Tyler Pike - Research

Publications

Working Papers & Works in Progress

Google Scholar Profile

Publications

Bank Lending Standards and the U.S. Economy

Journal of Economic Dynamics and Control, 2026

Coauthored with Elijah Broadbent, Huberto Ennis, and Horacio Sapriza

Abstract: The provision of bank credit to firms and households affects macroeconomic performance. We use survey measures of changes in bank lending standards, disaggregated by loan category, to quantify the effect of changes in banks' attitudes toward lending on aggregate output, inflation, and interest rates. Bank lending to businesses is particularly important for macroeconomic outcomes, with peak effects on output of around half a percentage point after four quarters of the initial shock. These effects depend on the stage of the business cycle and the proximity of the short-term interest rate to its effective lower bound. The effects are larger when output is growing below trend and when the interest rate is away from its lower bound. We also find that the response of the economy to lending-standards shocks is asymmetric, with tightening shocks having larger effects on output.

Published Paper

Data: Micro-adjusted lending standard shocks

Working Papers

Banks as Firms: The Macroeconomics of Financial Firm Dynamics Job Market Paper

Abstract: This paper studies the firm dynamics of banks and their role in shaping aggregate and regional business cycles. I develop a framework in which banks are heterogeneous firms that endogenously enter and exit oligopolistic regional loan markets, competing on screening ability, funding costs, loan appeal, and regulatory costs. The model delivers sharp predictions linking loan rates, market shares, and entry decisions to bank fundamentals and local frictions. Guided by theory, I estimate bank-level fundamentals and region-level loan market frictions using a structurally identified dynamic state-space model. Screening ability emerges as the primary driver of bank growth and market share, while loan market frictions explain over half of aggregate loan spreads. Embedding the estimated bank dynamics in a quantitative multi-region New Keynesian DSGE model, I show that banking shocks account for roughly one-third of output fluctuations and generate substantial regional heterogeneity. Bank dynamics drive the uneven amplification and spatial propagation of shocks through novel credit elasticity and banking network channels, leading to the asymmetric regional transmission of national monetary policy.

Working Paper

2. Banks, Sentiments, and Business Cycles

Abstract: This paper measures the ``animal spirits” of U.S. banks and asks whether they are an important determinant of credit conditions and source of business cycle fluctuations. I first construct a novel semi-structural measure of bank-level sentiment, revealing heterogeneous animal spirits across banks and common dynamics marked by surges in pessimism during crises and excessive optimism during periods of elevated asset prices. I then jointly estimate the contribution of shocks to bank and household sentiment, aggregate demand and supply, financial risk, and monetary policy to fluctuations in macroeconomic conditions using a structural BVAR framework. Bank sentiment shocks explain 38% of the business cycle variation in credit conditions, 10% in output, 22% in prices, and 26% in the policy rate.

Working Paper

Works in Progress

Monetary Policy, Financial Vulnerabilities, and Macro Risks

Coauthored with Andrea Ajello

Financial Conditions and the Spatial Distribution of Entrepreneurship

Coauthored with Emin Dinlersoz, Timothy Dunne, John Halitwanger, and Veronika Penciakova

Uncertainty Shocks, Market Concentration, and the Entrepreneurial Funding Channel

Presented at the 2024 SEA meeting in Washington DC

Pre-Doctoral Research

Out-of-Sample Performance of Recession Probability Models

Coauthored with Francisco Vazquez-Grande

Abstract: This note discusses the out-of-sample (OOS) performance of several probit models used to assess the likelihood that the U.S. economy will be in a recession within the following year.

FEDS Note
Combining Forecasts: Can Machines Beat the Average?

Coauthored with Francisco Vazquez-Grande

Abstract: Yes. This paper documents the benefits of combining forecasts using weights built with non-linear models. We introduce our tree-based forecast combinations and compare them with benchmark equal weight combination as well as other nonlinear forecast weights. We find that nonlinear models can improve consistently upon the equal weight alternative–breaking the so-called ``forecast combination puzzle’’–and that our proposed methods compete well with other nonlinear methods.

Working paper | GitHub
Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults Using Machine Learning

Coauthored with Horacio Sapriza and Tom Zimmermann

Abstract: This paper constructs a leading macroeconomic indicator from microeconomic data using recent machine learning techniques. Using tree-based methods, we estimate probabilities of default for publicly traded non-financial firms in the United States. We then use the cross-section of out-of-sample predicted default probabilities to construct a leading indicator of non-financial corporate health. The index predicts real economic outcomes such as GDP growth and employment up to eight quarters ahead. Impulse responses validate the interpretation of the index as a measure of financial stress.

FEDS Paper | GitHub

Page updated

Google Sites

Report abuse