The use of Macro-Economic Modelling at RBNZ Grant Spencer Forecasting Bootcamp - International training 23-27 Sept 2019, Wellington, New Zealand

The Use of Economic Modelling at RBNZ Purpose of modelling RBNZ modelling history Common characteristics of RB macro models RBNZ’s forecasting - policy process Pros and Cons of model-based policy process Current issues with RB’s modelling framework Future direction of modelling?

Be clear on purpose of modelling Alternative purposes of economic/financial modelling Forecasting tool Story telling – eg to describe the drivers of growth and inflation Policy decision making (eg OCR decisions) Economic analysis – eg via simulation experiments Financial sector stress testing RBNZ has used macro-models mainly for the first 3 purposes

RBNZ - macro-modelling since 1970’s International surge in macro-econometric modelling in 1970s facilitated by development of computers and quarterly national accounts. Led by the US (Lawrence Klein, Wharton model, FRB-MIT model) Early RBNZ efforts required construction of quarterly national accounts data for NZ RB models became large and relatively ad-hoc before reducing to smaller structures that were more theoretically coherent RBNZ built a DSGE model in 2005-06 but it was not good at forecasting and not very useful for policy analysis/ story-telling GFC not well handled by RB(and other CB) macro-models due to weak representation of financial sector linkages

Common characteristics of RBNZ models In the Keynesian demand-side tradition: Y = C + I + G +X – M Simple supply-side representation with 2-3 “products:, eg one non-traded good and two traded (export and import) goods Model explains short-term (2-3 year horizon) dynamics around simple long term trends Endogenous OCR since 1999; Exch rate partially endogenous Key exogenous variables: World GDP, export volumes, import and export prices (in USD), net migration, Govt spending, tax rates Two key monetary policy transmission channels: housing and the exchange rate

RBNZ’s forecasting – policy process Quarterly forecasting/MPS/OCR decision cycle with 6 weekly interim OCR decisions based on forecast “updates” Economics Dept presents “first pass” forecast to MPC plus internal advisory group. Forecasts unpicked over several days. Focus on forecasts, not policy. Outcome is “second pass” (ie final) forecast, owned by MPC MPC plus smaller internal advisory group then meets to discuss policy decision, communications, potential financial market impact MPC makes OCR decision and agrees text of press release and MPC minutes Governor presents decision at press conference and fronts parliamentary committee Unpicking of forecasts includes testing assumptions re projections of TOT, world growth, immigration, export volumes, savings rate, house prices, participation rate, exchange rate. Also persistence/decay of recent forecast errors,

Pros and cons of model-based policy process Provides a consistent and coherent framework for reconciling the large amount of information relevant to Monetary policy decisions Facilitates story telling about how recent information is likely to affect future growth and inflation outcomes Simple model structure allows MPC to easily assess the impact of alternative assumptions re exogenous variables and model parameters Cons New MPC members may find the process complex and impenetrable – risk that MPC members may be captured by the model “auto-pilot” RB’s self-imposed discipline of publishing exch rate and int rate tracks can be restrictive Risk of ignoring factors that are not part of the model, eg supply-side, confidence and “balance-sheet” factors Publication of the OCR track is a double-edged sword: It adds transparency and credibility to the policy story, but can also complicate the Bank’s policy communications

Current issues with RB modelling framework Supply side modelling is weak at a time when the economy is being impacted by significant supply shocks. (Positive supply shocks from globalization and digitisation). Output gap (Q-Q*) is not well identified Absence of a Labour market in the model – this is changing with the new dual mandate but the output gap (not unemployment) remains the key transmission channel Significant movement and uncertainty around key equilibrium values: U* (NAIRU) and r* (equilibrium real interest rate) Simplicity of financial sector transmission channels. No credit effects, no distinction between substitution and cash flow effects of int rate changes Uncertainty and risk are not well handled

Future direction of modelling? IT advances are facilitating more disaggregated models of specific markets (eg Labour mkt), larger DSGE models and agent-based computational models. However, policy makers are likely to continue using simple macro-models for forecasting and story telling Expect increasing use of “big data” and AI in financial regulation (Regtech), including financial sector modelling for stress testing (eg Federal Reserve collects micro-financial data) Potential to integrate macro and financial system models to support analysis of macro-financial stability