14.452 Economic Growth: Lectures 5 and 6, Neoclassical
Growth
Daron Acemoglu
M IT
November 10 and 12, 2009.
D aron A cemoglu (MIT)
E conomic G rowth L ectures 5 and 6
N ovember 1 0 and 1 2, 2 009.
1 / 7 1
Introduction
Introduction
Introduction
Ramsey o
14.452 Economic Growth: Lecture 11, Human Capital,
Technology Diffusion and Interdependencies
Daron Acemoglu
MIT
December 1, 2009.
Daron Acemoglu (MIT)
Economic Growth Lecture 11
December 1, 2009.
1 / 52
Human Capital and Technology
The Role of Human Cap
14.452 Economic Growth: Lecture 3, The Solow Growth
Model and the Data
Daron Acemoglu
MIT
November 3, 2009.
Daron Acemoglu (MIT)
Economic Growth Lecture 3
November 3, 2009.
1 / 55
Mapping the Model to Data
Introduction
Solow Growth Model and the Data
U
14.452 Economic Growth: Lecture 2: The Solow Growth
Model
Daron Acemoglu
MIT
October 29, 2009.
Daron Acemoglu (MIT)
Economic Growth Lecture 2
October 29, 2009.
1 / 68
Transitional Dynamics in the Discrete Time Solow Model
Transitional Dynamics
Review of
14.452 Economic Growth: Lecture 1, Stylized Facts of Economic Growth and Development and Introduction to the Solow Model
Daron Acemoglu
MIT
October 26, 2009.
Daron Acemoglu (MIT)
Economic Growth Lecture 1
October 26, 2009.
1 / 55
Growth and Development: T
Recursive Methods
Recursive Methods
Nr. 1
Outline Today's Lecture
continue APS:
worst and best value
Application: Insurance with Limitted Commitment stochastic dynamics
Recursive Methods
Nr. 2
B(W) operator
Definition: For each set W R, let B(W ) be the
Recursive Methods
Recursive Methods
Nr. 1
Outline Today's Lecture
Dynamic Programming under Uncertainty notation of sequence problem leave study of dynamics for next week Dynamic Recursive Games: Abreu-Pearce-Stachetti Application: today's Macro seminar
Recursive Methods
Recursive Methods
Nr. 1
Outline Todays Lecture
Anything goes: Boldrin Montrucchio Global Stability: Liapunov functions Linear Dynamics Local Stability: Linear Approximation of Euler Equations
Recursive Methods
Nr. 2
Anything Goes
treat
Recursive Methods
Introduction to Dynamic Optimization
Nr. 1
Outline Today's Lecture
neoclassical growth application: use all theorems constant returns to scale homogenous returns unbounded returns
Introduction to Dynamic Optimization
Nr. 2
Constant Retu
Recursive Methods
Introduction to Dynamic Optimization
Nr. 1
Outline Today's Lecture
discuss Matlab code differentiability of value function application: neoclassical growth model homogenous and unbounded returns, more applications
Introduction to Dynami
Recursive Methods
Introduction to Dynamic Optimization
Nr. 1
Outline Today's Lecture
finish off: theorem of the maximum Bellman equation with bounded and continuous F differentiability of value function application: neoclassical growth model homogenous a
Recursive Methods
Introduction to Dynamic Optimization
Nr. 1
Outline Today's Lecture
study Functional Equation (Bellman equation) with bounded and continuous F tools: contraction mapping and theorem of the maximum
Introduction to Dynamic Optimization
Nr.
Recursive Methods
Introduction to Dynamic Optimization
Nr. 1
Outline Today's Lecture
housekeeping: ps#1 and recitation day/ theory general / web page finish Principle of Optimality: Sequence Problem solution to Bellman Equation
(for values and plans)
be
Recursive Methods
Introduction to Dynamic Optimization
Nr. 1
Outline Today's Lecture
finish Euler Equations and Transversality Condition Principle of Optimality: Bellman's Equation Study of Bellman equation with bounded F contraction mapping and theorem
14.128. Problem Set #3
1 Neoclassical Growth: Linear and Non-Linear Speed of Convergence
Consider the neoclassical growth model with u (c) = c1- / (1 - ) G (k, 1) = k and depreciation rate . (a) Using the linearized dynamics compute several tables showing
Problem Set #2: Recursive Methods
Spring 2003
1
Differentiability of the value function
This problem is for those that would like to attempt it. There is no need to hand it in. For any dynamic program show that the value function, v (), is differ entiable
1
Solutions Pset 3
1) Do some programing 3) Brock Mirman problem a) Take V = a1 log k + a2 log + a3 . Then the max problem is T V (k) = T V (k) = max ln (Ak - k0 ) + E [a1 log k0 + a2 log + a3 ] ln (Ak - k0 ) + a1 log k0 + a2 E log + a3 1 a1 + 0 =0 k Ak -
14.128 Recursive Methods: Problem Set #1
Solve the following important exercises from SLP. In some cases you only have turn in a subset of the exercises you are required to do. However, you are expected and encouraged to solve and learn the material in al
Problem Set 1
1
3.2
Answers to the required problems
a) Take any three vectors x, y, z in Rl and two real number , R. Define the zero vector = (0, ., 0) Rl . To check that it is a vector space, define the sum of two vectors as the vector of the sum elemen
14.461 Part II Problem Set 2
Fall 2009
1
Unemployment Insurance and Saving
This problem studies unemployment insurance in the presence of a moral hazard problem, when agents are allowed to privately save (based on Shimer and Werning). There is a represent
14.461 Part II Problem Set 1, Solutions
Fall 2009
1
Wage Dispersion
This problem extends the search model with random matching and Nash bargaining seen in class to allow for match-speci.c productivity. This simple extension will be able to generate wage d
14.461 Part II Problem Set 1
Fall 2009
1
Wage Dispersion
This problem extends the search model with random matching and Nash bargaining seen in class to allow for match-speci.c productivity. This simple extension will be able to generate wage dispersion.
14.461 Problem Set 2
Fall 2009
Problem 1: Imperfect information on technology
[Based on Angeletos and La' 2009] O, Consider an economy with a continuum of sectors [0; 1]. In each sector there is a continuum of producers. The representative producer in sec