Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model

• URL: http://arxiv.org/abs/2209.05998v1
• Date: Sun, 31 Jul 2022 06:24:15 GMT
• Title: Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model
• Authors: Yukang Jiang, Xueqin Wang, Zhixi Xiong, Haisheng Yang, Ting Tian
• Abstract summary: The paper proposes a time-varying parameter global vector autoregressive framework for predicting and analysing developed region economic variables. We show the convincing in-sample of our proposed model in all economic variables and relatively high precision out-of-sample predictions with different-frequency economic inputs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The paper proposes a time-varying parameter global vector autoregressive (TVP-GVAR) framework for predicting and analysing developed region economic variables. We want to provide an easily accessible approach for the economy application settings, where a variety of machine learning models can be incorporated for out-of-sample prediction. The LASSO-type technique for numerically efficient model selection of mean squared errors (MSEs) is selected. We show the convincing in-sample performance of our proposed model in all economic variables and relatively high precision out-of-sample predictions with different-frequency economic inputs. Furthermore, the time-varying orthogonal impulse responses provide novel insights into the connectedness of economic variables at critical time points across developed regions. We also derive the corresponding asymptotic bands (the confidence intervals) for orthogonal impulse responses function under standard assumptions.

Related papers

• Learning Augmentation Policies from A Model Zoo for Time Series Forecasting [58.66211334969299]
  We introduce AutoTSAug, a learnable data augmentation method based on reinforcement learning. By augmenting the marginal samples with a learnable policy, AutoTSAug substantially improves forecasting performance.
  arXiv  Detail & Related papers  (2024-09-10T07:34:19Z)
• Editable Fairness: Fine-Grained Bias Mitigation in Language Models [52.66450426729818]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases. FAST surpasses state-of-the-art baselines with superior debiasing performance. This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• Optimizing Sales Forecasts through Automated Integration of Market Indicators [0.0]
  This work investigates the potential of data-driven techniques to automatically select and integrate market indicators for improving customer demand predictions. By adopting an exploratory methodology, we integrate macroeconomic time series, such as national GDP growth, into textitNeural Prophet and textitSARIMAX forecasting models. It could be shown that forecasts can be significantly enhanced by incorporating external information.
  arXiv  Detail & Related papers  (2024-05-15T08:11:41Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Economic Recession Prediction Using Deep Neural Network [26.504845007567972]
  We identify the deep learning methodology of Bi-LSTM with Autoencoder as the most accurate model to forecast the beginning and end of economic recessions in the U.S. We adopt commonly-available macro and market-condition features to compare the ability of different machine learning models to generate good predictions both in-sample and out-of-sample.
  arXiv  Detail & Related papers  (2021-07-21T22:55:14Z)
• Aggregate Learning for Mixed Frequency Data [0.0]
  We propose a mixed-temporal aggregate learning model that predicts economic indicators for smaller areas in real-time. We find that the proposed model predicts (i) the regional heterogeneity of the labor market condition and (ii) the rapidly changing economic status. The model can be applied to various tasks, especially economic analysis.
  arXiv  Detail & Related papers  (2021-05-20T08:12:43Z)
• Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach [29.11201102550876]
  We build a semi-parametric structural model to learn individual price elasticity and predict counterfactual demand. We propose a multi-period dynamic pricing algorithm to maximize the overall profit of a perishable product over its finite selling horizon. The proposed framework has been successfully deployed to the well-known e-commerce fresh retail scenario - Freshippo.
  arXiv  Detail & Related papers  (2021-05-18T07:01:37Z)
• Deep Learning for Individual Heterogeneity: An Automatic Inference Framework [2.6813717321945107]
  We develop methodology for estimation and inference using machine learning to enrich economic models. We show how to design the network architecture to match the structure of the economic model. We obtain inference based on a novel influence function calculation.
  arXiv  Detail & Related papers  (2020-10-28T01:41:47Z)
• Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
  We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
  arXiv  Detail & Related papers  (2020-10-27T17:54:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.