In the very short term, we generate a forecast for real GDP growth from which we infer whether the output gap will narrow or widen. We use high-frequency data and survey indicators to produce that forecast. It usually covers the quarter that is currently in progress (and will therefore not be covered by outturn data) and the next quarter ahead. In our March 2019
forecast , it covered the first and second quarter of 2019. These high-frequency indicators allow us to make an assessment of how much ‘momentum’ there is in the economy and, therefore, whether we expect quarterly GDP growth to pick up, slow or stabilise.
The Office for National Statistics now produces monthly estimates of GDP based on output components (e.g. construction or business services). These provide the most reliable early indicators of quarterly GDP growth and these data are used as the basis for the preliminary estimates of quarterly GDP.
To inform our judgements about the likely near-term path of GDP, we also use models that incorporate other timely indicators. These include business surveys such as the IHS Markit/CIPS Purchasing Managers’ Index (PMIs) and surveys from the Confederation of British Industry (CBI). If there are specific events that we believe are likely to have affected GDP in a given quarter, we will make any adjustments that we deem necessary. This could be due to unusual weather conditions or specific events – such as the 2012 Olympic Games or the additional bank holiday for the Queen’s Diamond Jubilee.
Our assessment of momentum in the current quarter informs our judgement about GDP growth in the following quarter. This is supplemented by survey data on business expectations which, in general, are less reliable for forecasting than the high-frequency, backward-looking indicators but are, nonetheless, useful.
OBR staff run the various models described above and present the results to the BRC. It is ultimately the BRC’s judgement on the most likely path for near-term GDP that is published as our forecast. The BRC decide which data or models they judge to be providing the most reliable indicators at any time, or the extent to which model predictions should be adjusted to reflect one-off factors.