the outcomes in practice could, and probably will, turn out quite differently to our projections at any particular time. but the important thing is that we ’ ve done the best we can to anticipate the different possibilities and how we might have to react to them. in our quarterly forecast cycle we do our best to update, test, revise and communicate our views thoroughly. this enables us to keep the ocr as well - positioned as we can, and our policy strategy wellunderstood, so that our efforts to maintain stable inflation do not cause more economic volatility than necessary. references bergstrom, k and o karagedikli ( 2013 ) “ the interest rate conditioning assumption : investigating the effects of central bank communication in new zealand ”, rbnz discussion paper, forthcoming. blinder, a, m ehrmann, m fratzscher, j de haan & d jansen ( 2008 ) “ central bank communication and monetary policy : a survey of theory and evidence ”, journal of economic literature 46 ( 4 ), 910 – 945. hampton, t, d stephens and r philip ( 2003 ) “ monetary policy communication and uncertainty ”, rbnz bulletin 66 ( 2 ), 29 – 34. labbe, f and h pepper ( 2009 ) “ assessing recent external forecasts ”, rbnz bulletin 72 ( 4 ), 19 – 25. roger ( 2009 ) provides a review of the two decades of inflation targeting experience around the world, that began with new zealand ’ s introduction of inflation targeting with the reserve bank act of 1989 and subsequently spread. bis central bankers ’ speeches mccaw, s and s ranchhod ( 2002 ) “ the reserve bank ’ s forecasting performance ”, rbnz bulletin 65 ( 4 ), 5 – 23. roger, s ( 2009 ) “ inflation targeting at 20 : achievements and challenges ”, imf working paper wp / 09 / 236. svensson, l ( 2006 ) “ social value of public information : morris and shin ( 2002 ) is actually pro transparency, not con ”, american economic review 96 ( 1 ), 448 – 451. woodford, m ( 2005 ) “ central bank communication and policy effectiveness ”, nber working paper 11898. bis central bankers ’ speeches

rates, reflecting the price stability objective of monetary policy, tend to rise in response to increases in future inflation pressure. all of these key macroeconomic variables are therefore linked together by economic behavioural tendencies. we say “ tendencies ” because other things than those mentioned matter too, and may at times be substantial drivers of the patterns we see in the data. for example, a major source of exchange rate fluctuations in new zealand is the outlook for the world prices of our commodity exports and the terms of trade. housing investment moves quite substantially with the ebbs and flows of net migration. government spending and other fiscal policy changes are a further source of demand shock. bis central bankers ’ speeches the role of models and the need for judgement the overall variation in the data can therefore be viewed as a combination of the sequence of shocks and the responses of the economy to those shocks. one major task of the economic analyst is to disentangle the two, at least conceptually. one tool we use to help with the task is a structural forecasting model, which continues our long tradition of using such models in forecasting and policy analysis. the model is calibrated to the “ typical ” economic behaviour observed over the past two decades of price stability in new zealand, and embeds the fairly standard views of economic behaviour outlined above. its key features include intuitive economic mechanisms, a relatively simple structure, and adaptability to accommodate a range of economic circumstances. using a model provides a number of benefits. it enables us to link policy decisions to our objectives and to the economic outlook, to consider simultaneous shocks and interactions among economic variables systematically and consistently, and to ensure that there is some consistency in interpreting the flow of data between forecast updates and policy decisions. but even the most advanced models can only be very abstract representations of the economy, and our forecasting model is deliberately designed to be relatively simple and adaptable. too much complexity would make it difficult to understand the transmission mechanisms and dynamics in the model, and would be unlikely to improve forecasting accuracy materially. judgemental input to any model and adjustment to the model ’ s outputs are therefore a necessary part of building up the forecast – and indeed the process of adding judgement itself helps us form views about the outlook in a systematic manner. there are three main motivations for adding judgement currently. firstly, responses to rare events such as the global financial crisis and the canterbury earthquakes will, by design, not be picked up in the forecasts of a model that is cal