英国国家经济和社会研究所-英国月度GDP预测：基于大数据方法和预测组合算法的自下而上部门建模证据（英）

Nowcasting MonthlyUK GDP: Evidencefrom Bottom-UpSectoral Modelingwith Big DataMethods andForecastCombinationAlgorithmsNIESR Policy Paper 46June 2025Paula Bejarano CarboRory MacqueenEfthymios Xylangouraswww.niesr.ac.uk About the National Institute of Economic and Social Research The National Institute of Economic and Social Research is Britain's longest established independent research institute, founded in 1938. The vision of our founders was to carry out research to improve understanding of the economic and social forces that affect people’s lives, and the ways in which policy can bring about change. Over eighty years later, this remains central to NIESR’s ethos. We continue to apply our expertise in both quantitative and qualitative methods and our understanding of economic and social issues to current debates and to influence policy. The Institute is independent of all party-political interests. National Institute of Economic and Social Research 2 Dean Trench St London SW1P 3HE T: +44 (0)20 7222 7665 E: enquiries@niesr.ac.uk niesr.ac.uk Registered charity no. 306083 Policy papers are written by members of the National Institute of Economic and Social Research to specifically address a public policy issue. These may be evidence submitted to a public or parliamentary enquiry, or policy research commissioned by a third-party organisation. In all circumstances NIESR has full editorial control of these papers. We will make all policy papers available to the public whether they have been supported by specific funding as a matter of course. Some papers may be subsequently developed into research papers This paper was first published in June 2025 © National Institute of Economic and Social Research 2025 Nowcasting Monthly UK GDP: Evidence from Bottom-Up Sectoral Modeling with Big Data Methods and Forecast Combination Algorithms Paula Bejarano Carbo1 , Rory Macqueen2 , and Efthymios Xylangouras3 Abstract We construct a bottom-up sectoral nowcasting model, in the spirit of NIESR’s GDP tracker, to predict monthly GDP growth one month in advance of the first official estimate. We use a large dataset of public and private sector variables, including newly-available real time indicators, as inputs to 28 nowcasting models, each of which is applied to all 20 SIC industries. These range from simple univariate processes to high-dimensional ‘big data’ approaches. We then apply algorithms trained on previous forecast errors to combine the nowcasts for each sector, generating pseudo out-of-sample GDP nowcasts for 57 months covering before, during and after the Covid-19 shock. We find evidence that (a) our combination algorithms are better than single-model approaches during the initial pandemic shock and generally no worse in ‘normal’ times, (b) our algorithmic sectoral nowcasts are more accurate in terms of RMSFE than ONS first estimates for some industries, but (c) for nowcasting total GDP, human judgement seems impossible to beat, (d) a bottom-