Proficiency in Diverse Technologies: Our technical experts are adept at a wide array of data engineering technologies, including SQL, Python, SAS, Hadoop, Spark, and more, enabling us to handle diverse data infrastructures.

ETL Development: We have extensive experience in developing robust ETL (Extract, Transform, Load) pipelines, ensuring efficient data extraction from various sources, transformation into usable formats, and loading data into mainframes, databases, and data warehouses.

Real-Time Data Processing: We are capable of setting up real-time data processing systems, which are crucial for time-sensitive operations.

Data Governance and Quality Assurance: Our team implements stringent data governance policies and ensures high data quality through rigorous validation and cleaning processes.

Security Measures: We incorporate best practices for data security and adhere to all relevant data protection regulations.

End-to-End Data Solutions: Our data engineering services cover the entire data lifecycle, from initial data gathering and processing to final analysis and visualization.

Natural Language Processing (NLP): Leveraging text mining cutting-edge NLP techniques, we develop applications that understand, interpret, and generate human language, offering applications such as topic modeling, text summarization and sentiment analysis.

Supervised and Unsupervised Learning: Our data scientists are experienced in both supervised (learning from labeled data) and unsupervised (discovering hidden patterns in unlabeled data) machine learning methods.

Reinforcement Learning: Our team is skilled in reinforcement learning, a type of machine learning where an agent learns an optimal decision-making policy by interacting with its environment.

Model Tuning: We understand that effective machine learning requires more than just implementing models. We fine-tune models for optimal performance, adjusting parameters to improve accuracy and prevent overfitting.

Feature Selection: We employ various feature selection techniques to identify and select the most relevant variables for our clients’ models, which improves model performance and makes the model easier to interpret.

Deep Learning Capabilities: We work with neural networks and deep learning models for complex tasks like time-series forecasting, image and speech recognition, allowing for high-accuracy solutions for complex problems.

Cross-Domain Expertise: Our machine learning expertise spans across multiple industries, from healthcare to finance and Census-related areas, providing our clients with insights tailored to their specific needs.

The Econometrica team has performed multiple evaluation studies for federal and state agencies. Our team brings the following program evaluations skills and expertise:

• Design and implementation of innovative quantitative analyses, including applying cutting-edge Machine Learning techniques to address evaluation questions.
• Expertise in mixed-methods evaluation, balancing quantitative and qualitative research to produce robust and actionable evaluation results.
• Knowledge and experience leveraging secondary data sets critical for evaluations, such as nation-wide survey data, census data, and healthcare data.
• Experience in a variety of primary data collection techniques, ranging from common methods like surveys to novel approaches like web scraping.
• Experience conducting large-scale qualitative research, including qualitative design, data collection, analysis, and reporting.

Economic Analytics Expertise: Our Economists are experienced in performing different types of economic analyses, including:

• Benefit-cost analysis (BCA)
• Cost-effectiveness analysis (CEA)
• Break-even analysis
• Return on investment (ROI)
• Supply/demand analysis
• Risk and uncertainty analyses, including Monte Carlo simulation and solutions for addressing low-probability, high consequence (LPHC) events
• Economic impact analyses including the estimation of direct, indirect and induced impacts.

Economic modeling and profiling: Our team is skilled in the development of economic models for simulation, estimation, and forecasting purpose. We are also experienced in the construction of industry and market profiles.
Federal Rulemaking: We support of all phases of the Federal rulemaking process, from Notices of Proposed Rulemaking (NPRM) through Final Rules:

• Determination of the need for regulation and identification of regulatory alternatives
• Regulatory impact analysis (RIA), Small Business and Regulatory Flexibility Act analyses (RFA), Paperwork Reduction Act (PRA) analyses, Unfunded Mandates Reform Act (UMRA) analysis, etc.
• Summary and analysis of public comments as well as development of agency responses and OMB clearance packages.

Technical writing: Econometrica develops technical documents in conformance with the Federal Register Document Drafting Handbook and other style guides.

Expertise across Multiple Platforms: We are proficient in various data visualization platforms, including Power BI, Tableau, Amazon Quicksight, JavaScript, SAS, R, Python, and Excel, allowing us to choose the best tools for our clients’ needs.

Advanced Dashboard Capabilities: Our team excels at creating user-friendly, intuitive dashboards that provide clear and concise insights, making complex data understandable for all users regardless of their technical expertise.

Geospatial Analytics: We leverage our geospatial analysis skills to visualize geographic and location-specific data, providing critical insights to enhance decision-making processes.

Narrative Insights with Infographics: Our visualization experts excel at transforming raw data into compelling infographics that tell a clear, concise story, aiding in comprehension and engagement.

Real-Time Data Visualization: We have the capability to create real-time data visualizations, empowering businesses to make immediate decisions based on the very latest data.

Econometrica’s team of expert statisticians, mathematicians, and economists are skilled in the following statistical analyses and sampling methods:

• Parametric and nonparametric statistical analysis
• Linear and nonlinear regression modeling

• Simultaneous equation systems
• Two-stage and three-stage least squares
• Seemingly unrelated regression
• Logit, probit, and tobit models
• Instrumental and dummy variable models
• Difference-in-difference models
• Random and fixed-effects models
• Survival models
• Cox Proportional Hazard Model
• ARIMA, ARMA, and exponential time-series models
• Modeling to correct for sample-selection bias (e.g., Heckman), heteroskedasticity (e.g., white tests), and serial correlation
• Hausman tests and tests for specification errors—omitted variables, irrelevant variables, incorrect functional form, etc.

• Empirical Bayesian modeling
• Markov processes
• Bootstrap and Monte Carlo simulations
• Hypothesis testing
• Analysis of Variance (ANOVA)
• Propensity score and statistical matching
• Identification and treatment of missing data
• Simulation modeling
• Optimization models
• Artificial intelligence/machine learning
• Identification of outliers
• Imputation and extrapolation
• Forecasting and predictive analyses
• Independent, third-party, methodology review