Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder

• URL: http://arxiv.org/abs/2503.04386v1
• Date: Thu, 06 Mar 2025 12:37:55 GMT
• Title: Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder
• Authors: Yiyong Luo, Brooks Paige, Jim Griffin,
• Abstract summary: We introduce a Grouped Sparse autoencoder that employs the Spike-and-Slab Lasso prior.<n>We incorporate time-varying parameters into the VAR component to better capture evolving economic dynamics.<n>Our empirical application to the US economy demonstrates that the Grouped Sparse autoencoder produces more interpretable factors.
• Score: 4.769637827387851
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent economic events, including the global financial crisis and COVID-19 pandemic, have exposed limitations in linear Factor Augmented Vector Autoregressive (FAVAR) models for forecasting and structural analysis. Nonlinear dimension techniques, particularly autoencoders, have emerged as promising alternatives in a FAVAR framework, but challenges remain in identifiability, interpretability, and integration with traditional nonlinear time series methods. We address these challenges through two contributions. First, we introduce a Grouped Sparse autoencoder that employs the Spike-and-Slab Lasso prior, with parameters under this prior being shared across variables of the same economic category, thereby achieving semi-identifiability and enhancing model interpretability. Second, we incorporate time-varying parameters into the VAR component to better capture evolving economic dynamics. Our empirical application to the US economy demonstrates that the Grouped Sparse autoencoder produces more interpretable factors through its parsimonious structure; and its combination with time-varying parameter VAR shows superior performance in both point and density forecasting. Impulse response analysis reveals that monetary policy shocks during recessions generate more moderate responses with higher uncertainty compared to expansionary periods.

Related papers

• Autoencoder Enhanced Realised GARCH on Volatility Forecasting [2.1902930328664914]
  This thesis aims to synthesise the impact of various realised volatility measures on volatility forecasting. We propose an extension of the Realised GARCH model that incorporates an autoencoder-generated synthetic realised measure.
  arXiv  Detail & Related papers  (2024-11-26T06:05:44Z)
• Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting [46.63798583414426]
  Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis. Our study demonstrates, through both analytical and empirical evidence, that decomposition is key to containing excessive model inflation. Remarkably, by tailoring decomposition to the intrinsic dynamics of time series data, our proposed model outperforms existing benchmarks.
  arXiv  Detail & Related papers  (2024-01-22T13:15:40Z)
• Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach [2.0213537170294793]
  This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting.
  arXiv  Detail & Related papers  (2023-12-31T16:38:32Z)
• Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework [27.025720728622897]
  We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks. We apply this framework to identify and monitor the evolution of interconnectedness and systemic risk among major assets and industrial sectors within the financial network.
  arXiv  Detail & Related papers  (2023-12-27T20:09:57Z)
• It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
  Attack Graph (AG) represents the best-suited solution to support cyber risk assessment for multi-step attacks on computer networks. Current solutions propose to address the generation problem from the algorithmic perspective and postulate the analysis only after the generation is complete. This paper rethinks the classic AG analysis through a novel workflow in which the analyst can query the system anytime.
  arXiv  Detail & Related papers  (2023-12-27T10:44:58Z)
• Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression [53.15502562048627]
  Recent work has built the connection between self-supervised learning and the approximation of the top eigenspace of a graph Laplacian operator. This work delves into a statistical analysis of augmentation-based pretraining.
  arXiv  Detail & Related papers  (2023-06-01T15:18:55Z)
• 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)
• Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model [0.0]
  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.
  arXiv  Detail & Related papers  (2022-07-31T06:24:15Z)
• 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)
• Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting [93.73198973454944]
  Deep learning models have led to significant performance improvements in many problems coming from different domains, including prediction problems of financial time-series data. The Temporal Attention-Augmented Bilinear network was recently proposed as an efficient and high-performing model for Limit Order Book time-series forecasting. In this paper, we propose a low-rank tensor approximation of the model to further reduce the number of trainable parameters and increase its speed.
  arXiv  Detail & Related papers  (2021-07-05T10:15:23Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)
• Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting [16.934920617960085]
  DSARF approximates high dimensional data by a product variables between time dependent weights and spatially dependent factors. DSARF is different from the state-of-the-art techniques in that it parameterizes the weights in terms of a deep switching vector auto-regressive factorization. Our experiments attest the superior performance of DSARF in terms of long- and short-term prediction error, when compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-09-10T20:15:59Z)

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.