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Most of the tutorials are collections of m-files and pdf files (created by publishing the corresponding m-files). Each tutorial usually includes its own master file named read_me_first.m and read_me_first.pdf. This is the best place to start.

The tutorials are divided into the following main categories:

- Macroeconomic Modeling
- Model File Language and Preparser
- Time Series Analysis and Forecasting (VAR, SVAR, BVAR, Panel VAR, VARX, FAVAR, X12/X13, HP)
- Database and Time Series Management
- Reporting
- Other Topics

The tutorial is a collection of example files for a simple sticky-price business cycle model (SPBC). The m-files included describe a typical workflow in building and operating a small-scale DSGE models for practical policy analysis and forecasting.Log Minus Models by Jaromir Benes (Latest Update 20140814)

This tutorial shows how to construct models with variables that need to be log-linearised (because they are growing at a constant rate in steady state) but whose sign in steady state is either negative or cannot be decided ex ante (they can take either a positive or negative steady-state value).Optimal Policy Models under Discretion and Commitment by Jaromir Benes (Latest Update 20140319)

This tutorial describes how to use IRIS to calculate two basic types of optimal policies, discretion and commitment, and run various types of experiments with models based on them. The experiments include simulating shocks, simulation disinflation, and drawing policy frontiers to describe policy trade-offs.Nonlinear Simulations by Jaromir Benes (Latest Update 20140401)

In this tutorial, we show how to simulate models in exact nonlinear mode (equivalent to perfect-foresight or stacked-time solution methods) and explain how anticipated and unanticipated shocks need to be treated in such simulations. We run a Kalman filter that uses a nonlinear prediction step based on the same simulate technique.

In this tutorial, we create and preparse various model file examples to illustrate the use of the IRIS preparser commands and pseudofunctions. Note that we use the `'saveAs='` option in the `model` function to save the pre-parsed model files for us to be able to look at what the pre-parser commands exactly do.

The tutorial is an introduction into VAR modeling in IRIS. We prepare data, estimate a reduced-form VAR, check its properties, and assess the sampling uncertainty by bootstrapping. We then produce conditional and unconditional forecasts, and show how to identify a structural VAR (SVAR).VARs with Sign Restrictions by Jaromir Benes (Latest Update 20140602)

This tutorials describes the identification of structural VARs based on sign restrictions, or other, more general, types of underdetermined identifying assumptions.Panel VARs by Jaromir Benes (Latest Update 20140602)

This tutorial explains the basics of panel VAR estimation and simulation in IRIS. It only highlights the differences between plain VARs and panel VARs, and methods of comparing the two. For more on using plain VARs, see other tutorials.

Reporting equations are an easy option how to evaluate a collection of equations in a non-simultaneous time-recursive way, i.e. equation by equation, period by period. Reporting equations can be created in three different ways: (i) as an `rpteq` object directly in an m-file or command prompt, (ii) as an `rpteq` object created by reading a separate file describing the equations, or (iii) as `!reporting_equations` in model files. This tutorial shows how to create and run reporting equations in all three ways.Introduction to Reporting in IRIS by Petar Manchev (Latest Update 20140323)

This tutorial is an introduction to the reporting package in IRIS. It explains the report object and its basic elements, such as tables and graphs, and shows how to publish reports to PDF. Furthermore, it illustrates somewhat more sophisticated ways how to format, modify and customize the report elements.

Last edited May 27, 2015 at 9:44 PM by jaromirbenes, version 74