Going non-linear

by Jaromir Benes

A simple non-linear solver, equivalent to perfect-foresight solution, has been in IRIS for quite some time by now. Recently (17 February 2012), I released a new version of IRIS with two improvements in it. Let me explain how things work.

Migration from 7 to 8

by Antti Ripatti

My inner engineer wanted to get employed. I employed it to make the migration of the Bank of Finlands’s Aino model from Iris 7 to 8. The code now works without error messages, but is not tested yet (engineers’ bad habit). For those who have not made migration yet might benefit the following issues that the engineer met.

Know your neighbour

by Jaromir Benes

The neighbourhood function is a simple tool for examining and visualising the behaviour of an objective function (such as data likelihood or posterior density) in the neighbourhood of parameter estimates.

K-step-ahead Kalman predictions

by Jaromir Benes

This post is about the 'ahead' option available in the IRIS Kalman filter, and the associated plotpred function.

LaTeX not found?

by Jaromir Benes

Got LaTeX installed on your computer, and still IRIS cannot find it? No problem. You can always fix it manually.

SSTATE function and SSTATE objects

by Jaromir Benes

Calculating the steady state of a non-linear model – or its balanced-growth path, for that matter – is sometimes a bit of challenge in which you can (and often really need to) play all sorts of tricks. In IRIS, there are two basic ways how to handle and tame steady states.

Forecasts how we like them

by Jaromir Benes

Practitioners know that a good forecast is about a good story in the first place and – contrary to popular belief – not so much about very accurate numbers. And also that good forecasts can only be produced by putting together a large amount of information that has often little to do with a particular model itself while still using that model, and not by pushing a button on a black box. In other words, we usually need to add lots of various types of judgmental adjustments (or tunes, or whatever we call them) to our models and forecasts. In this post, I show a simple taxonomy of judgmental adjustments (JAs) that are available in IRIS.