Commodity Price Booms and Breaks: Detection, Magnitude and Implications for Developing Countries

Mariscal, Rodrigo; Powell, Andrew

doi:10.2139/ssrn.2384422

Cited by 16 publications

(13 citation statements)

References 58 publications

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“…Although only one and two shifts were considered in detail, the general nature of the approach makes it applicable when there are multiple shifts. There have already been a number of applications of SIS to empirical problems, including commodity price shifts in [35], location shifts in U.K. real wage determination in [21] and detecting crises in [15], as well as variants to measure the impacts of volcanic eruptions on temperature in [30]. Non-linearities that are relevant are not 'lost' by using SIS, whereas irrelevant, but spuriously significant ones can be eliminated by SIS.…”

Section: Resultsmentioning

confidence: 99%

Detecting Location Shifts during Model Selection by Step-Indicator Saturation

et al. 2015

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To capture location shifts in the context of model selection, we propose selecting significant step indicators from a saturating set added to the union of all of the candidate variables. The null retention frequency and approximate non-centrality of a selection test are derived using a 'split-half' analysis, the simplest specialization of a multiple-path block-search algorithm. Monte Carlo simulations, extended to sequential reduction, confirm the accuracy of nominal significance levels under the null and show retentions when location shifts occur, improving the non-null retention frequency compared to the corresponding impulse-indicator saturation (IIS)-based method and the lasso.

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Section: Resultsmentioning

confidence: 99%

Detecting Location Shifts during Model Selection by Step-Indicator Saturation

et al. 2015

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“…The series of interest are 100 times the logarithm of the ratio of the prices of each commodity to a manufacturing unit value index. The main reason to choose this particular dataset is its wide popularity, as many influential empirical studies found in the literature have used either its individual commodity price series (León and Soto, 1997;Cuddington, 1992;Harvey et al, 2011;Yamada and Yoon, 2014) or its aggregate indices (Cuddington and Urzua, 1989;Bleaney and Greenaway, 1993;Zanias, 2005;Cuddington et al, 2008;Mariscal and Powell, 2014).…”

Section: Testing For Unit Roots In Commodity Pricesmentioning

confidence: 99%

“…In a related analysis,Mariscal and Powell (2014) show that, after correcting the historical data for various level shifts, the logarithm of an aggregate commodity prices index cointegrates with the logarithm of a manufacturing unit value index. The cointegrating vector is (1, −1) which indicates that the relative commodity price index is stationary.…”

mentioning

confidence: 99%

Unit roots, flexible trends, and the Prebisch-Singer hypothesis

Winkelried

2018

Journal of Development Economics

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This paper studies the dynamic properties of relative commodity prices, especially the Prebisch-Singer hypothesis on the secular decline in these series, using a new family of unit root tests that is based on the Fourier approximation to the underlying trend of the data. The approximation controls for low-frequency variations such as structural breaks, or such as the long swings induced by hypothesized super cycles in the data. Relative to the extant literature, we find considerably more evidence in favor of trend stationarity in relative commodity prices, and relatively limited support for the Prebisch-Singer hypothesis.

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“…The following are specifications for simulation settings for a reference DGP: − Sample size T=240 observations, reflecting 20 years of monthly data. − Locations of the AO are as: A single AO was positioned right in the middle of the sample, while double AO were predetermined at the [0.25, 0.75] as a share of length T. − Target size or significance level, α=0.001, 0.01 and 0.025 [13] defined α as the statistical tolerance of the procedure to control the risk of inadvertently retain any irrelevant indicator. For example, a target of 0.01 for IIS indicates that on average, for every 100 observations, we accept a maximum of single impulse dummy that is not included in the data generating process.…”

Section: Monte Carlo Simulations Settingsmentioning

confidence: 99%

Outliers detection in state-space model using indicator saturation approach

Rose

Ismail²,

Tumin³

2021

IJEECS

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Structural changes that occur due to outliers may reduce the accuracy of an estimated time series model, shifting the mean distribution and causing forecast failure. This study used general-to-specific approach to detect outliers via indicator saturation approach in the local level model framework. Focusing on impulse indicator saturation, performance recorded by the suggested approach was evaluated using Monte Carlo simulations. To tackle the issue of higher number of regressors compared to the number of observations, this research utilized the split-half approach algorithm. We found that the impulse indicator saturation performance relies heavily on the size of outlier, location of outlier and number of splits in the series examined. Detection of outliers using sequential and non-sequential algorithms is the most crucial issue in this study. The sequential searching algorithm was able to outperform the non-sequential searching algorithm in eliminating the non-significant indicators based on potency and gauge. The outliers captured using impulse indicator saturation in financial times stock exchange (FTSE) United States of America (USA) shariah index correspond to the financial crisis in 2008-2009.

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Commodity Price Booms and Breaks: Detection, Magnitude and Implications for Developing Countries

Cited by 16 publications

References 58 publications

Detecting Location Shifts during Model Selection by Step-Indicator Saturation

Detecting Location Shifts during Model Selection by Step-Indicator Saturation

Unit roots, flexible trends, and the Prebisch-Singer hypothesis

Outliers detection in state-space model using indicator saturation approach

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