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Largevars: An R Package for Testing Large VARs for the Presence of Cointegration

Anna Bykhovskaya, Vadim Gorin, Eszter Kiss

arXiv 8 Sep 2025 · Econometrics

arXiv:2509.06295 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Cointegration is a property of multivariate time series that determines whether its non-stationary, growing components have a stationary linear combination. Largevars R package conducts a cointegration test for high-dimensional vector autoregressions of order k based on the large N, T asymptotics of Bykhovskaya and Gorin (2022, 2025). The implemented test is a modification of the Johansen likelihood ratio test. In the absence of cointegration the test converges to the partial sum of the Airy_1 point process, an object arising in random matrix theory. The package and this article contain simulated quantiles of the first ten partial sums of the Airy_1 point process that are precise up to the first 3 digits. We also include two examples using Largevars: an empirical example on S&P100 stocks and a simulated VAR(2) example.

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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1A. Bykhovskaya and V. Gorin (2025) Asymptotics of cointegration tests for high-dimensional VAR($k$)1.000125100%
2A. Bykhovskaya and V. Gorin (2022) Cointegration in large VARs1.000105100%
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4S. Johansen (1991) Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models0.87452100%
5A. Onatski and C. Wang (2018) Alternative asymptotics for cointegration tests in large vars0.81142100%
6A. Bejan (2005) Largest eigenvalues and sample covariance matrices. Tracy-widom and Painlevé ii: computational aspects and realization in s-plus…0.73732100%
7S. Johansen (1995) Likelihood-based inference in cointegrated vector autoregressive models0.73732100%
8A. Onatski and C. Wang (2019) Extreme canonical correlations and high-dimensional cointegration analysis0.64422100%
9I. M. Johnstone, Z. Ma, P. O. Perry, and M. Shahram (2022) RMTstat: Distributions, Statistics and Tests derived from Random Matrix Theory, 20220.64422100%
10R. Engle and C. Granger (1987) Co-integration and error correction: representation, estimation, and testing0.51121100%

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1Canonical correlation analysis of stochastic trends via functional approximation0.00011