Summary
The lectures provide an applied presentation of standard forecasting models which are typically employed in policymaking institutions to forecast macroeconomic time series. Lectures are followed by practical sessions with hands-on computational exercises on MATLAB software. Installing MATLAB: Dauphine provides a free licence for students. Please use your own laptop if you can with MATLAB installed. Please have Datafeed, Optimization and Econometrics toolboxes installed. You will need to tick them in the package list during the installation of MATLAB.

# Real time macro-data on MATLAB

This is a must-read as all of the lectures will rely on DBnomics. The latter is a database aggregator which can be queried directly from MATLAB. Please read carefully this following note on DBnomics and download as well the MATLAB function which allows to get real time macro data:

## Introduction to MATLAB programming

This introduction should take no more of four hours of in class teaching.

Lecture 1: Introduction to MATLAB Programming
Objectives of the Lecture:

• Utilize the basic mathematical operations;
• Get know the general purpose commands of Matlab;
• Manipulate matrix calculus;

Materials:

Lecture 2: Plotting
Objectives of the Lecture:

• UPlotting 2D and 3D graphs;
• Define the curve shape, titles and legends.

Materials:

Lecture 3: Loops and Conditional Statements
Objectives of the Lecture:

• Mastering the use of conditional statements;
• Coding loops with both while and for.
Materials:

Lecture 4: Functions
Objectives of the Lecture:

• Write a function with n-input arguments;
• Code a function n-output variables;
Materials:

# Handouts list

Handout 1: Building a (stationary) dataset
Objectives of the Lecture:

• Understanding the concept of stationarity in time series.
• Mastering how to stationarize time series.
• Detection unit root components.

Materials:

Handout 2: The AR(p) Benchmark model
Objectives of the Lecture:

• Constructing a likelihood function;
• Estimating an AR(p) model;
• Performing forecasting analysis: both in-sample and out-sample;
• Finding the optimal lag using likelihood comparison test;
• Understanding the basis of the state-space representation.

Materials:

Handout 3: Vecto Auto-Regressive (VAR) models
Objectives of the Lecture:

• Building and estimate a VAR Model;
• Imposing restrictions on the VAR coefficients;
• Performing forecasting.

Materials:

Handout 4: DSGE models
Objectives of the Lecture:

• Estimate a DSGE model;