Tyler J. Pike

Research Interests

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

Research Background

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

Selected Research Code

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

OOS for out-of-sample time series forecasting
Github | Website | CRAN
sovereign for state-dependent empirical analysis
Github | Website | CRAN

Tyler J. Pike's Projects

bear-toolbox

The Bayesian Estimation, Analysis and Regression toolbox (BEAR) is a comprehensive (Bayesian Panel) VAR toolbox for forecasting and policy analysis.

bottomupmacroindicators

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.

bvar_

Empirical macro toolbox

canmachinesbeattheaverage

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

dsge.jl

Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)

oos

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

sovereign

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