Do Seasonal Adjustments Induce Noncausal Dynamics in Inflation Rates?

Hecq, Alain; Telg, Sean; Lieb, Lenard

doi:10.3390/econometrics5040048

Cited by 12 publications

(6 citation statements)

References 33 publications

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“…The detrended series is reported in Figure 9. We are of course aware that this first step might alter the dynamics of the series, probably in the same manner that a X-11 seasonal filter modifies MAR models (see Hecq, Telg, and Lieb, 2017). We leave this important issue for further research.…”

Section: Empirical Analysismentioning

confidence: 98%

Forecasting bubbles with mixed causal-noncausal autoregressive models

Hecq

Voisin

2021

Econometrics and Statistics

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This paper investigates one-step ahead density forecasts of mixed causalnoncausal models. We compare the sample-based and the simulations-based approaches respectively developed by Gouriéroux and Jasiak (2016) and Lanne, Luoto, and Saikkonen (2012). We focus on explosive episodes and therefore on predicting turning points of bubbles bursts. We suggest the use of both methods to construct investment strategies based on how much probabilities are induced by the assumed model and by past behaviours. We illustrate our analysis on Nickel prices series.

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Section: Empirical Analysismentioning

confidence: 98%

Forecasting bubbles with mixed causal-noncausal autoregressive models

Hecq

Voisin

2021

Econometrics and Statistics

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“…including deconvolution of seismic signals [Wiggins (1978), Donoho (1981), Hsueh and Mendel (1985)], and analysis of astronomical data [Scargle (1981)]. Recent years have witnessed the emergence of a significant line of research on noncausal models in the econometric literature [see e.g., Lanne, Nyberg and Saarinen (2012), Lanne, Saikkonen (2011), Davis and Song (2012), Chen, Choi and Escanciano (2012), Hencic and Gouriéroux (2015), Velasco and Lobato (2015), Hecq, Lieb and Telg (2016, 2017a, 2017b, Cavaliere, Nielsen and Rahbek (2017)]. The distinction between causal and noncausal processes is only meaningful in a non-Gaussian framework, and the increasing interest in Mixed causal-noncausal AR processes (MAR) parallels the widespread use of non-Gaussian heavy-tailed processes in economic or financial applications.…”

Section: Introductionmentioning

confidence: 99%

Mixed Causal-Noncausal Ar Processes and the Modelling of Explosive Bubbles

Fries

Zakoïan²

2019

Econom. Theory

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Noncausal autoregressive models with heavy-tailed errors generate locally explosive processes and, therefore, provide a convenient framework for modelling bubbles in economic and financial time series. We investigate the probability properties of mixed causal-noncausal autoregressive processes, assuming the errors follow a stable non-Gaussian distribution. Extending the study of the noncausal AR(1) model by Gouriéroux and Zakoian (2017), we show that the conditional distribution in direct time is lighter-tailed than the errors distribution, and we emphasize the presence of ARCH effects in a causal representation of the process. Under the assumption that the errors belong to the domain of attraction of a stable distribution, we show that a causal AR representation with non-i.i.d. errors can be consistently estimated by classical least-squares. We derive a portmanteau test to check the validity of the estimated AR representation and propose a method based on extreme residuals clustering to determine whether the AR generating process is causal, noncausal, or mixed. An empirical study on simulated and real data illustrates the potential usefulness of the results.

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“…The series were not seasonality-adjusted because Granger causality relies on projections of one series onto another. The filtering process (e.g., X-11 filter) can produce unintended interferences in the results; see Dufour and Tessier (2006) and Hecq et al (2017). The analyzed data were obtained from FED St. Louis, see McCracken and Ng (2016), covering from 1959.Q1 to 2019.Q4.…”

Section: Stock Market and Macroeconomic Variablesmentioning

confidence: 99%

Non‐parametric short‐ and long‐run Granger causality testing in the frequency domain

Taufemback

2022

Journal Time Series Analysis

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Herein, we propose a novel non-parametric frequency Granger causality test. We apply a filtering process in the time domain to remove possible spurious causality, thereby eliminating potential interference. Thereafter, in the frequency domain, we perform a local kernel regression for each frequency and test the non-causality hypothesis from the distance between each estimate to zero. We provide asymptotic results for strict stationary series concerning 𝛼-mixing conditions. Our method can also perform group causality tests, a feature that is absent in most alternative methods. Monte Carlo experiments illustrate that our method is comparable, and in some cases, performs better than alternative methods in the literature. Finally, we test the causality between monetary policy variables and stock prices.

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Do Seasonal Adjustments Induce Noncausal Dynamics in Inflation Rates?

Cited by 12 publications

References 33 publications

Forecasting bubbles with mixed causal-noncausal autoregressive models

Forecasting bubbles with mixed causal-noncausal autoregressive models

Mixed Causal-Noncausal Ar Processes and the Modelling of Explosive Bubbles

Non‐parametric short‐ and long‐run Granger causality testing in the frequency domain

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