Bin Wang, Jinan University, [email protected]
This is the course page of PhD level advanced macroeconomics. I will update this website every week during the course. You can download the materials by clicking the link. You can contact me to obtain all the materials.
-
9.29. We introduced the syllabus of this course and learned some prerequisites of modern macroeconomics. Please download the lecture notes of Chapter 1. In this lecture, we learned why to take logarithm to the time series data and studied how to build the Hodrick-Prescott filter. We introduced the representative consumer's problem and related preference, utility function, indifference curve, marginal rate of substitution. We learned why the conditions that the partial derivatives of the Lagrangian function are zero are neccesary to the optimal solution of the optimization problem with equality constraint. You should also learn how to do comparative statics with implicit function theorem. Additionally, we proved the envelope theorem and its economic intuition. Problem Set 1 is posted.
-
10.06. We introduced the representative firm's problem, and the definition of competitive equilibrium, during which we learned Walras Law. We applied the implicit function theorem to study how we conduct comparative statics to the equilibrium characterization equations. Then we learned Pareto optimum and showed two fundamental theorems of welfare economics under some conditions. Please read Chapter 4 and 5 of Williamson (2018) to get more economic intution. You can also watch the recorded videos on bilibili: part 1, part 2, part 3.
-
10.13. We learned the two-period model with consumption-saving tradeoff. We showed the competative equilibrium and social optimum of this model. You should know what the key consumption-tradeoff is, namely what the Euler equation bears and what the Ricardian equivalence is.
-
10.20. Please download the lecture notes of Chapter 2. Answer of Problem Set 1 is posted. We learned a finite-horizon optimal growth model. We showed why Euler equation and "transversality condition" are both sufficient and neccesary for the solution. We numerically solved this model by Matlab. You can also watch the recorded videos on bilibili: part 1, part 2, part 3,part 4.
-
10.27. We introduced dynamic programming to solve a infinite-horizon dynamic model. You can watch the recorded videos on bilibili: part 1, part 2, part 3,part 4.
-
11.03. We continued to discuss dynamic programming. We proved why the solution of sequence problem is the same as that of functional equation. We deduced the Euler euqation and transversality condition for the solution of Bellman equation. We proved why these two conditions are both sufficient and neccessary for the solution of dynamic programming. You can watch the recorded videos on bilibili: part 1, part 2, part 3,part 4.
-
11.10. We introduced stochastic version of dynamic programming. Then we learned the general form of dynamic programming. You can watch the recorded videos on bilibili: part 1, part 2, part 3,part 4.
-
11.17. We rectified one mistake in last lecture. Then we introduced the Neoclassical growth model. You can watch the recorded videos on bilibili: part 1.
-
11.24. We provided more examples of dynamic programming such as Ramsey model with human and technology growth, Lucas tree model, and habit formation. You can watch the recorded videos on bilibili: part 1, part 2, part 3.
-
11.30. We continued to learn more examples. At last, we show that we can derive the first order conditions by directly apply the Lagrangian multiplier method quickly intead of refering to dynamic programming for DSGE models.
-
12.1. We started to learn how to solve the dynamic programming probelm by numerical method. Today we learned value function iteration and policy function iteration using the optimal growth model as the example.
-
12.8. We learned local method to solve the DSGE models, the first-order perturbation method. We introduced log-linearization to lineariza the first order conditions of DSGE models. We learned Blanchard and Kahn's method and Sims' method to get local solution.