RDP 2019-07: MARTIN Has Its Place: A Macroeconometric Model of the Australian Economy 2. What is MARTIN and What is it Used for?

MARTIN is a macroeconometric model of the Australian economy. It describes relationships between key macroeconomic variables and is used to generate economic forecasts and conduct counterfactual scenario analysis. When developing the model, we had to make choices about what variables the model would capture, as well as its structure and estimation framework. A key objective in making these choices was to be consistent with the RBA's existing analytical and forecasting approach. This meant that we could easily integrate MARTIN into the RBA's processes and ensured that the model would be well understood by staff and policymakers. As such, while the model is large, its design is relatively simple and was developed in close consultation with the forecasting staff who are the main consumers of the model's output.

2.1 Model Variables

MARTIN aims to capture the mechanisms that matter most for a central bank; namely the features of the Australian economy that affect the setting and transmission of monetary policy. As such, the model centres on the interactions between the expenditure components of GDP (such as consumption and investment), prices and the labour market. Several components of the household balance sheet are modelled, as well as some financial market variables (such as the exchange rate and equity prices). MARTIN also includes exogenous variables that summarise overseas economic conditions.

Although the model is large and its equations capture numerous economic interactions, it does not cover every aspect of the economy. Like all models, MARTIN provides a simplified representation of reality. In order to remain tractable, it omits some variables that, in certain circumstances, could have a material influence on economic outcomes. Some notable omissions include a standalone banking sector, a detailed treatment of firms' income and balance sheets, industry detail and measures of economic agents' confidence or sentiment. Some features that are present in a limited way include the foreign and government sectors, productivity growth and other aspects of the supply side of the economy, expectation formation and stock-flow dynamics. Over time, we expect that further development of these aspects of the model will occur. Some of these extensions will be incorporated into the core of MARTIN, while others are likely to exist as satellite models that can be used in conjunction with MARTIN to address specific questions.

2.2 Model Framework

MARTIN is an economy-wide model. These types of models examine many economic variables at the same time, with only a few variables taken as given. That is, most variables in MARTIN are endogenous. MARTIN determines the value of its endogenous variables jointly as a system. Examining variables in a system is desirable for a number of reasons. As outlined in Atkin and La Cava (2017), monetary policy influences economic activity through multiple channels, such as through its effect on the housing market and the exchange rate. Examining variables in a system allows us to isolate the roles that individual channels play in determining aggregate economic outcomes, and aids our understanding of how monetary policy works. Economy-wide models also account for feedback between economic variables. For example, an increase in aggregate demand will encourage firms to hire more workers, which raises employment and lowers the unemployment rate. The tightening of the labour market is likely to lead to an increase in wages growth. The resulting increase in household incomes is likely to lead to an increase in consumption, further raising aggregate demand. Accounting for feedback mechanisms is particularly important for understanding medium- and longer-term developments, because over these horizons interrelationships between economic variables become more important and there are fewer leading indicators available.

A key feature of MARTIN is that many of its equations are based on the many single-equation models that the RBA already maintains.^[1] However, differences between the equations in MARTIN and the single-equation models exist. For example, we have restricted the value of some coefficients in several of MARTIN's equations to ensure that the model has stable long-run properties. MARTIN's equations are also relatively parsimonious to ensure less volatile short-run model dynamics.

The RBA also maintains another economy-wide model: the multi-sector dynamic stochastic general equilibrium (DSGE) model, MSM, documented in Rees, Smith and Hall (2016) and extended by Gibbs, Hambur and Nodari (2018). This model is built on a consistent theoretical framework of optimising households and firms. The framework provides a clear interpretation of the causal mechanisms within the model, maintains internal consistency and has an explicit role for forward-looking expectations. This makes it useful for scenario analysis. However, in common with other DSGE models, MSM does not forecast as well as some other models, and its causal mechanisms do not always correspond to how policymakers think the economy works.^[2] MSM also lacks some of the detail – particularly around the labour market and household balance sheets – that economists at the RBA use to analyse the economy, and it is difficult to modify to incorporate new variables or changing economic dynamics.

MARTIN lies between the two extremes of a fully data-driven model and one guided solely by theory.^[3] Most of the model's estimated equations are error correction models (ECMs).^[4] This methodology explicitly distinguishes between short-term dynamics and long-term equilibrium relationships. In MARTIN, economic theory influences the choice of what variables to include in each equation and, in some cases, how those variables relate to each other. This is particularly true for the long-run relationships in the model. But MARTIN is also designed to capture observed relationships in the data, particularly in its short-run dynamics. As such, most equations are estimated, although some relationships are calibrated to better fit our beliefs or external estimates. The benefit of this empirical approach is that MARTIN's equations are flexible enough to incorporate the economic mechanisms that policymakers at the RBA believe are important and yet still fit the observable relationships in the data reasonably well. The flexibility allows us to introduce features that are specific to the Australian economy, which can be difficult to incorporate in a DSGE model. This flexibility reflects the fact that the model is not derived from a single theoretical framework. The downside is that the causal mechanisms in MARTIN are less clear than in, say, a DSGE model.

So, although MARTIN might capture empirical relationships that exist in the data, the drivers of these relationships might in some cases be hard to interpret.

The development of MARTIN has coincided with an active debate about the merits of alternative approaches to building models for economic policy analysis. Blanchard (2018) and Wren-Lewis (2018) describe the benefits of macroeconometric models like MARTIN, with their flexibility and ability to match the data being key. Lindé (2018) offers a more sceptical appraisal. Although DSGE models remain widely used, several other central banks maintain macroeconometric models that are similar to MARTIN. Leading examples include the Federal Reserve's FRB/US model, the Bank of Canada's LENS model and the Bank of Japan's Q-JEM model (Brayton, Laubach and Reifschneider 2014; Gervais and Gosselin 2014; Fukunaga et al 2011). MARTIN is also similar in style to some previous models of the Australian economy, including AUS-M (Downes, Hanslow and Tulip 2014) and its predecessor, TRYM, which was developed by the Australian Treasury (Taplin et al 1993).^[5]

2.3 Model Uses

RBA staff use MARTIN for four main tasks:

• enhancing the staff's understanding of recent economic developments and how their forecasts fit together;
• extending staff forecasts beyond the usual two-to-three year forecast horizon to generate medium- and long-run projections;
• generating model forecasts, for comparison to staff forecasts;
• producing scenarios to quantify uncertainties around the central forecasts.

We can use MARTIN to replicate the RBA staff forecasts (that is, the forecasts produced using single-equation models, real-time data and a range of off-model information, such as insights from liaison with businesses and other organisations) by appending ‘add factors’ to the equations. These ‘add factors’ are additional reduced-form errors that account for the differences between the staff and MARTIN forecasts. They reflect aspects of the forecasts that MARTIN cannot explain, and can be loosely considered as a proxy for the ‘judgement’ that the staff apply in producing their forecasts. While the existence of ‘judgement’ does not make the staff forecasts wrong, MARTIN's decompositions help the staff to gauge whether the judgements being made are appropriate, and whether they have been applied consistently across the forecasts. An example of where judgement may appear is if the staff forecasts take account of the effects of a pre-announced change in income tax rates that the model does not foresee. Another example of judgement could be if the staff forecasts take a different view than MARTIN about the speed at which a change in wages growth would feed into inflation. In the former case it is easy to explain how and why judgement has been applied, whereas the latter example could be an impetus for further work to better understand wage-price dynamics.

After matching the RBA staff forecasts, we can use MARTIN to decompose and understand the economic outlook based on the relationships captured in the model. For example, we can determine the proximate factors influencing inflation, such as input costs, and then decompose these into their ultimate drivers, like changes in world commodity prices or consumer behaviour. MARTIN can also produce probability intervals around the forecasts, based on the uncertainty implied by the historical model residuals.

In addition to helping us analyse the staff forecasts, MARTIN can also produce its own model-based forecasts. These provide a crosscheck on the staff forecasts and allow us to extend these forecasts over a longer time period, by allowing them to converge to the long-run paths produced by MARTIN. These long-run paths are informed by economic theory, making them well-suited for forecast extensions. Producing extended forecasts can be particularly useful if the economy is experiencing large economic shocks as standard forecast horizons may be too short to fully illustrate how variables like inflation or the unemployment rate return to their long-run targets. Indeed, in these cases having an extended forecast horizon can be important to allow policymakers to fully assess the costs and benefits of alternative policy recommendations.^[6]

Finally, we can use MARTIN to construct scenarios that explore and quantify risks to the staff forecasts. This involves imposing alternative paths for one or more of the variables, then comparing the model outcomes to a baseline forecast. The differences between the alternative scenarios and the baseline illustrate the economy-wide effects of potential economic developments.^[7] Similarly, we can use MARTIN to answer policy-relevant questions by comparing past outcomes to what may have happened under a counterfactual path for a particular variable.^[8]

MARTIN is well suited for both forecasting and policy analysis. This is because it features: flexibility in the equations and variables included; coverage of many aspects of economic activity relevant for monetary policy; and a similar framework to the RBA staff's existing forecasting models. However, tensions can arise between what is useful in a model for forecasting relative to what is useful in scenario analysis, as well as between producing short- versus long-run forecasts. For instance, equations with many lagged dependent variables often provide a good fit for the data and forecast well at short horizons, but the presence of lagged variables can produce undesirable oscillating behaviour in long-run simulations or forecasts.

Footnotes

See Cassidy et al (2019) for an example of the use of single-equation models for understanding inflation. [1]

For example, in MSM, business investment is highly sensitive to interest rates because firms seek to equate the marginal productivity of an incremental unit of capital to its user cost. In practice, most studies find that interest rates have a relatively small direct effect on aggregate business investment, which seems to be more responsive to other economic influences (Cockerell and Pennings 2007; Lane and Rosewall 2015), although Hambur and La Cava (2018) argue that these papers may underestimate the effects of interest rates because they do not account for firm heterogeneity. [2]

See Box A in Cusbert and Kendall (2018) for a taxonomy of economic models along the spectrums of theory- and empirically-driven models, as well as full-system versus single-equation models. [3]

See Enders (2004) for an exposition of cointegration and the error correction modelling framework. [4]

Pagan (2019) describes MARTIN's relationship to other models used in Australian policy institutions, including the RBA. [5]

Because it is a reduced form model, the use of MARTIN to examine alternative policy choices is potentially subject to the Lucas critique (Lucas 1976). In practice, the policy options we consider typically involve relatively small departures from standard policy settings and so can treated as ‘modest policy interventions’ (Leeper and Zha 2003) that do not alter agents' beliefs about the policy regime or cause material changes in the structure of the economy. [6]

For an example of this type of scenario, see Guttmann et al (2019). [7]

For an example of this type of scenario, see May, Nodari and Rees (2019). [8]