Horses for Courses: A discussion of the challenge of modelling macroeconomic dynamics

1. Horses for Courses: A discussion of the challenge of modelling macroeconomic dynamics Alistair Milne Loughborough University ESRC conference on Diversity in Macroeconomics University of Essex, 24th Feb 2014

2. De Grauwe“Behavioural Macroeconomics” • ‘a model in which agents have cognitive limitation and do not understand the whole picture (the underlying model)’ • Endogenous switching towards best performer of: • Yields rich dynamics (booms/ busts) • “animal spirits” as agents switch to extrapolation • in an otherwise standard NK model

3. Schenk-Hoppé“Leverage in financial markets” • Funds follow evolutionary portfolio strategies • Information set is order book, observed earnings • not underlying state • Initial “strategies” randomly generated • A list of at most 128 machines instructions on this information. • population of strategies then evolves via “tournaments” • Cross-over • Mutation • Reproduction (selection) • Generates market volatility, asset price booms • Explore implications of short selling, leverage restrictions, transactions taxes

4. Doyne FarmerAgent based models for systemic risk • Doyne has written perceptively on both agent based modelling in finance/ economics and ‘econophysics’ (power laws etc.) • A paper of his well worth reading • Geanakoplous (et al) “Getting at Systemic Risk via an Agent Based model of the [Washington DC] housing market” Cowles Foundation DP SSRN (2012) (builds on Wall St. Prepayment modelling) • Key issues to me • Matching to microeconomic data e.g. • Quantification of “behavioural” rules emerge from the data • Allows much more sophisticated “what if” (in this case behavioural leverage increase beats interest rates as explanation of boom) • This modelling is truly microfounded • In way that New Keynsian models NEVER are

5. My views ... having spent many years as part of the policy making machine • There can be no canonical model of the macroeconomy • To think this is our task is to suffer from “physics envy” • Models are there to help us understand and explore specific issues (horses for courses). Examples • Inflation expectations (NK pretty good for this) • Housing dynamics (Doyne’s work is state of art) • Credit constraints and financial distress (my life long obsession) • Political economy of booms and busts • More thoughts on models • They need not always be quantitative, but sometimes they have to be • They must be simple enough to persuade and illuminate • please no more wasting our time on incorporating bells and whistles into NK models • calibration to aggregate data tells us next to nothing about whether a model will help us • use micro (agent level) data wherever possible • avoid the Basel II fallacy (safety in numbers) • Read study and link models to history, the best laboratory for our work of all. • Where I depart from Doyne, I don’t think models have (always) to be expensive

6. And a bit of shameless selling ... • Conference on data standards, information and financial stability • Loughborough April 11th -12th • Supported by Sloan Foundation • http://www.lboro.ac.uk/departments/sbe/research/conferences/cdsifs/ • We need to crack the data problem • if we are going to make any progress at all with macrofinancial modelling