Temporal aggregation and the power of tests for a unit root

Pierse, Richard; Snell, Andy

doi:10.1016/0304-4076(93)01589-e

Cited by 85 publications

(53 citation statements)

References 20 publications

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“…For instance, Shiller and Perron (1985), Perron (1991), and Campbell and Perron (1991) find through Monte Carlo simulations that the power of unit root tests is mainly affected by the time span, and much less by sampling frequency. In line with this finding, Pierse and Snell (1995) show theoretically that the asymptotic local power of a unit root test is not dependent on sample frequency. However, Boswijk and Klaassen (2012) demonstrate that this result does not hold for time series that exhibit fat tails and volatility clustering.…”

Section: Introductionsupporting

confidence: 65%

Detecting Periods of Exuberance: A Look at the Role of Aggregation with an Application to House Prices

Pavlidis¹,

Martínez‐García²,

Grossman³

2017

gwp

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The recently developed SADF and GSADF unit root tests of Phillips et al. (2011) andPhillips et al. (2015) have become popular in the literature for detecting exuberance in asset prices. In this paper, we examine through simulation experiments the effect of cross-sectional aggregation on the power properties of these tests. The simulation design considered is based on actual housing data for both U.S. metropolitan and international housing markets and thus allows us to draw conclusions for different levels of aggregation. Our findings suggest that aggregation lowers the power of both the SADF and GSADF tests. The effect, however, is much larger for the SADF test. We also provide evidence that tests based on panel data techniques, namely the panel GSADF test recently proposed by Pavlidis et al. (2015), can perform substantially better than univariate tests applied to aggregated series. JEL codes: C22, C12, G12, R30, R31

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Section: Introductionsupporting

confidence: 65%

Detecting Periods of Exuberance: A Look at the Role of Aggregation with an Application to House Prices

Pavlidis¹,

Martínez‐García²,

Grossman³

2017

gwp

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“…For the coal price series ADF tests conducted on the corresponding yearly series over the period suggests that a stochastic trend is an appropriate specification. The works of Shiller and Perron (1985) and Pierse and Snell (1995) indicate, in fact, that, in general, the power of unit-root tests depends more on the sample size than on the sample frequency. Then, a conclusion about the order of integration should preferably be drawn by low frequency data covering a longer time span.…”

Section: Discussionmentioning

confidence: 99%

Modelling Evolutionary Long-Run Relationships: An Application to the Italian Energy Market

Morana

1998

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract The paper considers a SUTSE model embedded in a dynamic framework to estimate an energy cost share model for the Italian economy in an evolutionary environment. This is achieved by allowing stochastic seasonal and trend components in the long-run specification and constructing an error correction mechanism to model short-run dynamics. Modelling instability in the structural time series approach has provided some improvement in the estimates of the elasticities of substitutions and of the price elasticities with respect to those obtained using deterministic trend and seasonal components. Tests for instability in the cointegrating regression support the evolutionary specification adopted. Terms of use: Documents in EconStor may

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“…Thus, a time series which is stationary (non stationary) at the disaggregated level remains so at the aggregated level. This property has been directly or indirectly derived among others by TELSER (1967), AMEMIYA and WU (1972), TIAO (1972), BREWER (1973), TIAO and WEI (1976), WEI (1981), HARVEY (1981), AHSANULLAH and WEI (1984), WEISS (1984), STRAM and WEI (1986), CHRISTIANO and EICHENBAUM (1987), ROSSANA andSEATER (1995) andPIERSE andSNELL (1995). Most of these studies resorted to analytical tools, accompanied by Monte Carlo experiments and empirical evidence to draw and back up their conclusions.…”

Section: Aggregation Over Timementioning

confidence: 99%

“…That said, a look at the literature reveals that while the first three questions have been systematically examined, this is not the case for the last question. Following the pioneering work of SHILLER and PERRON (1985) as well as PERRON (1987PERRON ( , 1989) in unit root context, HAKKIO and RUSH (1991), MAMINGI (1992MAMINGI ( , 2005b), HOOKER (1993), LAHIRI and MAMINGI (1995), PIERSE and SNELL (1995), OTERO and SMITH (2000) and HAUG (2002) examined the impact of the data span on the power of tests for cointegration. However, with the exception of MAMINGI (1992MAMINGI ( , 2005b no study has examined explicitly the issue in the context of the three scenarios of aggregation over time.…”

Section: Alteration Of Power Of Testsmentioning

confidence: 99%

“…To illustrate the importance of the data span in the context of residual-based tests for cointegration, PIERSE and SNELL (1995) use the relationship between real non-durable consumption and real net wealth for UK data for different data spans. The residual-based cointegration tests of interest are: the CRDW (cointegration Durbin Watson), the ADF (augmented Dickey-Fuller) and the Z t (Phillips-Ouliaris) test.…”

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confidence: 99%

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Beauty and Ugliness of Aggregation over Time: A Survey

Mamingi

2017

Review of Economics

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This paper delivers an up-to-date literature review dealing with aggregation over time of economic time series, e.g. the transformation of highfrequency data to low frequency data, with a focus on its benefits (the beauty) and its costs (the ugliness). While there are some benefits associated with aggregating data over time, the negative effects are numerous. Aggregation over time is shown to have implications for inferences, public policy and forecasting.

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Temporal aggregation and the power of tests for a unit root

Cited by 85 publications

References 20 publications

Detecting Periods of Exuberance: A Look at the Role of Aggregation with an Application to House Prices

Detecting Periods of Exuberance: A Look at the Role of Aggregation with an Application to House Prices

Modelling Evolutionary Long-Run Relationships: An Application to the Italian Energy Market

Beauty and Ugliness of Aggregation over Time: A Survey

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