Skip to content
View tylerJPike's full-sized avatar
23 followers · 6 following

Achievements

Achievement: StarstruckAchievement: Pull Shark

Achievements

Achievement: StarstruckAchievement: Pull Shark

Highlights

  • Pro

Block or report tylerJPike

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don't include any personal information such as legal names or email addresses. Markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
tylerJPike/README.md

Tyler J. Pike

Research Interests

  1. Macroeconomics: Macro-Finance, Monetary Policy, Firm Dynamics
  2. Econometrics: Non-linear statistics and machine learning, time series analysis and macroeconometrics

Research Background

  1. University of Maryland
    current position
    • PhD student in economics
    • Research Assistant to John Haltiwanger
  2. Federal Reserve Board
    • Research Assistant to Vice Chair Clarida
    • Research Assistant to Macro-Financial Anlaysis Section
  3. University of Richmond
    • BS in Mathematical Economics
    • Economics Research Assistant and Research Fellow

Selected Research Code

  1. "Combining forecasts: Can machines beat the average?"
    with Francisco Vazquez-Grande, September 2020
    Github | Working Paper

  2. "Bottom-up leading macroeconomic indicators: An application to non-financial corporate defaults using machine learning"
    with Horacio Sapriza, and Tom Zimmermann, September 2019
    Github | Working Paper

Statistical Software Packages

  1. OOS for out-of-sample time series forecasting
    Github | Website | CRAN

  2. sovereign for state-dependent empirical analysis
    Github | Website | CRAN

Github badge Github badge Github badge Linkedin Badge Website Gmail Badge

Pinned Loading

  1. OOS OOS Public

    Out-Of-Sample Time Series Forecasting: OOS introduces a comprehensive framework for time series forecasting with traditional econometric and modern machine learning techniques.

    R 11 2

  2. CanMachinesBeatTheAverage CanMachinesBeatTheAverage Public

    Replication files for "Combining forecasts: Can machines beat the average?" by Tyler Pike and Francisco Vazquez-Grande.

    R 1 2

  3. sovereign sovereign Public

    State-Dependent Empirical Analysis: tools for state-dependent forecasts, impulse response functions, historical decomposition, and forecast error variance decomposition.

    R 16 6

  4. BottomUpMacroIndicators BottomUpMacroIndicators Public

    Replication files for "Bottom-Up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults Using Machine Learning" by Tyler Pike, Horacio Sapriza, and Tom Zimmermann.

    R 1 2