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The purpose of this Request for Quotation (RFQ) is to develop an Efficient Estimation Procedure for Models with High Dimensional Fixed Effects in Python (HDFE) for the United States International Trad... The purpose of this Request for Quotation (RFQ) is to develop an Efficient Estimation Procedure for Models with High Dimensional Fixed Effects in Python (HDFE) for the United States International Trade Commission (USITC or Commission). The trade data used by international trade models has many dimensions: exporting country, importing country, year, and industry. Newly available datasets, such as the International Trade and Production Dataset (ITPD) have data for many countries, industries, and, importantly, years. Estimating economic models with high-dimensional data requires high-dimensional fixed effects in the estimating equation to properly account for various country linkages and country characteristics. Many models developed at the Commission, including the models of trade policy uncertainty and the gravity model, use high-dimensional data and require HDFE. However, model estimation with HDFE using traditional statistical techniques is either infeasible or has very long solution times. This leads to reduced complexity of the models used at the Commission. Recently developed statistical methods make HDFE estimation feasible even with large datasets.