Spatial Market Arbitrage and Threshold Cointegration

Sephton, Peter S.

doi:10.1111/1467-8276.00506

Cited by 58 publications

(36 citation statements)

References 16 publications

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“…Furthermore, in both applications the RB estimates of the adjustment parameters are more plausible. In the application with the Goodwin and Piggott (2001) data they appear to confirm Sephton's (2003) finding that the twothreshold TVECM is misspecified. In the application with Spanish and German pork prices they are, unlike the profile likelihood estimates, consistent with spatial equilibrium theory and price transmission between the markets in question.…”

Section: Price Transmission Between German and Spanish Pork Pricessupporting

confidence: 57%

“…If trade only flows in one direction, then a model with one threshold and two regimes would be more appropriate. Sephton (2003), who also revisits the Goodwin and Piggott (2001) data, finds that a one-threshold model is indicated for four of the six pairs, and that the pairs Raleigh-Fayetteville and GreenvilleFayetteville display little evidence of any threshold effects whatsoever. Our regularized Bayesian estimates of very similar or identical adjustment coefficients across regimes for some market pairs appear to corroborate Sephton's finding.…”

Section: Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

The Estimation of Threshold Models in Price Transmission Analysis

Greb¹,

Cramon‐Taubadel²,

Krivobokova³

et al. 2013

American J Agri Economics

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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. October 8, 2012 Terms of use: Documents in AbstractThe threshold vector error correction model is a popular tool for the analysis of spatial price transmission and market integration. In the literature, the profile likelihood estimator is the preferred choice for estimating this model. Yet, in certain settings this estimator performs poorly. In particular, if the true thresholds are such that one or more regimes contain only a small number of observations, if unknown model parameters are numerous or if parameters differ little between regimes, the profile likelihood estimator displays large bias and variance. Such settings are likely when studying price transmission. For simpler, but related threshold models Greb et al. (2011) have developed an alternative estimator, the regularized Bayesian estimator, which does not exhibit these weaknesses. We explore the properties of this estimator for threshold vector error correction models. Simulation results show that it outperforms the profile likelihood estimator, especially in situations in which the profile likelihood estimator fails. Two empirical applications -a reassessment of the the seminal paper by Goodwin and Piggott (2001), and an analysis of price transmission between German and Spanish markets for pork -demonstrate the relevance of the new approach for spatial price transmission analysis.

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Section: Price Transmission Between German and Spanish Pork Pricessupporting

confidence: 57%

Section: Simulationsmentioning

confidence: 99%

The Estimation of Threshold Models in Price Transmission Analysis

Greb¹,

Cramon‐Taubadel²,

Krivobokova³

et al. 2013

American J Agri Economics

View full text Add to dashboard Cite

show abstract

“…Empirical investigations of the role of nonlinearity as pertains to the LOP have been reported by Goodwin and Piggott (2001), Lo and Zivot (2001), Sephton (2003), Balcombe, Bailey, and Brooks (2007), and Park, Mjelde, and Bessler (2007). The empirical work reported in these studies has been conducted primarily by using variants of discrete threshold cointegration 2 models of the sort introduced by Balke and Fomby (1997).…”

mentioning

confidence: 99%

The Commodity Terms of Trade, Unit Roots, and Nonlinear Alternatives: A Smooth Transition Approach

Balagtas

Holt

2009

American J Agri Economics

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Market price dynamics for North American oriented strand board markets are examined. Specifically, the role of transactions costs are examined vis-à-vis the law of one price. Weekly data for the January 3rd, 1995 through April 14th, 2006 period are used in the analysis. Nonlinearities induced by unobservable transactions costs are modelled by estimating smooth transition autoregressions (STARs). Results indicate that nonlinearity is an important feature of these markets and that the parity relationships implied by economic theory are generally supported by the STAR models. Implications for the efficiency of spatial market linkages are examined by estimating generalized impulse response functions.

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“…Since the development of cointegration tests by Johansen (1988), Johansen and Juselius (1990) and Johansen (1991), the error correction framework has been used to examine spatial arbitrage and the law of one price for commodities, trade conditions, and purchasing power parity, to name a few prominent examples (Corbae and Ouliaris, 1988;Enders, 1988;Kim, 1990;Goodwin and Schroeder, 1991;Goodwin, 1992;Johansen and Juselius, 1992;Chowdhury, 1993;Kugler and Lenz, 1993;Adamowicz and Luckert, 1997;Bahmani-Oskooee, 1998;Asche et al, 1999;Boyd, Caporale and Smith, 2001;Goodwin and Piggott, 2001;Caporale and Chui, 2002;Sephton, 2003;Hoover et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Testing for Cointegration in the Presence of Moving Average Errors

Mallory

Lence

2012

Journal of Time Series Econometrics

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This study explores performance of the Johansen cointegration statistics on data containing negative moving average (NMA) errors. Monte Carlo experiments demonstrate that the asymptotic distributions of the statistics are sensitive to NMA parameters, and that using the standard 5% asymptotic critical values results in severe underestimation of the actual test sizes. We demonstrate that problems associated with NMA errors do not decrease as sample size increases; instead, they become more severe. Further we examine evidence that many U.S. commodity prices are characterized by NMA errors. Pretesting data is recommended before using standard asymptotic critical values for Johansen's cointegration tests. AbstractThis study explores performance of the Johansen cointegration statistics on data containing negative moving average (NMA) errors. Monte Carlo experiments demonstrate that the asymptotic distributions of the statistics are sensitive to NMA parameters, and that using the standard 5% asymptotic critical values results in severe underestimation of the actual test sizes. We demonstrate that problems associated with NMA errors do not decrease as sample size increases; instead, they become more severe. Further we examine evidence that many U.S. commodity prices are characterized by NMA errors. Pretesting data is recommended before using standard asymptotic critical values for Johansen's cointegration tests.

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Spatial Market Arbitrage and Threshold Cointegration

Cited by 58 publications

References 16 publications

The Estimation of Threshold Models in Price Transmission Analysis

The Estimation of Threshold Models in Price Transmission Analysis

The Commodity Terms of Trade, Unit Roots, and Nonlinear Alternatives: A Smooth Transition Approach

Testing for Cointegration in the Presence of Moving Average Errors

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