I Tried to Build a DSGE Model and Failed: This Is What I Learnt

What's a DSGE model anyway?

Before I tell you about my failure, let me explain what I was trying to build. A DSGE model - Dynamic Stochastic General Equilibrium model - is the workhorse of modern macroeconomics. It's a mathematical representation of an economy where households decide how much to spend and save, firms decide how much to produce and hire, and a central bank sets interest rates. Everything ties together through prices and interest rates until supply equals demand. "Equilibrium" means all the pieces fit; "dynamic" means it evolves over time; "stochastic" means random shocks hit the economy and we watch how it responds.

Central banks around the world use these models to forecast inflation and guide interest rate decisions. More sophisticated versions of this approach won Nobel Prizes. I figured if I built one for Australia, I'd learn something about how the macroeconomy actually works.

I expected the model to explain Australian inflation, natural rate of unemployment and potential output. Instead, it explained why the model no longer worked.

Economic outlook 2026: Productivity, Inflation, and the End of Easy Policy

Australia enters 2026 with an economy running close to its speed limit - and that speed limit has fallen. The collapse in multifactor productivity (MFP) growth, structural shifts in the composition of the economy, and a more fraught global backdrop all point to an extended period of modest growth, sticky inflation, and a Reserve Bank less willing to provide accommodation than at any time since the global financial crisis.

Regime-Switching Phillips Curves and Productivity Puzzles: A Day of Model Building

One of the enduring puzzles in macroeconomics is the flattening of the Phillips curve - the relationship between unemployment and inflation that once seemed so reliable has become frustratingly weak in recent decades. Today's work on our Bayesian state-space model for the Australian economy tackled this head-on, with some satisfying results.

From Toy Model to Working Framework

Extending the NAIRU and Output Gap Model for Australia

Multi-factor Productivity (MFP): The Solow Residual

The Puzzle

In the late 1990s, something remarkable was happening to Australian productivity. Multi-factor productivity growth – the portion of economic growth not explained by adding more capital or labour – was running at over 2% per year. A decade later, it had collapsed to near zero, and by some measures had turned negative. What happened?

This question matters enormously for policy. Productivity growth is, in Paul Krugman's memorable phrase, "not everything, but in the long run it's almost everything." It determines living standards, fiscal sustainability, and the scope for wage growth without inflation. Yet when we try to measure it, we encounter a fundamental problem: productivity isn't directly observable. We can only infer it as a residual – the growth left over after accounting for measurable inputs.

This residual approach, pioneered by Robert Solow in 1957, has become the workhorse of productivity measurement. It has also been called "the measure of our ignorance." Understanding both its power and its limitations is essential for anyone working with macroeconomic data.

The case for RBA patience is getting stronger

Markets may be leaning dovish, but the data no longer is. Labour supply is slowing, inflation is synchronising upward, and supply-side optimism looks premature.

November Jobs: Market Caution May Be Misplaced

Markets moved quickly to reprice RBA rate expectations after today's November labour force release. But in their rush to declare the economy sicker than we thought, they may be overlooking the bigger picture.

The labour market is cooling, but not enough to ease inflation pressure. In fact, once you adjust for participation and NAIRU dynamics, the labour market remains tighter than it appears - and inflation is re-broadening. Markets may be reacting to the wrong signal.