nep-for New Economics Papers
on Forecasting
Issue of 2024‒02‒26
three papers chosen by
Rob J Hyndman, Monash University

  1. The role of survey-based expectations in real-time forecasting of US inflation By Andriantomanga, Zo
  2. Can Paddy Growing Phase Produce an Accurate Forecast of Paddy Harvested Area in Indonesia? Analysis of the Area Sampling Frame Results By Kadir, Kadir; Prasetyo, Octavia Rizky
  3. High-dimensional forecasting with known knowns and known unknowns By M. Hashem Pesaran; Ron P. Smith

  1. By: Andriantomanga, Zo
    Abstract: This paper performs a real-time forecasting exercise for US inflation from 1992Q1 to 2022Q2. We reinvestigate the literature on autoregressive (AR) inflation gap models - the deviation of inflation from long-run inflation expectations. The findings corroborate that, while simple models remain hard to beat, the multivariate extensions to the AR gap models can improve forecasting performance at short horizons. The results show that (i) forecast combination improves forecast accuracy over simpler models, (ii) aggregating survey measures, using dynamic principal components, improves forecast accuracy, (iii) and the additional information obtained from the error correction process between inflation and long run inflation expectations can improve forecasting performance. In spite of our models providing more accurate one-step ahead forecasts on average, fluctuation tests reveal that over unstable time periods - mainly during the GFC and the Covid-19 pandemic - the AR(1) benchmark performed better.
    Keywords: Inflation, survey forecasts, forecast combination, inflation expectations, error correction, real-time data
    JEL: C53 E31 E37
    Date: 2023–11
  2. By: Kadir, Kadir; Prasetyo, Octavia Rizky
    Abstract: Our study aims to evaluate the accuracy of the forecasts produced based on the paddy growing phase obtained from the results of the Area Sampling Frame (ASF) Survey and, as a comparison, proposes an alternative forecast method taking into account the seasonal pattern and hierarchical structure of the national paddy harvested area estimation obtained from the ASF to improve the accuracy. In doing so, we calculated the MAPE by comparing the realization of paddy harvested area during the period January to September 2022 with their forecasts produced from the area of generative, late vegetative, and early vegetative phases. We also implemented a Hierarchical forecasting method on monthly data of the harvested area from January 2018 to August 2022 for all provinces. Specifically, we applied the bottom-up method for the reconciliation and the rolling window method to produce a three-consecutive month forecast for the period January to September 2022. We found that the accuracy prediction based on the paddy growing phase is moderately accurate. The combination of the bottom-up reconciliation method and the SARIMA model produces a much better accuracy for the national figure of paddy harvested area as shown by a lower MAPE. Our findings suggest that the Hierarchical forecasting method could be an alternative for the prediction of harvested area based on the ASF results other than the prediction obtained from the standing crops.
    Keywords: ASF, Hierarchical, forecasting, paddy, SARIMA
    JEL: C1 C18 C40 Q1 Q10
    Date: 2023–08–12
  3. By: M. Hashem Pesaran; Ron P. Smith
    Abstract: Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1-2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables.
    Date: 2024–01

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