Why do we Sometimes get Nonsense-Correlations between Time-Series?--A Study in Sampling and the Nature of Time-Series

Yule, George

doi:10.2307/2341482

Cited by 969 publications

(386 citation statements)

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“…In effect, any trend function that we postulate in an econometric specification will turn out to be statistically significant in large samples provided the data do in fact have a trend, whether it is of the same form as that specified in the empirical regression or belongs to a general alternative such as (2). The history of this subject has early beginnings dating back to the formal discussion by Yule (1926) [31] and beyond. Over the last two decades, the case of multivariate regression of unit root processes (including independent random walks) was analyzed in [15] and that of polynomial trends fitted to stochastic trends by Durlauf and Phillips [4].…”

Section: No One Understands Trendsmentioning

confidence: 95%

Challenges of trending time series econometrics

Phillips

2005

Mathematics and Computers in Simulation

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We discuss some challenges presented by trending data in time series econometrics. To the empirical economist there is little guidance from theory about the source of trend behavior and even less guidance about practical formulations. Moreover, recent proximity theorems [W. Ploberger, P.C.B. Phillips, Empirical limits for time series econometric models, Econometrica 71 (2003) 627-673] reveal that trends are more elusive to model empirically than stationary processes, with the upshot that optimal forecasts are also harder to estimate when the data involve trends. These limitations are implicitly acknowledged in much practical modeling and forecasting work, where adaptive methods are often used to help keep models on track as trends evolve. The paper discusses these broader issues and limitations of econometrics and offers some thoughts on new practical possibilities for data analysis in the absence of good theory models for trends. In particular, a new concept of coordinate cointegration is introduced and some new econometric methodology is suggested for analyzing trends and co-movement and for producing forecasts in a general way that is agnostic about the specific nature of the trend process. Some simulation exercises are conducted and some long historical series on prices and yields on long securities are used to illustrate the methods.

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Section: No One Understands Trendsmentioning

confidence: 95%

Challenges of trending time series econometrics

Phillips

2005

Mathematics and Computers in Simulation

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“…Seemingly significant (with respect to standard t-statistics) coefficients may emerge from regressions of stochastically independent variables on each other, hence the name 'spurious'. This phenomenon was first observed by Yule (1926), and analyzed analytically in Phillips (1986). In order to obtain meaningful regression results from a regression containing integrated variables, it is necessary that these variables are cointegrated, i.e.…”

Section: Unit Roots Cointegration and Cross-sectional Dependencementioning

confidence: 99%

Exploring the environmental Kuznets hypothesis: Theoretical and econometric problems

Müller–Fürstenberger

Wagner

2007

Ecological Economics

154

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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. Terms of use: Documents in AbstractFocussing on the prime example of CO 2 emissions, we discuss several important theoretical and econometric problems that arise when studying environmental Kuznets curves (EKCs).The dominant theoretical approach is given by integrated assessment modelling, which consists of economic models that are combined with environmental impactmodels. We critically evaluate the aggregation, model dynamics and calibration aspects and their implications for the validity of the results. We then turn to a discussion of several important econometric problems that go almost unnoticed in the literature. The most fundamental problems relate to nonlinear transformations of nonstationary regressors and, in a nonstationary panel context, to neglected cross-sectional dependence. We discuss the implications of these two major and some minor problems that arise in the econometric analysis of Kuznets curves. Our discussion shows that EKC modelling as performed to date is subject to major drawbacks at both the theoretical and the econometric level. KeywordsCarbon Kuznets curve, integrated assessment models, regressions with integrated variables, nonstationary panels JEL Classification Q20, C12, C13 CommentsWe gratefully appreciate the helpful comments of two anonymous reviewers and the editor, which have led to substantial improvements of the paper. Furthermore, the comments of Gregor Bäurle, Robert Kunst, Klaus Neusser, and Reto Tanner are gratefully acknowledged. We thank Benito Müller for providing the data used in this study and for discussions on the topic. The usual disclaimer applies.

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“…Consider the AR(1) process below: Yule (1926). Granger and Newbold (1974) argue that it is a good rule of thumb to suspect that the estimated regression is spurious if R 2 is greater than Durbin-Watson d value; that is R 2 >d.…”

Section: Stationarity and Unit Root Problemmentioning

confidence: 99%

Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey

2007

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In the early 2000s, the Republic of Turkey has initiated an ambitious reform program in her electricity market, which requires privatization, liberalization as well as a radical restructuring. The most controversial reason behind, or justification for, recent reforms has been the rapid electricity demand growth; that is to say, the whole reform process has been a part of the endeavors to avoid so-called "energy crisis". Using cointegration analysis and ARIMA modeling, the present article focuses on this issue by both providing an electricity demand estimation and forecast, and comparing the results with official projections. The study concludes, first, that consumers' respond to price and income changes is quite limited and therefore there is a need for * Corresponding author. Tel.: +90-312-2872560 E-mail: erkan@erdogdu.net URL: http://erkan.erdogdu.net/english b The author is working as an Energy Expert in Energy Market Regulatory Authority of the Republic of Turkey. The present article is a part of the dissertation submitted by the author in fulfillment of the requirements for the award of the degree "MSc in Energy Economics and Policy". In October 2005, the author is awarded an "MSc with distinction" by the Department of Economics, University of Surrey (UK). The views, findings and conclusions expressed in this article are entirely those of the author and do not represent in any way the views of any institution he is affiliated with. 2 economic regulation in Turkish electricity market; and second, that the current official electricity demand projections highly overestimate the electricity demand, which may endanger the development of both a coherent energy policy in general and a healthy electricity market in particular.

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Why do we Sometimes get Nonsense-Correlations between Time-Series?--A Study in Sampling and the Nature of Time-Series

Cited by 969 publications

References 0 publications

Challenges of trending time series econometrics

Challenges of trending time series econometrics

Exploring the environmental Kuznets hypothesis: Theoretical and econometric problems

Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey

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