Moving forward with time series analysis

Enns, Peter K.; Kelly, Nathan J.; Masaki, Takaaki; Wohlfarth, Patrick C.

doi:10.1177/2053168017732231

Cited by 5 publications

(8 citation statements)

References 25 publications

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“…Finally, we make the additional point that, while there is consensus that the GECM avoids spurious regression when assumptions are met and it is implemented correctly (e.g., Enns et al , 2016a, 2017b; Grant and Lebo, 2016), this symposium highlights that there is still some disagreement about how frequently these assumptions are met. We understand that part of the disagreement stems from the application of different approaches to simulations.…”

Section: Discussionmentioning

confidence: 95%

“…Yet, some recent studies question the use of GECM/ADL based on type I errors (Grant and Lebo, 2016; Keele et al , 2016), which seemingly contrasts with Enns and Wlezien's (2017) findings and other studies showing that when simulations are implemented correctly, the type I error rate follows the expected 5 percent (Enns et al , 2016a, 2017b; Esarey, 2016). Like all methods, the GECM is only appropriate when relevant assumptions are met.…”

Section: Equation Balance With Mixed Orders Of Integration Does Not N...mentioning

confidence: 90%

“…Since social scientists often confront small samples, it is of course critical to evaluate methods when T is small. However, when conducting these simulations, the series should be diagnosed and model assumptions tested before selecting the model (e.g., Enns et al , 2016a, 2017b) or else both small and large samples should be simulated to assess whether the small sample properties deviate from the asymptotic results (e.g., Enns and Wlezien, 2017). Failure to follow these steps risks misinterpreting simulation results because the statistical model was chosen based on the asymptotic properties of the DGP instead of the properties of the data being analyzed.…”

Section: Equation Balance With Mixed Orders Of Integration Does Not N...mentioning

confidence: 99%

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Detecting true relationships in time series data with different orders of integration

Enns

Moehlecke

Wlezien

2021

PSRM

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It is fairly well-known that proper time series analysis requires that estimated equations be balanced. Numerous scholars mistake this to mean that one cannot mix orders of integration. Previous studies have clarified the distinction between equation balance and having different orders of integration, and shown that mixing orders of integration does not increase the risk of type I error when using the general error correction/autoregressive distributed lag (GECM/ADL) models, that is, so long as equations are balanced (and other modeling assumptions are met). This paper builds on that research to assess the consequences for type II error when employing those models. Specifically, we consider cases where a true relationship exists, the left- and right-hand sides of the equation mix orders of integration, and the equation still is balanced. Using the asymptotic case, we find that the different orders of integration do not preclude identification of the true relationship using the GECM/ADL. We then highlight that estimation is trickier in practice, over finite time, as data sometimes do not reveal the underlying process. But, simulations show that even in these cases, researchers will typically draw accurate inferences as long as they select their models based on the observed characteristics of the data and test to be sure that standard model assumptions are met. We conclude by considering the implications for researchers analyzing or conducting simulations with time series data.

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Section: Discussionmentioning

confidence: 95%

Section: Equation Balance With Mixed Orders Of Integration Does Not N...mentioning

confidence: 90%

Section: Equation Balance With Mixed Orders Of Integration Does Not N...mentioning

confidence: 99%

See 1 more Smart Citation

Detecting true relationships in time series data with different orders of integration

Enns

Moehlecke

Wlezien

2021

PSRM

Self Cite

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“…In addition, it is a strategic test in the cointegration test because it is an engine for the cointegration test and should be done first before the cointegration test. [11] and [12].…”

Section: Introductionmentioning

confidence: 99%

Bus Station Fees Unit Root Testing during A Transition Time: A Time-series Microeconomics Development, Consistencies and Theoretical Unification

Riyardi¹

2019

IJETER

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This paper employs three unit root tests to the daily fees of Tirtonadi Bus Station in Surakarta, Indonesia. The fee consists of Passenger, Parking, Short-distance bus and Long-distance bus fees. The data were collected from transition period starting November until December 2016. The research reveals two findings. The first is regarding the fees. All fees are found to be persistent and are useful. The second is regarding Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and Ng-Perron (NP) unit root tests. They confirm error variance estimation at zero frequency, detrending estimation and optimal lag selection. All of the results open opportunities to time-series microeconomics practice and studying. In addition, they are an example of consistencies and the theoretical unification

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“…() contend with the different substantive conclusions that come from divergent interpretations of unit root test results. Enns and Wlezien () and Pickup and Kellstedt () discuss the complications that arise when analysts conclude their regressors and regressands have different orders of integration. Yet, none of this work provides a general solution to the problem.…”

mentioning

confidence: 99%

Beyond the Unit Root Question: Uncertainty and Inference

Webb

Linn

Lebo

2020

American J Political Sci

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A fundamental challenge facing applied time‐series analysts is how to draw inferences about long‐run relationships (LRR) when we are uncertain whether the data contain unit roots. Unit root tests are notoriously unreliable and often leave analysts uncertain, but popular extant methods hinge on correct classification. Webb, Linn, and Lebo (WLL; 2019) develop a framework for inference based on critical value bounds for hypothesis tests on the long‐run multiplier (LRM) that eschews unit root tests and incorporates the uncertainty inherent in identifying the dynamic properties of the data into inferences about LRRs. We show how the WLL bounds procedure can be applied to any fully specified regression model to solve this fundamental challenge, extend the results of WLL by presenting a general set of critical value bounds to be used in applied work, and demonstrate the empirical relevance of the LRM bounds procedure in two applications.

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Moving forward with time series analysis

Cited by 5 publications

References 25 publications

Detecting true relationships in time series data with different orders of integration

Detecting true relationships in time series data with different orders of integration

Bus Station Fees Unit Root Testing during A Transition Time: A Time-series Microeconomics Development, Consistencies and Theoretical Unification

Beyond the Unit Root Question: Uncertainty and Inference

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