nep-for New Economics Papers
on Forecasting
Issue of 2024‒05‒13
two papers chosen by
Rob J Hyndman, Monash University

  1. Bayesian Bi-level Sparse Group Regressions for Macroeconomic Forecasting By Matteo Mogliani; Anna Simoni
  2. Neural Network Modeling for Forecasting Tourism Demand in Stopi\'{c}a Cave: A Serbian Cave Tourism Study By Buda Baji\'c; Sr{\dj}an Mili\'cevi\'c; Aleksandar Anti\'c; Slobodan Markovi\'c; Nemanja Tomi\'c

  1. By: Matteo Mogliani; Anna Simoni
    Abstract: We propose a Machine Learning approach for optimal macroeconomic forecasting in a high-dimensional setting with covariates presenting a known group structure. Our model encompasses forecasting settings with many series, mixed frequencies, and unknown nonlinearities. We introduce in time-series econometrics the concept of bi-level sparsity, i.e. sparsity holds at both the group level and within groups, and we assume the true model satisfies this assumption. We propose a prior that induces bi-level sparsity, and the corresponding posterior distribution is demonstrated to contract at the minimax-optimal rate, recover the model parameters, and have a support that includes the support of the model asymptotically. Our theory allows for correlation between groups, while predictors in the same group can be characterized by strong covariation as well as common characteristics and patterns. Finite sample performance is illustrated through comprehensive Monte Carlo experiments and a real-data nowcasting exercise of the US GDP growth rate.
    Date: 2024–04
  2. By: Buda Baji\'c; Sr{\dj}an Mili\'cevi\'c; Aleksandar Anti\'c; Slobodan Markovi\'c; Nemanja Tomi\'c
    Abstract: For modeling the number of visits in Stopi\'{c}a cave (Serbia) we consider the classical Auto-regressive Integrated Moving Average (ARIMA) model, Machine Learning (ML) method Support Vector Regression (SVR), and hybrid NeuralPropeth method which combines classical and ML concepts. The most accurate predictions were obtained with NeuralPropeth which includes the seasonal component and growing trend of time-series. In addition, non-linearity is modeled by shallow Neural Network (NN), and Google Trend is incorporated as an exogenous variable. Modeling tourist demand represents great importance for management structures and decision-makers due to its applicability in establishing sustainable tourism utilization strategies in environmentally vulnerable destinations such as caves. The data provided insights into the tourist demand in Stopi\'{c}a cave and preliminary data for addressing the issues of carrying capacity within the most visited cave in Serbia.
    Date: 2024–04

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