hendershott, Dan Li, Dmitry Livdan, Norman Schürhoff. It includes source codes and datasets for replicating and extending the study's analysis on the total cost of immediacy (TCI) in over-the-counter markets.

To effectively use this repository, users must adjust the directory paths within the Stata code to match their local environment. The repository supports switching between real and simulated data by setting a global variable in the source code.

The replicator should expect the code to run for between 3 days and 1 week.

Repository contents: 1. Original Stata Source Code: Implements the methodologies for calculating TCI, using both optimal and constrained quantile rotations. This code is the core for reproducing the main results presented in the paper. 2. Simulated Data Generation Code: Written in Stata, this script produces simulated datasets that mimic the data used in the study. It allows for the examination of TCI for different tranches under various hypothetical market conditions. 3. Simulated Input Datasets: Three tranches of pre-generated simulated data are included. These datasets serve as ready-to-use examples for computing TCI, facilitating the analysis without the need for initial data generation. 4. Log File: Contains the output from applying the original source code to the simulated datasets. The log illustrates the expected results and serves as a benchmark for verifying the correct execution of the code.

This dataset consists of full database of finantial and operational data of Portuguese firms covering the period 2014-2019. In addition, geographical location data is also shared, in order to construct the spatial weights matrix. The Stata do. file is also shared with the computed routines explained in the manuscript. Any question/inquiry should be addressed to samuelf@utad.pt.

This archive contains the replication files for "Sample selection in linear panel data models with heterogeneous coefficients" by Alyssa Carlson and Riju Joshi, in Journal of Applied Econometrics (2023). All codes and data are provided. There are two folders corresponding to the simulation study and the empirical application of the paper. To replicate, navigate to the respective folder and follow the README instructions. All codes and datasets are in Stata.

csv data set and Stata do-files.. Visit https://dataone.org/datasets/sha256%3Af11fd91f00a1311f174bc83f36597c01c636a9a48ba4a50cddc43167ec179524 for complete metadata about this dataset.

All replication code for the above paper. This includes general-purpose R and Stata code, all simulation code, all empirical data sets, and R and Stata code to replicate the empirical results

Stata do-files and data to support tutorial "Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis" (Leurent, B. et al. PharmacoEconomics (2018) 36: 889).Do-files should be similar to the code provided in the article's supplementary material.Dataset based on 10 Top Tips trial, but modified to preserve confidentiality. Results will differ from those published.

These files included the data set and the Stata program file to reproduce the findings reported in the manuscript

Addressing sample selection bias for machine learning methods (replication data)

Dylan Brewer and Alyssa Carlson

Accepted at Journal of Applied Econometrics, 2023

Overview

This replication package contains files required to reproduce results, tables, and figures using Matlab and Stata. We divide the project into instructions to replicate the simulation, the result from Huang et al (2006), and the application.

Simulation

For reproducing the simulation results

Included files in *\Simulation with short descriptions:

• SSML_simfunc: function that produces individual simulations runs
• SSML_simulation: script that loops over the SSML_simfunc for different DGP and multiple simulation runs
• SSML_figures: script that generates all figures for the paper
• SSML_compilefunc: function that compiles the results from SSML_simulation for the SSML_figures script

Steps for replicating simulation:

1. Save SSML_simfunc, SSML_simulation, SSML_figures, SSML_compilefunc to the same folder. This location will be referred to as the FILEPATH.
2. Create OUTPUT folder inside the FILEPATH location.
3. Change the FILEPATH location inside SSML_simulation and SSML_figures.
4. Run SSML_simulation to produce simulation data and results.
5. Run SSML_figures to produce figures.

Huang et al replication

For reproducing the Huang et. al. (2006) replication results.

Included files in *\HuangetalReplication with short descriptions:

• SSML_huangrep: script that replicates the results from Huang et. al. (2006)

Obtaining the dataset:

Go to https://archive.ics.uci.edu/dataset/14/breast+cancer and save file as "breast-cancer-wisconsin.data"

Steps for replicating results:

1. Save SSML_huangrep and the breast cancer data to the same folder. This location will be referred to as the FILEPATH.
2. Change the FILEPATH location inside SSML_huangrep
3. Run SSML_huangrep to produce results and figures.

Application

For reproducing the application section results.

Included program files in *\Application with short descriptions:

• G0_main_202308.do: Stata wrapper code that will run all application replication files
• G1_cqclean_202308.do: Cleans election outcomes data
• G2_cqopen_202308.do: Cleans open elections data
• G3_demographics_cainc30_202308.do: Cleans demographics data
• G4_fips_202308.do: Cleans FIPS code data
• G5_klarnerclean_202308.do: Cleans Klarner gubernatorial data
• G6_merge_202308.do: Merges cleaned datasets together
• G7_summary_202308.do: Generates summary statistics tables and figures
• G8_firststage_202308.do: Runs L1 penalized probit for the first stage
• G9_prediction_202308.m: Trains learners and makes predictions
• G10_figures_202308.m: Generates figures of prediction patterns
• G11_final_202308.do: Generates final figures and tables of results
• r1_lasso_alwayskeepCF_202308.do: Examines the effect of requiring the control function is not dropped from LASSO
• latexTable.m: Code by Eli Duenisch to write LaTeX tables from Matlab (https://www.mathworks.com/matlabcentral/fileexchange/44274-latextable)

Included non-confidential data in subdirectory `*\Application\Data`:

• \CAINC30: County level income and demographics data from the BEA
• \CPI: CPI data from the BLS
• \KlarnerGovernors: Carl Klarner's Governors Dataset available at https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:1902.1/20408

Confidential data suppressed in subdirectory `*\Application\CD`:

These data cannot be transferred as part of the data use agreement with the CQ Press. Thus, the files are not included.

• \CQ_county: County level election outcomes available from http://library.cqpress.com/elections/login.php?requested=%2Felections%2Fdownload-data.php
• \CQ_open: Open elections available from http://library.cqpress.com/elections/advsearch/elections-with-open-seats-results.php?open_year1=1968&open_year2=2019&open_office=4

There is no batch download--downloads for each year must be done by hand. For each year, download as many state outcomes as possible and name the files YYYYa.csv, YYYYb.csv, etc. (Example: 1970a.csv, 1970b.csv, 1970c.csv, 1970d.csv). See line 18 of G1_cqclean_202308.do for file structure information.

Steps for replicating application:

1. Download confidential data from the CQ Press.
2. Change the working directory in G0_main_202308.do on line 18 to the application folder.
3. Change local matlabpath in G0_main_202308.do on line 18 to the appropriate location.
4. Set directory and file path in G9_prediction_202308.m and G10_figures_202308.m as necessary.
5. Run G0_main_202308.do in Stata to run all programs.
6. All output (figures and tables) will be saved to subdirectory *\Application\Output.

Contact

Contact Dylan Brewer (brewer@gatech.edu) or Alyssa Carlson (carlsonah@missouri.edu) for help with replication.

This zip file contains the data and the Stata do-files to replicate the results presented in Boto-García, Alvarez & Del Corral (2024) "Separating catch-up and technical change in Stochastic Frontier models: A Monte Carlo Analysis"

This archive contains the replication package for "Re-examining the relationship between patience, risk-taking, and human capital investment across countries" by Alexandra de Gendre, Jan Feld and Nicolás Salamanca, in Journal of Applied Econometrics. The replication files include a readme file, and Stata do-files. We do not have permission to share the data.

This project folder contains supplementary data files and software codes (for STATA) to replicate the estimation results documented in:

"Can large-scale RDI funding stimulate post-crisis recovery growth? Evidence for Finland during COVID-19"

by Mitze, T. and Makkonen, T. (2022).

A previous working paper version of this research can be found in the arXiv repository with ID: arXiv:2112.11562

Link to working paper: https://arxiv.org/abs/2112.11562

Two Stata .do files (Replication_Main_Dofile.do and Replication_AppendixB_Dofile) are provided that replicate all tables and four figures in the manuscript, and all tables and figures in the supplemental materials with our two final datasets. However, our contract with Core Logic prohibits us from distributing or sharing data with third parties. We provide our dataset for our estimation in main body as a Stata.dta file (Main_estimation.dta), but with restricted variables filled in as a string “Proprietary: Core Logic” which will prevent estimation of our models using the .do file.

Replication data and Stata .do files for "Unraveling the Price-Concentration Relationship: The Role of National Distribution Centers in Chilean Supermarket Industry Consolidation", Economic Modelling.

This research tests three hypotheses: 1) Internet diffusion has a significant effect on within-province income inequality in Indonesia; 2) Internet diffusion has a significant nonlinear effect on within-province income inequality in Indonesia; and 3) Internet diffusion has a significant effect on within-province income inequality in Indonesia differing by per capita income and education level. An important finding in this research is that Internet diffusion has a significant effect on income inequality as indicated by the positive influence of the Internet on income inequality between regions. In addition, the influence of the internet tends to be nonlinear and interacts differently with the heterogeneity of each province. The attached data is in .dta and .do format which can be processed with STATA by running the file. Original data was collected from the Badan Pusat Statistik (BPS) website which was processed into STATA. IneqPaper14.dta is a data set and Paper_2013-2019.do contains commands for processing data set files. IneqPaper15_2013-2019.dta is the final processed data.

This repository provides a comprehensive dataset and supporting materials for analyzing the relationship between artificial intelligence, carbon emissions, and sustainable urban development in China. The collection covers 272 cities from 2006 to 2022 and is organized to ensure transparency, replicability, and flexibility for further research.Contents:Full panel dataset: Balanced panel data covering economic, technological, and environmental dimensions across cities and years.Code package: Stata code files to replicate the empirical analysis, including data cleaning, model estimation, and robustness checks.Data formats: The dataset is available in Stata (.dta) format for direct use, with supporting documentation for ease of application.Spatial weight matrices: Files constructed to support spatial econometric models, enabling the analysis of spatial dependence and spillover effects.Data processing: All sources were harmonized, standardized, and deflated to constant prices for temporal comparability. Outlier treatments and spatial structure construction were applied to improve robustness and enable advanced econometric modeling.Applications: The dataset and code can be used for econometric, mediation, moderation, and spatial analyses, as well as extended to broader studies on urban low-carbon transition, technological innovation, and policy evaluation. It is designed to be both reproducible and adaptable for future research.This integrated dataset provides a valuable resource for scholars and policymakers interested in climate change mitigation, digital transformation, and sustainable development in urban China.

The data is in Stata format and includes 2 files. The file named Agric has variables: spot price of Chicago corn and Chicago soybeans, the futures price of Chicago corn and Chicago soybeans and long positions of commodity index traders. The file named Energy contains variables on spot and futures prices of WTI crude oil and Henry Hub natural gas. The data is originally obtained from US commodity futures trading commission