Noncausality and the commodity currency hypothesis

Lof, Matthijs; Nyberg, Henri

doi:10.1016/j.eneco.2017.05.024

Cited by 32 publications

(20 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The presence of cross-covariances leads to asymmetries in the autocovariance functions of the different models, which allows for a confirmatory analysis. We study commodity prices that are expected to (i) show signs of noncausality (see, e.g., Karapanagiotidis, 2014;Lof & Nyberg, 2017) due to their forward-looking nature and (ii) depend on an indicator of economic activity (Issler, Rodrigues, & Burjack, 2014) and the US dollar exchange rate.…”

Section: Introductionmentioning

confidence: 99%

Mixed causal–noncausal autoregressions with exogenous regressors

Hecq

Issler

Telg

2020

J of Applied Econometrics

View full text Add to dashboard Cite

Mixed causal-noncausal autoregressive (MAR) models have been proposed to model time series exhibiting nonlinear dynamics. Possible exogenous regressors are typically substituted into the error term to maintain the MAR structure of the dependent variable. We introduce a representation including these covariates called MARX to study their direct impact. The asymptotic distribution of the MARX parameters is derived for a class of non-Gaussian densities. For a Student t likelihood, closed-form standard errors are provided. By simulations, we evaluate the MARX model selection procedure using information criteria. We examine the influence of the exchange rate and industrial production index on commodity prices.

show abstract

Section: Introductionmentioning

confidence: 99%

Mixed causal–noncausal autoregressions with exogenous regressors

Hecq

Issler

Telg

2020

J of Applied Econometrics

View full text Add to dashboard Cite

show abstract

“…Papers by Hencic and Gouriéroux (2015), Gouriéroux and Zakoian (2015), Gouriéroux and Jasiak (2016), Gouriéroux and Zakoian (2017) and Fries and Zakoian (2019), assume Cauchy distributed disturbances in (1), that is, very fat-tailed distributions needed to capture bubble-like dynamics. For other macroeconomic variables such as inflation or interest rates, a popular choice is the Student's t-distribution with scale parameter > 0 and > 2 degrees of freedom, see inter alia Lanne and Saikkonen (2011), Nyberg, Lanne and Saarinen (2012), Lanne and Saikkonen (2013), Lof (2013) or Lof and Nyberg (2017). With these Student's t-disturbances, the approximate maximum likelihood estimation (MLE) approach has been advocated by , Andrews, Davis and Breidt (2006) and further promoted by Lanne and Saikkonen (2011).…”

Section: Introductionmentioning

confidence: 99%

Mixed Causal–Noncausal Autoregressions: Bimodality Issues in Estimation and Unit Root Testing1

2020

View full text Add to dashboard Cite

This paper stresses the bimodality of the likelihood function of the Mixed causal-noncausal AutoRegressions (MAR), and it is shown that the bimodality issue becomes more salient as the causal root approaches unity from below. The consequences are important as the roots of the local maxima are typically interchanged, attributing the noncausal component to the causal one and vice-versa. This severely changes the interpretation of the results, and the properties of unit root tests of the backward root are adversely affected. To circumvent the bimodality issue, this paper proposes an estimation strategy which (i) increases noticeably the probability of attaining the global MLE; and (ii) selects carefully the maximum used for the unit root test against a MAR stationary alternative.

show abstract

“…These countries are usually considered in studies analyzing predictability from exchange rates to commodity prices. See for instance, CRR, Bork, Rovira and Sercu (2014) and Lof and Nyberg (2017).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Aluminum Prices with Commodity Currencies

Pincheira

Hardy

2019

SSRN Journal

View full text Add to dashboard Cite

In this paper we show that the exchange rates of some commodity exporter countries have the ability to predict the price of spot and future contracts of aluminum. This is shown with both insample and out-of-sample analyses. The theoretical underpinning of these results relies on the present-value model for exchange rate determination and on the tight connection between commodity prices and the currencies of commodity exporter countries. We show results using traditional statistical metrics of forecast accuracy: Mean Squared Prediction Error and Mean Directional Accuracy. We also show that the first principal component of our sample of exchange rates is a useful way to summarize the predictive information contained in our set of commodity currencies.

show abstract

Noncausality and the commodity currency hypothesis

Cited by 32 publications

References 53 publications

Mixed causal–noncausal autoregressions with exogenous regressors

Mixed causal–noncausal autoregressions with exogenous regressors

Mixed Causal–Noncausal Autoregressions: Bimodality Issues in Estimation and Unit Root Testing1

Forecasting Aluminum Prices with Commodity Currencies

Contact Info

Product

Resources

About