Unit Root CADF Testing with<i>R</i>

Lupi, Claudio

doi:10.18637/jss.v032.i02

Cited by 27 publications

(10 citation statements)

References 39 publications

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“…1 1 A more detailed description of the procedure is reported in Lupi (2009) and in the extended discussion paper version of this article (Costantini and Lupi, 2011). As a by‐product of our analysis, we can compute a detailed table of asymptotic critical values of the CADF test: see Costantini and Lupi (2011, Table 1).…”

mentioning

confidence: 99%

A Simple Panel‐CADF Test for Unit Roots*

Costantini¹,

Lupi²

2012

Oxf Bull Econ Stat

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In this paper, we propose a simple extension to the panel case of the covariate‐augmented Dickey–Fuller (CADF) test for unit roots developed in Hansen (1995). The panel test we propose is based on a P values combination approach that takes into account cross‐section dependence. We show that the test has good size properties and gives power gains with respect to other popular panel approaches. An empirical application is carried out for illustration purposes on international data to test the purchasing power parity (PPP) hypothesis.

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mentioning

confidence: 99%

A Simple Panel‐CADF Test for Unit Roots*

Costantini¹,

Lupi²

2012

Oxf Bull Econ Stat

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“…Hence, the elements on the diagonal for those institutions are equal to their VaR at 99%. • quantreg [57] • timeSeries [91] • CADFtest [64] • moments [59] • zoo [94] • tidyverse [88] • dplyr [89] • xtable [27] • haven [90] • corrplot [87] • igraph [26]…”

Section: Data Filtering Proceduresmentioning

confidence: 99%

$Δ-$CoES

Aleksy¹

2022

Preprint

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In this paper the ∆-CoVaR method is extended in both the conditional and unconditional cases to be based on the Expected Shortfall (ES) using quantile regression with a more expansive distress definition. We find the resulting ∆-CoES measure to be complementary to the ∆-CoVaR and to be more effective than the ∆-CoVaR in measuring short-term changes in systemic risk and in identifying heterogeneity in the systemic risk contributions of financial institutions and linkages between institutions due to its lower robustness. For regulators, risk managers and market participants these properties are interesting from an economic standpoint when they require the increased sensitivity and heterogeneity of the ∆-CoES to set short-term capital requirements/risk limits, find problematic financial linkages, problematic financial institutions or have some kind of early warning system for the emergence of systemic risk. Lastly, the ∆-CoES is straightforward to estimate and would fit within recent regulatory frameworks such as the FRTB. To show the statistical advantages and properties empirically, the ∆-CoVaR and ∆-CoES methods are used on a large sample (from 31-12-1970 to 31-12-2020 1564 firms) of daily equity data from US financial institutions both in a system and network fashion. On a sample of 9 US-based GSIBS we also show the properties and the utility of the ∆-CoES when it comes to identifying problematic financial links.

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“…Unlike the other packages, urca only comes with critical values to judge the significance of the unit root test, the p-value is not reported, see Table 1. As discussed in Lupi (2009), the p-value reported under summary() is computed using the t-distribution, which is incorrect under the unit root null. Finally, only the packages CADFtest and fUnitRoots can handle missing values, for the other packages, we removed missing values prior to performing the unit root tests.…”

Section: Macrotsmentioning

confidence: 99%

“…The most popular unit root test is the classical augmented-Dickey Fuller (ADF) test Fuller 1979, 1981). Implementations of the ADF test are incorporated in various packages, in particular CADFtest (Lupi 2009), fUnitRoots (Wuertz, Setz, and Chalabi 2017), tseries (Trapletti, Hornik, and LeBaron 2019), and urca (Pfaff 2008). 1 As we will argue in the next section, most 'standard' unit root tests, such as the ones implemented in these packages, require seemingly innocuous choices from the practitioner regarding model specifications or which test to use, that may have a major impact on the performance of the unit root tests.…”

Section: Introductionmentioning

confidence: 99%

bootUR: An R Package for Bootstrap Unit Root Tests

Smeekes¹,

Wilms²

2020

Preprint

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Unit root tests form an essential part of any time series analysis. We provide practitioners with a single, unified framework for comprehensive and reliable unit root testing in the R package bootUR. The package's backbone is the popular augmented Dickey-Fuller (ADF) test paired with a union of rejections principle, which can be performed directly on single time series or multiple (including panel) time series. Accurate inference is ensured through the use of bootstrap methods. The package addresses the needs of both novice users, by providing user-friendly and easy-to-implement functions with sensible default options, as well as expert users, by giving full user-control to adjust the tests to one's desired settings. Our OpenMP-parallelized efficient C++ implementation ensures that all unit root tests are scalable to datasets containing many time series.

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Unit Root CADF Testing withR

Cited by 27 publications

References 39 publications

A Simple Panel‐CADF Test for Unit Roots*

A Simple Panel‐CADF Test for Unit Roots*

$Δ-$CoES

bootUR: An R Package for Bootstrap Unit Root Tests

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