Three Day Course on the New Keynesian
Model

By Lawrence J. Christiano

I plan to review the basic New Keynesian model and some financial friction extensions that are currently under development. The course is aimed at a broad audience, including people actively doing research with dynamic, stochastic, general equilibrium (DSGE) models, as well as people interested in seeing a review of the structure of these models and what they are used for. There will be afternoon homework sessions. The sessions are designed to acquaint participants with Dynare as a tool for analyzing and estimating DSGE models. The first part of these sessions is integrally related to the lectures (especially (1) below), as they explore the fundamental properties and policy implications of the New Keynesian model. In the second part of the afternoon sessions, we will review the fundamentals of Bayesian inference and then do Bayesian inference using Dynare.

**Three Morning Lectures**

1)
The simple New Keynesian (NK) model without
capital (background: my handbook chapter).

Additional material, not covered in
lectures:

Ramsey-optimal monetary policy and the timeless perspective
(lecture handout,
longer handout, computer
code).

2) Medium Sized NK model (background: handbook chapter).

Additional material, not covered in lectures: Dynare code for a medium-sized New Keynesian model.

3) Introducing financial frictions into the New
Keynesian DSGE Model.

a)
Microfoundations
for the Costly State Verification (CSV) approach (section 6 of background paper).

b)
Integrating CSV
into an NK model and the results of Bayesian estimation of the model using US
and EA data (manuscript).

i)
The model.

ii)
The importance of risk shocks.

iii) The
response of monetary policy to an increase in interest rate spreads.

iv) Background
reading: Bernanke, Gertler and Gilchrist’s classic 1999
paper and Christiano-Motto-Rostagno.

c) Very brief discussion of extending CSV to risky banking
(discussion based on papers by Zeng
and by Hirakata, Sudo and Ueda).

d) Extension to small open economy (manuscript
and code, slides).

e) Additional
material on financial frictions specifically in the banking sector (slides, reading).

**Three Afternoon Sessions**

In the afternoon sessions,
participants can work with Dynare programs to
explore: (i) basic economic principles implied by the
New Keynesian model and (ii) methods for the empirical analysis of DSGE models,
including Bayesian inference. Part (i) will build on
part 1) of the morning lectures. Part (ii) will be preceded by a short lecture
on Bayesian inference (for a longer lecture that also reviews the state-space/observer
representation of a model, see this).
The afternoon sessions will center on doing the questions in assignment 9.

Apart from giving participants hands-on experience
with the quantitative analysis of models using Dynare, question 1 in assignment
9 allows us to discuss the following topics using the model developed in the
first lecture:

1) The sensitivity of the dynamic response of
inflation and output to the persistence properties of shocks.

a) Making precise the NK concepts of ‘insufficient
aggregate demand’ and ‘excessive aggregate demand’ (see section 3.4 of handbook chapter).

a) The rationale for the principle in the standard
NK model (see section 3.1 of handbook chapter).

b) The Taylor rule moves the interest rate in the
right direction in response to ‘standard’ shocks, but does not move it far
enough (see section 3.4 of handbook chapter).

3) Circumstances when things can go awry with the
Taylor principle:

a) An important working capital channel may
overturn the stabilizing properties of the Taylor principle (section 3.1 of handbook chapter).

b) News shocks may imply that the monetary
authority implementing the Taylor principle moves the interest rate in the
wrong direction (see the following slides; Christiano-Ilut-Motto-Rostagno, Jackson Hole
paper; and section 3.2 of handbook chapter).

Questions 2-11 in assignment 9 explore various
econometric issues related to the empirical analysis of dynamic models.
Question 2 studies the efficiency of the MCMC algorithm. Question 3 studies the
HP filter and evaluates its accuracy for estimating the output gap. Question 4
studies maximum likelihood estimation. Question 5 explores the tools for
Bayesian econometric inference for a DSGE model. Questions 6-11 examine other
topics in estimation of a
model, including Kalman smoothing and
forecasting. Most likely, we will do a strict subset of these questions.

**Assignment
#9**** **

The text for this assignment, as
well as all the necessary software, is included in this zip file.