### Updates

#### Exam solutions---finally!

I know it's taken me quite a while, but attached are solutions for the exam. There is a PDF plus several M-files with the Matlab codes. Let me know if there are any questions. I hope the skills you learned in the course will prove useful for you. Have a good summer! |

#### The exam!

Here it is, in case you need another copy. Remember---you are to work on it on your own. Good luck! |

#### A few more pages of Lecture 5...

Plus---a critical typo has been corrected! See if you can find where it was. |

#### Homework 5 solution

Here's what I get for the eight cases (M-files are attached): >>> ez_hw_shell hw_out = 1.00000 0.54451 5.78790 2.00000 3.26731 3.92600 3.00000 1.59097 21.68136 4.00000 5.77248 0.84773 5.00000 0.52521 2.55120 6.00000 3.16072 0.74717 7.00000 1.56741 17.95242 8.00000 5.66508 -2.23606 >>> The function file illustrates both methods of solving the EZ model---the one you used (where the SDF involves the value function), and the alternative where you substitute the value function out of the SDF using the return on the consumption claim. |

#### Rouwenhorst code for homework problem

Here is the Matlab code to implement Rouwenhorst's method for constructing Markov chains to approximate AR(1) processes. |

#### Partial notes for Lecture 5...

Not quite complete, but enough for now. Be careful, too---not thoroughly proofread. |

#### Solutions for Homework 4

See the attached PDF and M-files. Hopefully, the next set of lecture notes will be ready before class Wednesday. |

#### More complete version of Lecture 4

Added a bit more about EZ preferences, plus a discussion of the state-dependent preferences in Melino & Yang. I don't think we have time for disappointment aversion. After this, we'll move on to long-run risk and rare disasters. Lecture 5 notes will probably not be ready before class. |

#### Homework 3 solutions

Attached are solutions for Homework 3. The M-file covers both exercize 3.1.1 and 3.1.2. In the M-file, note especially the way I use Matlab's repmat function to save some steps. Type "help repmat" in Matlab to learn more. Using array multiplication ( .* ) and summing across rows (for example, sum(P.*R,2) for conditional expectation of R) also saves some steps. |

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