COMPLETED MACROECONOMICS (AND RELATED) COURSES/WORKSHOPS IN PHD

Macro-Finance by Markus Brunnermeier at Princeton University, June - July 2024.

Broad Topics Covered: 

• Continuous Time Modeling Without Diffusion Risk (Kiyotaki and Moore)

• Continuous Time Stochastic Optimization (Consumption, Portfolio)

• Endogenous Risk Dynamics with Log Utility

• Contrasting Financial Frictions

• Monetary Models with One Sector

• Different Monetary Theories

• Numerical Methods  and Value Function Iteration

The QuantEcon Workshop by Thomas J. Sargent and John Stachurski, August 2022.

Broad Topics Covered: 

• Python’s Scientific Ecosystem: NumPy, SciPy, Matplotlib, Pandas, Numba

• Foundations of statistics and probability: Law of Large Numbers (LLN) and the Central Limit Theorem (CLT), discrete-time dynamics, object-oriented programming in the context of the Python programming language, Heavy Tailed Distributions

• McCall Search Model, Inventory Dynamics, Lake Model of Employment and Unemployment, Job Search with Learning building on the standard McCall model, Exchangeability and Baysian Updating, Linear Quadratic Control, sequential analysis by Abraham Wald, Kalman filter in the context of the Multivariate Normal Distribution

Advanced Macroeconomics at IIT Delhi (sat through and worked as teaching assistant), 2023, 2024.

Broad Topics Covered: 

• Review of IS-LM Model, A neo-classical model of labor market, Adaptive expectations and aggregate supply, Dynamics in AD-AS model with adaptive expectation 

• Rational expectations and Lucas aggregate supply curve, Rational expectaion, Lucas critique and Policy Irrelevant Proposition (PIP), A wage contract model and implications for PIP  

• Introduction to inter-temporal choice: A dynamic theory of investment, Mathematical groundwork: Vector space, metric space, normed vector space, Mathematical groundwork: Linear transformation, contraction mapping, fixed point, Mathematical groundwork: Functional derivative, Euler-Lagrange equation, Optimal Control: Hamiltonian, Pontryagin’s maximum principle

• Transitional dynamics, phase diagram, saddle-path stability, Application: Ramsey model, Dynamic programming: Bellman’s principle of optimality, Hamilton-Jacobi-Bellman Equation (HJB), Value function iteration, policy function iteration

• Numerical methods, Stochastic dynamic programming, Application: Social planners problem, with Markov shocks, Competitive equilibrium: Arrow-Debreu Equilibrium, Sequential Market Equilibrium and Recursive Competitive Equilibrium, One-sector RBC model, A DSGE model with nominal rigidity

Macroeconomic Dynamics at IIT Delhi, part of PhD Coursework, 2020.

Broad Topics Covered: 

• General Equilibrium in a One-Period Competitive Model, Savings and Investment in a Two-Period Competitive Model, General Equilibrium in the Two-Period Model, Diagrammatic Exposition of the Intertemporal Equilibrium, The Treatment of Time and the Intertemporal Approach

• The Solow Model: Savings, Investment and Economic Growth, The Kaldor Stylized Facts of Economic Growth, Dynamic Simulations of the Model, 

• The Representative Household Model of Optimal Growth: Ramsey Model, Dynamic Simulations of the Model, Overlapping Generations Models of Growth: The Diamond Model, Dynamic Simulations of a Calibrated Diamond Model, The Blanchard-Weil Model in Discrete Time: Dynamic Simulations of the Model

• Dynamic Stochastic Models under Rational Expectations, The Rational Expectations Hypothesis, Stock Prices in Efficient Capital Markets, The Cagan Model of Money Demand, Second-Order Linear Expectational Models

• The Stochastic Growth Model of Aggregate Fluctuations, Steady State, Log-Linearizing, A Dynamic Simulation of the Log-Linear Stochastic Growth Model

• Keynesian Models and the Phillips Curve, The Theory of Discretionary Monetary and Fiscal Policy, Inflation and Unemployment under Rational Expectations and Adaptive Expectations, A Model of Imperfect Competition and Staggered Pricing, A Dynamic Simulation of the Model, Real and Monetary Shocks and Aggregate Fluctuations

Time Series Econometrics at IIT Delhi, part of PhD Coursework, 2020. 

Broad Topics Covered: 

• Serial correlation, Auto-regressive series, Random walk, Spectral analysis

• Stochastic process, Stationarity, Ergodicity, Correlation of two series, Multiple linear regression in a time series, OLS in the context of TS data, Contemporaneous and strict exogeneity of regressors, Static model, Linear trend model, Finite Distributed Lag Models, Trends and Seasonality, Comparison of models, Drift estimate, Fractional differencing

• Smoothing, ARMA model, Characteristics equations, eigenvalues, eigenvectors, Stationarity with complex roots, Invertibility, Causal model, Redundant parameterization, Q-statistic

• Minimum MSE Predictor, Wold’s Decomposition, Box-Jenkins Modelling Philosophy, Estimations using R, Diagnostics, Stability of the model

• Non-stationary Models, Unit root and Stochastic trend, Deterministic vs. stochastic trend, Random walk, Stationary process with structural break, Critique of Perron, Endogenous Structural Break

• ARIMA model, VAR model, Granger causality, Structural VAR, Cointegration, Error Correction Models, VECM model