Long-term prediction intervals of economic time series

Chudy, Marek; Karmakar, Sayar; Wu, Wei Biao

doi:10.1007/s00181-019-01689-2

Cited by 19 publications

(22 citation statements)

References 53 publications

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“…Additionally, using the Gaussian kernel density to obtain the kernel quantile estimator (See Sheather and Marron (1990)) further improves the performance in prediction. These adjustments are supported by the empirical evidence given in Chudỳ et al (2020) in a univariate setup.…”

Section: Bootstrap Adjustmentsupporting

confidence: 61%

“…for some p > 2, it is easy to derive the following conditions on a i to ensure the convergence in (3.2). The proof can be found in the appendix of Chudỳ et al (2020).…”

Section: Linear Error Process: Theoretical Resultsmentioning

confidence: 99%

“…where ∆ denotes the Levy distance, m → ∞ and σ 2 = lim m→∞ S m 2 2 /m is the long-run variance. We start by collecting an asymptotic normality result for linear process the results for the linear processes from Chudỳ et al (2020).…”

Section: Prediction Interval For Error Processmentioning

confidence: 99%

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Long-term prediction intervals with many covariates

Karmakar¹,

Chudy²,

Wu³

2020

Preprint

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Accurate forecasting is one of the fundamental focus in the literature of econometric time-series. Often practitioners and policy makers want to predict outcomes of an entire time horizon in the future instead of just a single kstep ahead prediction. These series, apart from their own possible non-linear dependence, are often also influenced by many external predictors. In this paper, we construct prediction intervals of time-aggregated forecasts in a highdimensional regression setting. Our approach is based on quantiles of residuals obtained by the popular LASSO routine. We allow for general heavy-tailed, long-memory, and nonlinear stationary error process and stochastic predictors. Through a series of systematically arranged consistency results we provide theoretical guarantees of our proposed quantile-based method in all of these scenarios. After validating our approach using simulations we also propose a novel bootstrap based method that can boost the coverage of the theoretical intervals. Finally analyzing the EPEX Spot data, we construct prediction intervals for hourly electricity prices over horizons spanning 17 weeks and contrast them to selected Bayesian and bootstrap interval forecasts.

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Section: Bootstrap Adjustmentsupporting

confidence: 61%

“…for some p > 2, it is easy to derive the following conditions on a i to ensure the convergence in (3.2). The proof can be found in the appendix of Chudỳ et al (2020).…”

Section: Linear Error Process: Theoretical Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Long-term prediction intervals with many covariates

Karmakar¹,

Chudy²,

Wu³

2020

Preprint

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“…Time-aggregated prediction here stands for the prediction of Y n+1 + • • • + Y n+h after observing {Y t } n t=1 . Such predictions remain crucial for strategic decisions implemented by commodity or service providers, ( [5,6]), trust funds, pension management, insurance companies, portfolio management of specific derivatives ( [7]) and assets ( [8]). Time-aggregated forecasting is also able to provide some degree of confidence in understanding the general trend in the near future, potentially for the entire following week or months ahead, which is definitely more meaningful than merely understanding what might happen for any single step ahead (predicting Y n+h for one value of h) in the time horizon.…”

Section: Introductionmentioning

confidence: 99%

Model-Free Time-Aggregated Predictions for Econometric Datasets

Karmakar

2021

Forecasting

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Forecasting volatility from econometric datasets is a crucial task in finance. To acquire meaningful volatility predictions, various methods were built upon GARCH-type models, but these classical techniques suffer from instability of short and volatile data. Recently, a novel existing normalizing and variance-stabilizing (NoVaS) method for predicting squared log-returns of financial data was proposed. This model-free method has been shown to possess more accurate and stable prediction performance than GARCH-type methods. However, whether this method can sustain this high performance for long-term prediction is still in doubt. In this article, we firstly explore the robustness of the existing NoVaS method for long-term time-aggregated predictions. Then, we develop a more parsimonious variant of the existing method. With systematic justification and extensive data analysis, our new method shows better performance than current NoVaS and standard GARCH(1,1) methods on both short- and long-term time-aggregated predictions. The success of our new method is remarkable since efficient predictions with short and volatile data always carry great importance. Additionally, this article opens potential avenues where one can design a model-free prediction structure to meet specific needs.

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“…Prediction intervals express uncertainty in terms of confidence probabilities, of which humans have a natural cognitive intuition (Cosmides & Tooby, 1996;Juanchich & Miroslav, 2020) of guidance for decision making. As such, their use to quantify uncertainty is standardized across a wide variety of safety-critical regression applications, including medicine (IntHout et al, 2016), economics (Chudý M. et al, 2020), finance (Huang & Hsu, 2020); as well as in the forecasting of electrical load (Quan et al, 2014), solar energy (Galván et al, 2017), gas flow (Sun et al, 2017), wind power (Wang et al, 2017), and many other forecasting problems (Makridakis et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Can a single neuron learn quantiles?

Solano-Carrillo¹

2021

Preprint

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A novel non-parametric quantile estimation method for continuous random variables is introduced, based on a minimal neural network architecture consisting of a single unit. Its advantage over estimations from ranking the order statistics is shown, specifically for small sample size. In a regression context, the method can be used to quantify predictive uncertainty under the split conformal prediction setting, where prediction intervals are estimated from the residuals of a pre-trained model on a held-out validation set to quantify the uncertainty in future predictions. Benchmarking experiments demonstrate that the method is competitive in quality and coverage with state-of-the-art solutions, with the added benefit of being more computationally efficient.Preprint. Under review.

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Long-term prediction intervals of economic time series

Cited by 19 publications

References 53 publications

Long-term prediction intervals with many covariates

Long-term prediction intervals with many covariates

Model-Free Time-Aggregated Predictions for Econometric Datasets

Can a single neuron learn quantiles?

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