Estimation and HAC-based Inference for Machine Learning Time Series Regressions

Babii, Andrii; Ghysels, Éric; Striaukas, Jonas

doi:10.2139/ssrn.3503191

Cited by 3 publications

(3 citation statements)

References 86 publications

(106 reference statements)

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“…The ridge regression would typically require m/T → 0, cf., Carrasco et al (2007b). The LASSO would require the approximate sparsity, somewhat stronger weak dependence conditions, and m 1/κ /T 1−1/κ → 0, where κ measures tails and weak dependence, cf., Babii et al (2019).…”

Section: Monte Carlo Experimentsmentioning

confidence: 99%

“…3 In particular, Belloni et al (2012) propose to use the LASSO to address the problem of many instruments and the nonparametric series estimation of the optimal instrument. Our mixed-frequency IV regression is qualitatively different from the above models and does not impose the approximate sparsity on the highdimensional slope coefficients, see Babii, Ghysels, and Striaukas (2019) for a comprehensive treatment of approximately sparse mixed-frequency time series regressions. The problem of the optimal instrument is more challenging in our nonparametric setting and is left for future research, see Florens and Sokullu (2018) for some steps in this direction.…”

Section: Introductionmentioning

confidence: 99%

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High-dimensional mixed-frequency IV regression

Babii¹

2020

Preprint

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This paper introduces a high-dimensional linear IV regression for the data sampled at mixed frequencies. We show that the high-dimensional slope parameter of a high-frequency covariate can be identified and accurately estimated leveraging on a low-frequency instrumental variable. The distinguishing feature of the model is that it allows handing high-dimensional datasets without imposing the approximate sparsity restrictions. We propose a Tikhonovregularized estimator and derive the convergence rate of its mean-integrated squared error for time series data. The estimator has a closed-form expression that is easy to compute and demonstrates excellent performance in our Monte Carlo experiments. We estimate the real-time price elasticity of supply on the Australian electricity spot market. Our estimates suggest that the supply is relatively inelastic and that its elasticity is heterogeneous throughout the day. * First draft: October 2014. This paper is a substantially revisited Chapter 2 of my Ph.D. thesis. I'm deeply indebted to my advisor Jean-Pierre Florens and other members of my Ph.D. committee: Eric Gautier, Ingrid van Keilegom, and Timothy Christensen for helpful discussions and suggestions.

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Section: Monte Carlo Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-dimensional mixed-frequency IV regression

Babii¹

2020

Preprint

Self Cite

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“…The methodologies we employ have been applied in the context of macroeconomic forecasting using a large number of predictors. Specifically, studies using shrinkage methods to examine the predictability of key macroeconomic indicators include Bai and Ng (2008), De Mol et al (2008), Stock and Watson (2012), Carrasco and Rossi (2016), Kotchoni et al (2019), and Babii et al (2019). The advantages of machine learning in the context of asset pricing and return predictability have been explored among others by Rapach et al (2013), Neely et al (2014), Lima and Meng (2017), Kelly et al (2019), Rapach et al (2019), and Kozak (2019).…”

Section: Introductionmentioning

confidence: 99%

Does model complexity add value to asset allocation? Evidence from machine learning forecasting models

Kynigakis

Panopoulou

2021

J of Applied Econometrics

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Summary This study evaluates the benefits of integrating return forecasts from a variety of machine learning and forecast combination methods into an out‐of‐sample asset allocation framework. The economic evaluation of the forecasts shows that model complexity translates to improved results in the majority of cases considered, with shrinkage methods and shallow neural networks generating the highest individual performance. Overall, an investor would consistently realize superior out‐of‐sample gains by incorporating forecast combinations of machine learning models in the portfolio formation process.

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How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

Stübinger

Adler

2020

Algorithms

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This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.

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Estimation and HAC-based Inference for Machine Learning Time Series Regressions

Cited by 3 publications

References 86 publications

High-dimensional mixed-frequency IV regression

High-dimensional mixed-frequency IV regression

Does model complexity add value to asset allocation? Evidence from machine learning forecasting models

How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

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