Renato Zimmermann

Renato Zimmermann

PhD Student in Economics, University of Toronto

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About Me

I am a third-year PhD student in Economics at the University of Toronto. My research is situated at the intersection of macroeconomics, household finance, and computational economics. I focus on how the decisions of heterogeneous households and entrepreneurs interact with monetary and fiscal policy. My work aims to build more realistic models of the economy to better understand the effects of policy on investment, hiring, and economic stability.

Research

Working Papers

  • Leverage, Default and the Entrepreneurial Channel of Policy Transmission (Work in Progress)

    Abstract

    This paper develops a heterogeneous-agent New Keynesian model to study the investment channel of monetary policy transmission through risk-averse, credit-constrained entrepreneurs. The model provides a new perspective on fiscal policy, demonstrating that debt-financed expansions can be significantly dampened by a crowding-out effect on entrepreneurial investment.

  • The Retirement Gap: Entrepreneurs, Tax Policy, and the Cost of Liquidity (Work in Progress)

    With: Sebastian Dyrda, Baxter Robinson

    Conferences: Winter SED (Argentina, 2024); BSE Summer Forum (2025); Summer SED (Copenhagen, 2025)

    Abstract

    This paper studies how entrepreneur's income risk affects their wealth accumulation and portfolio allocation. We document novel facts about the portfolio allocation of entrepreneurs, showing that entrepreneurs hold substantial financial assets outside their business. We then build a life-cycle model of entrepreneurship that features risky intangible capital. The risky nature of intangible capital helps our model match the portfolio allocation of entrepreneurs of assets inside vs. outside their business. We demonstrate the aggregate importance of this mechanism by studying the aggregate cost of financial frictions in a model with and without intangible capital. We also study two policy applications: an expansion of small business loans and a fiscal stimulus transfer showing that the effects of both policies depend critically on the presence or absence of risky intangible capital.

  • Demand, Spillovers, and Declining Entry (Work in Progress)

    With: Mahmood Haddara

    Abstract

    The rate of innovation in a market is closely related to its level of competition. As such, endogenous growth models increasingly incorporate rich interactions between innovation and market structure. In this paper, we develop a model of endogenous growth and oligopolistic competition that distinguishes between two margins of dynamic investment: i) innovation and ii) demand (branding, marketing, etc.). Demand investment increases the distance between incumbents and new firms, naturally deterring entry. This gives rise to negative spillovers, in stark contrast to the positive spillovers of innovation. If governments cannot fully distinguish true innovation from demand investment, this can dampen the welfare gains from R&D subsidies. Previous oligopolistic growth models have abstracted from the demand-innovation distinction in large part due to technical infeasibility (i.e., the curse of dimensionality). We show that recent computational methods can be used to overcome this barrier. Among other techniques, we represent the state and operator spaces using sparse matrices. This allows us to make use of modern linear algebra libraries and graphics processing units (GPUs), enabling computation even with an exceptionally large state space.

Pre-PhD

  • Common Errors in Machine Learning Projects: A Second Look (2024)

    Conferences: Koli Calling '23

    Abstract

    While machine learning (ML) has proved impactful in many disciplines, design decisions involved in building ML models are difficult for novices to make, and mistakes can cause harm. Prior work by Skripchuk et al. [35] identified common errors made by ML students via qualitative analysis of open-ended ML assessments, but their sample was limited to a single institution, course, and assessment setting. Our work is an extended, conceptual replication of this work to understand the common errors made by machine learning students. We use a mixed-method approach to analyze errors in 30 final project reports in an undergraduate machine learning course. The final reports describe the model-building process for a classification task, where students build models on a complex data set with numerical, categorical, ordinal and text features. Our choice to analyze project reports (rather than code) allows us to uncover design errors via how students justify their methodology. Our project task is to achieve the best test accuracy on an unseen test set; thus, as a way to validate these common errors, we identify the association between these errors and the model’s test accuracy performance. Common errors we find include those consistent with Skripchuk et al. [35], for example issues with data processing, hyperparameter tuning, and model selection. In addition, our focus on design error exposes other common errors, for example where students use certain kinds of features (e.g., bag of words representations) only with particular models (e.g., Naive Bayes). We call these latter types of errors model misconceptions, and such errors are associated with lower test accuracy. Some of these errors are also present in work by practitioners. Others reflect a difficulty by students to make correct connections between ML concepts and achieve the relational level of the SOLO taxonomy. We identify areas of opportunity to improve machine learning pedagogy, particular... [truncated]

  • Modeling Social Learning Using Dyna-Q and Ant Colony Optimization (2023)

    Conferences: The Carroll Round XVII

    Abstract

    This paper introduces a novel way of modeling social learning in macroeconomics using techniques from model-based reinforcement learning and ant colony optimization. The work extends previous works in bounded rationality and social learning by providing tools to complement previously-distinct models in adaptive learning. We test these new techniques using simulations of job search and consumption. Results demonstrate that models fit using the proposed techniques can learn core economic behaviors given no information about the environment, but do not fully fit reward functions in line with rational expectations theory.

Education

  • PhD in Economics, University of Toronto (2023 - Present)
  • MA in Economics, University of Toronto (2022 - 2023)
  • HBSc in Computer Science and Economics, University of Toronto (2018 - 2022)
  • Workshop on Heterogeneous-Agent Macroeconomics, NBER (2025)
  • Optimization-Conscious Econometrics Summer School, University of Chicago (2023)

Experience

  • Course Instructor, University of Toronto
    Courses
    • Economics
    • ECO482: Machine Learning Applications in Macroeconomic Finance

    • Computer Science
    • CSC258: Computer Organization
    • CSC207: Software Design
  • Teaching Assistant, University of Toronto
    Courses
    • Economics
    • ECO2107 (Graduate): Monetary Theory
    • ECO2101 (Graduate): Macroeconomic Theory II (PhD)
    • ECO2100 (Graduate): Macroeconomic Theory I (PhD)
    • ECO2460 (Graduate): Economic Applications of Machine Learning
    • ECO482: Machine Learning Applications in Macroeconomic Finance
    • ECO375: Econometrics I
    • ECO227: Quantitative Methods in Economics
    • ECO208: Macroeconomic Theory
    • ECO200: Microeconomic Theory
    • ECO102: Principals to Macroeconomics
    • ECO101: Principals to Microeconomics
    • ECO100: Introduction to Economics

    • Computer Science
    • CSC367: Parallel Programming
    • CSC311: Introduction to Machine Learning
    • CSC236: Introduction to the Theory of Computation
    • CSC148: Introduction to Computer Science
    • CSC108: Introduction to Computer Programming
  • Research Assistant, University of Toronto
    Details
    • Murat Celik
    • Re-write and optimize Matlab code in Julia to solve a heterogeneous agent macroeconomic model.
    • Achieve 30x speed improvement by leveraging advanced code optimization and numerical procedures.
    • Adapt the model to be fully compliant of automatic differentiation to improve estimation efficiency.

    • Sebastian Dyrda
    • Design, implement and test global nonlinear optimizer in modern object-oriented Coarray (MPI) Fortran.
    • Read literature and study techniques to optimize code for high performance computing environments.
    • Run and profile massively-distributed models in up to 6000 cores in the Compute Canada supercomputer.

    • Xu Tian
    • Create high-performance parallel algorithms in C++ to scrape, process and analyze 4TB+ of financial data.
    • Design and implement dynamic structural models for research in corporate finance using Matlab and Fortran.
    • Accelerate computationally-intensive routines with GPUs and use MPI to parallelise estimation.

    • Marlene Koffi
    • Implement language processing models using techniques from machine learning and neural networks.
    • Parallelize and optimize mathematical code for research in gender economics.
    • Profile code used for large scale parallelism in supercomputer clusters.

    • Ismael Mourifie
    • Translate structural econometric models from Matlab to Fortran and optimize memory and cache efficiency.
    • Design and implement distributed implementations of main research algorithms.
    • Improve total program execution speed from 1.2 hours (reported by PI) to 6.8 seconds.