Here you can find some of my notes on different courses I have taught over the years on computation, macroeconomics, and economic history. Since these are teaching notes, I borrow some material from papers, books, etc., both mine and of different people. To the best of my understanding, all that material is covered by the "fair use" doctrine or under creative commons licenses.
This set of lecture notes has been prepared for my one-year (two semesters) course on computational methods for economists. See, also, my course on machine learning and on estimation of dynamic equilibrium models for extra material on machine learning, reinforcement learning, Bayesian methods, and simulation. Lecture 1: High-Performance Computing in Economics. Lecture 2: Software Engineering. Lecture 3: OS and Basic Utilities. Lecture 4: Concepts on Programming Languages. Lecture 5: Scientific Computing Languages. Lecture 6: Coding Tools. Lecture 7: Programming Paradigms. Lecture 8: The Elements of Programming Style. Lecture 9: Data Handling. Lecture 10: Web Scrapping. Lecture 11: Paralellization. Lecture 12: FPGAs in Economics. Lecture 13: Numerical Differentiation and Integration. Lecture 14: Optimization. Lecture 15: Dynamic Programming. Lecture 16: Computational Complexity. Lecture 17: Nonlinear Methods. Lecture 18: Projection Methods. Lecture 19: Perturbation Methods I, Basic Results. Lecture 20: Perturbation Methods II, General Case. Lecture 21: Perturbation Methods III, Change of Variables. Lecture 22: Perturbation Methods IV, Perturbing the Value Function. Lecture 23: Perturbation Methods V, Pruning Lecture 24: Appendix on Linearization. Lecture 25: Heterogeneous Agent Models I. Lecture 26: Heterogeneous Agent Models II. Lecture 27: Heterogeneous Agent Models III. Lecture 28: Heterogeneous Agent Models IV. Extra material: Chapter on software engineering for economists. Chapter on Unix. Chapter on Git. Chapter on Make. Chapter on notebooks, markdown, and Pandoc. Chapter on Julia. Now for Julia 1.1!Also, check my script for a 4-hour tutorial on Julia here and a good cheat sheet here. A Practical Guide to Parallelization in Economics.My github page: here.The github page on parallelization: here.Some codes:A basic RBC model.An RBC model with stochastic volatility.An RBC with EZ preferences, Taylor rule, and yield curve.An RBC computed with Chebyshev polynomials.An example of memory locality.Machine Learning for MacroeconomicsThis set of lecture notes has been prepared for a course on machine learning for macroeconomics. Lecture 1: Machine Learning for Macroeconomics. Lecture 2: Coding Machine Learning Algorithms. Lecture 3: Introduction to Deep Learning. Lecture 4: Optimization in Deep Learning. Lecture 5: Challenges Solving Economic Models. Lecture 6: Deep Learning for Solving Economic Models. Lecture 7: Advanced Topics in Deep Learning. Lecture 8: Symmetry in Dynamic Programming. Lecture 9: Transversality and Stationarity with Deep Learning. Lecture 10: Reinforcement Learning. Lecture 11: Machine Learning for Data Analysis. Lecture 11: Text Analysis. Lecture 12: Large Language Models.Solving and Estimating Dynamic Equlibrium ModelsThis set of lecture notes is the backbone of a course on formulation, computation, and estimation of dynamic general equilibrium models. The notes have been written jointly with Juan Rubio-Ramirez at Emory University. Lecture Notes 1: Introduction Lecture Notes 2: Formulating Dynamic EquilibriumModels Lecture Notes 3: Solving Dynamic EquilibriumModels Lecture Notes 4: Introduction to BayesianEconometrics Lecture Notes 5: Monte Carlo Methods Lecture Notes 6: Markov Chain Monte Carlo Lecture Notes 7: Metropolis-Hastings Lecture Notes 9: Filtering Theory Lecture Notes 10: Model Comparison Lecture Notes 11: Inference Lecture Notes 12:Nonlinear and/or Non-gaussian Filtering
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This set of transparencies are prepared for a graduate course inmacroeconomics. Dirk Krueger is the source of much material in them and some of the material is borrowed from standard first-year textbooks (Acemoglu, L-S, SLP, etc.). LectureNotes 1: Introduction to Uncertainty. Lecture Notes 2: Equilibrium with Complete Markets. LectureNotes 3: Asset Pricing. LectureNotes 4: OLG Models. LectureNotes 5: Topics in OLG Models. LectureNotes 6: OLG Models with Production. LectureNotes 7: Job Search. LectureNotes 8: Random Matching. LectureNotes 9: Search Theoretic Models of Money. LectureNotes 10: Neoclassical Growth Model. LectureNotes 11: Endogenous Growth Models. LectureNotes 12: RBC Models. LectureNotes 13: Ramsey Fiscal Policy. Technical Lecture Notes 0: Measure Theory. Technical Lecture Notes 1: Stochastic DynamicProgramming. Technical Lecture Notes 2: Numerical Dynamic Programming. Technical Lecture Notes 3: Continuous Time Stochastic Processes. Technical Lecture Notes 4: Optimization inContinuous Time. Technical Lecture Notes 5: Spectral Analysis. Technical Lecture Notes 6: NIPA.
Lecture Notes 1: Motivation. Lecture Notes 2: Dynamic Programming in Continuous Time. Lecture Notes 3: Deep Learning and Reinforcement Learning. Lecture Notes 4: Heterogeneous Agent Models. Lecture Notes 5: Optimal Policies with Heterogeneous Agents.
This set of transparencies are prepared for a graduate class in macroeconomics with financial frictions. LectureNotes 1: Macroeconomic Models with Financial Frictions. Lecture Notes 2: A Model with Collateral Constraints. LectureNotes 3: A Model with Costly-State Verification. LectureNotes 4: A Model with Costly Enforcement. LectureNotes 5: A Model of Financial Intermediation. LectureNotes 6: A Model with Explicit Solution.
This set of lecture notes is an undergraduate class on Macroeconomics taught from an equilibriumperspective. There is more than enough material for a semester course and probably enough for a oneyear sequence. When I teach this class I pick and choose from those lecture notes.This is a work in progress and I will welcome any comments! Lecture Notes on Macroeconomics
This set of lecture notes is the backbone of a course on Global Economic History.I have borrowed material, tables, and figures from many researchers' work as well as received detailed comments from top economists. Among others, I owe either a direct or an indirect debt to Daron Acemoglu, Robert Allen, Mike Dotsey, Mark Koyama, Joel Mokyr, Nathan Nunn, Kevin O'Rourke, and Jim Robinson. I am working on a formal draft of these notes, where I will make all the attributions explicit.Of course, I warmly welcome comments (and the pointing out of errors!). Lecture Notes 0: Empirical Strategies in Economic History Lecture Notes 1: Introduction Lecture Notes 2: Classical Greece Lecture Notes 3: Ancient Rome Lecture Notes 4: The Islamic World Lecture Notes 5: China (to be added later). Lecture Notes 6: Contacts Lecture Notes 7: Malthus: Population and Economic Growth Lecture Notes 8: Geography, Environment, and Climate: the "Real" Real Shocks Lecture Notes 9: Energy: The Mover of Output Lecture Notes 10: Sea Empires Lecture Notes 11: Land Empires Lecture Notes 12: Europe Gets Ahead Lecture Notes 13: Cradle of Modernity Lecture Notes 14: Catching Up, Falling Behind Lecture Notes 15: The Strange Death of Liberal Europe Lecture Notes 16: False Hopes: Communism and Fascism Lecture Notes 17: Africa Lecture Notes 18: Les Trente Glorieuses Lecture Notes 19: New Countries: Failures and Successes Lecture Notes 20: The East is Red Lecture Notes 21: Death and Transfiguration Lecture Notes 22: Back to the Future: the Global Recession
Consequentially, higher education on feminist economic topics has been scarce, if not non-existent. Only since 2016 a third professorship with a denomination in economics and gender has been appointed in Germany. However, its geographical and institutional location in the province at a university of applied sciences still reveals a lot about the institutional gatekeeping of the economics profession in Germany. Internationally, a number of professors are known for their work in feminist economics, but they are not institutionally marked with a denomination as professors of feminist economics, the chair of feminist economics at the University of Valencia being the exception.
These historical and institutional circumstances require feminist economics courses to work towards building competences for entering a discourse. Moreover, they pose three major questions for educators who want to teach feminist economics: What can and should instructors in feminist economics teach without being able to rely on a canon? How can instructors teach feminist economics so that it is perceived as economically relevant despite not being anchored in mainstream economics departments? And how can it be taught given that students from diverse backgrounds and disciplines want to engage with feminist economics?
A key field of inquiry for feminist economics was always social reproduction and has become care work in the recent decades. Both topics center around activities traditionally done by women and people of color in many societies, but especially in Western capitalist societies. While social reproduction can refer to the reproduction of the social conditions of capitalist production, the reproduction of the labor force as well as biological reproduction (Edholm et al. 1978), I understand care work in a narrower sense as the caring activities for people who are not completely autonomous due to age, illness or a disability (Jochimsen 2003). Debates about social reproduction seek to clarify what role this plays in the overall reproduction of society and continued capitalist accumulation, whereas debates around care work highlight the specificities of care work versus other work. Both key concerns have been discussed on a spectrum reaching from an inclusion of house- and care work into the Marxian value theory of labor to an ethical theory of care work. 152ee80cbc
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