# Technological-Innovation-Resource-Allocation-and-Growth-Replication-Kit **Repository Path**: ccyyoung/Technological-Innovation-Resource-Allocation-and-Growth-Replication-Kit ## Basic Information - **Project Name**: Technological-Innovation-Resource-Allocation-and-Growth-Replication-Kit - **Description**: This repository provides the replication code and data for Kogan, L., Papanikolaou, D., Seru, A. and Stoffman, N., QJE 2017. - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 25 - **Created**: 2021-08-17 - **Last Updated**: 2021-08-17 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Replication Code and Data for Kogan, L., Papanikolaou, D., Seru, A. and Stoffman, N., QJE 2017 > 原仓库地址： > 🍎 Note: 数据文件需要到原仓库逐个下载。批量下载整个仓库的方式无法正确下载数据文件。(Update: `2021/7/19 12:38`)   This package provides the replication code for the main results in **Kogan, L., Papanikolaou, D., Seru, A. and Stoffman, N., 2017. Technological innovation, resource allocation, and growth. Quarterly Journal of Economics, 132(2), pp. 665-712.** The paper is available at https://academic.oup.com/qje/article/132/2/665/3076284. The folder ./Code contains all programs, while the folder ./Data includes all needed input data files. ## Code The folder ./Code includes code files of two types of programs: Stata and Matlab. ### Code files using Stata: #### Prerequisite: In order for the code to run correctly, you need the following packages installed in Stata: - **winsorizeJ**: Winsorize variables using breakpoints that vary by year. (A winsorizeJ.ado file is provided in the folder ./Code) - **cluster2**: 2D clustered standard errors. (A cluster2.ado file is provided in the folder ./Code) - **ivreg2**: IV regression with more features than STATA's ivreg. (Install the package using command "ssc install ivreg2") - **reghdfe**: Regression with multiple levels of fixed effects. (Install the package using command "ssc install reghdfe") - **estout**: Output Stata tables in tex format. (Install the package using command "ssc install estout") #### File description: - **FilterReturnsCreateFirmMeasures.do**: Creates the firm-level innovation measure using patent and stock return data - **PatentValueCites.do**: Descriptive statistics for the patent-level measure, including robustness for alternative distributions (Table 1 in the paper and Table A.6 in the Online Appendix); relates the estimated patent values to forward citations (Table 2 in the paper and Table A.7 in the Online Appendix) - **CreateFirmSample.do**: Creates the data for the firm-level regressions - **FirmSummaryStats.do**: Descriptive statistics for the firm-level measure (Table 3 in the paper) - **FirmProfitsRegressionSM.do**: Firm Profits and innovation (Panel a of Table 4 in the paper) **FirmOutputRegressionSM.do**: Firm Output and innovation (Panel b of Table 4 in the paper) **FirmReallocationRegressionSM.do**: Firm Capital/Labor and innovation (Panels c and d of Table 4 in the paper ) **FirmTFPRegressionSM.do**: Firm TFP and innovation (Panel e of Table 4 in the paper) - **FirmProfitsRegressionCW.do**: Firm Profits and innovation using citation-weighted patents (Panel a of Table 5 in the paper) **FirmOutputRegressionCW.do**: Firm Output and innovation using citation-weighted patents (Panel b of Table 5 in the paper) **FirmReallocationRegressionCW.do**: Firm Capital/Labor and innovation using citation-weighted patents (Panels c and d of Table 5 in the paper) **FirmTFPRegressionCW.do**: Firm TFP and innovation using citation-weighted patents (Panel e of Table 5 in the paper) - **FirmProfitsRegressionSMCW.do**: Firm Profits and innovation using both our measure and citation-weighted patents (Panel a of Table 6 in the paper) **FirmOutputRegressionSMCW.do**: Firm Output and innovation using both our measure and citation-weighted patents (Panel b of Table 6 in the paper) **FirmReallocationRegressionSMCW.do**: Firm Capital/Labor and innovation using both our measure and citation-weighted patents (Panels c and d of Table 6 in the paper) **FirmTFPRegressionSMCW.do**: Firm TFP and innovation using both our measure and citation-weighted patents (Panel e of Table 6 in the paper) - **PatentValueScatterPlot.do**: Plots the cross-sectional relation between forward patent citations and patent market value (Figure 2 in the paper) - **TimeSeriesPlots.do**: Produces and plots the aggregate measures of innovation (Figure 4 in the paper) - **AggregateOutput.do**: Runs the aggregate OLS regressions and VARs that relate our two innovation indices to output and TFP (The regression results for Figure 5 in the paper and Figure A.3 in the Online Appendix) ### Code files using Matlab: #### Prerequisite: In order for the code to run correctly, you need to install the following program as well: - **matlab2tikz**: Converts MATLAB®/Octave figures to TikZ/pgfplots figures for smooth integration into LaTeX. (Please go to https://github.com/matlab2tikz/matlab2tikz for detailed installation information) #### File description: - **plot_OLS_responses.m**: Take the output of AggregateOutput.do and creates Figure 5 in the paper - **csvimport.m**: Define the function called in plot_OLS_responses.m which helps import the .csv files - **jbfill.m**: Define the function called in plot_OLS_responses.m which helps plot the confidence intervals ## Additional Notes: - There is a minor discrepancy between the value of average acceptance probabilty used in the codes and reported on page 677 (published version of the paper). This minor difference is due to a typo in the published version. - There are some minor discrepancies between the output that the code generates and the results reported in Table 5 (published version of the paper). These minor differences are due to typos in the published version. - There are copyright restrictions and file size limitations in posting daily data from CRSP. Please contact the authors below to obtain the code and data needed to replicate Figures 1 and 3 in the paper that use this data. ## Contact Please contact Dimitris Papanikolaou (d-papanikolaou@kellogg.northwestern.edu) or Amit Seru (aseru@stanford.edu) for any questions regarding the codes or data. **Please see the paper for more information on the codes and data. If you use these codes files or data, please CITE this paper as the source.**