WITHDRAWN: Forecasting UK inflation bottom up

Joseph, Andreas; Kalamara, Eleni; Kapetanios, George; Potjagailo, Galina

doi:10.1016/j.ijforecast.2021.03.005

Cited by 2 publications

(1 citation statement)

References 46 publications

(40 reference statements)

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“…In addition to these results, we also report on the Shapley values for a selected statistical learning model that appears to provide attractive out-of-sample results, to identify the important drivers of future inflationary pressure. This work follows Lundberg and Lee (2017) and Joseph et al (2020), and the results are contained in section H of the online appendix.…”

Section: Resultsmentioning

confidence: 99%

Big data forecasting of South African inflation

Botha¹,

Burger

Kotzé

et al. 2022

Empir Econ

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We investigate whether the use of statistical learning techniques and big data can enhance the accuracy of inflation forecasts. We make use of a large dataset for the disaggregated prices of consumption goods and services, which we partially reconstruct, and a large suite of different statistical learning and traditional time-series models. The results suggest that the statistical learning models are able to compete with most benchmarks over medium to longer horizons, despite the fact that we only have a relatively small sample of available data. This may imply that the ability of statistical learning models to explain nonlinear relationships, or as an alternative, restrict the set of predictors to relevant information, is of importance. These characteristics of the statistical learning models may be particularly useful during periods of crisis, when deviations from the steady state are more persistent. We find that the accuracy of the central bank's near-term inflation forecasts compares favourably with those of The authors would like to thank Patrick Kelly and Marietjie Bennett from Statistics South Africa for their assistance with the data. The insightful comments of the anonymous referees have also lead to a number of improvements, for which the authors are grateful. The remaining errors are those of the authors.

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Section: Resultsmentioning

confidence: 99%

Big data forecasting of South African inflation

Botha¹,

Burger

Kotzé

et al. 2022

Empir Econ

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Factor extraction using Kalman filter and smoothing: This is not just another survey

Poncela

Ruiz

Miranda

2021

International Journal of Forecasting

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Dynamic Factor Models, which assume the existence of a small number of unobserved latent factors that capture the comovements in a system of variables, are the main big data tool used by empirical macroeconomists during the last 30 years. One important tool to extract the factors is based on Kalman lter and smoothing procedures that can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity and many other characteristics often observed in real systems of economic variables. This paper surveys the literature on latent common factors extracted using Kalman lter and smoothing procedures in the context of Dynamic Factor Models. Signal extraction and parameter estimation issues are separately analyzed. Identication issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases.

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WITHDRAWN: Forecasting UK inflation bottom up

Cited by 2 publications

References 46 publications

Big data forecasting of South African inflation

Big data forecasting of South African inflation

Factor extraction using Kalman filter and smoothing: This is not just another survey

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