Random coefficient continuous systems: Testing for extreme sample path behavior

Tao, Yubo; Phillips, Peter C.B.; Yu, Jun

doi:10.1016/j.jeconom.2019.01.002

Cited by 17 publications

(28 citation statements)

References 54 publications

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“…Unlikeâ n ,ĉ n is inconsistent irrespective of whether u" = 0 and this accords with known results for simpler models without STUR e¤ects (Phillips, 1987). However, the localizing coe¢ cient c may be estimated consistently under certain conditions when the data support joint large span and in…ll asymptotics, as shown in Tao et al (2017).…”

Section: Parameter Estimationsupporting

confidence: 79%

“…The estimator is consistent when u" = 0. When u" 6 = 0; the parameter a may be estimated consistently using instrumental variables (Lieberman and Phillips, 2017b) or by in…ll asymptotics via a two-stage process involving realized variance when high frequency data is available (Tao et al, 2017)). Unlikeâ n ,ĉ n is inconsistent irrespective of whether u" = 0 and this accords with known results for simpler models without STUR e¤ects (Phillips, 1987).…”

Section: Parameter Estimationmentioning

confidence: 99%

“…So the model may be considered a discrete time version of such a system. Working directly with this nonlinear continuous time system, Tao et. al.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid stochastic local unit roots

Lieberman

Phillips

2020

Journal of Econometrics

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Two approaches have dominated formulations designed to capture small departures from unit root autoregressions. The …rst involves deterministic departures that include local-to-unity (LUR) and mildly (or moderately) integrated (MI) speci…cations where departures shrink to zero as the sample size n ! 1. The second approach allows for stochastic departures from unity, leading to stochastic unit root (STUR) speci…cations. This paper introduces a hybrid local stochastic unit root (LSTUR) speci…cation that has both LUR and STUR components and allows for endogeneity in the time varying coe¢ cient that introduces structural elements to the autoregression. This hybrid model generates trajectories that, upon normalization, have non-linear di¤usion limit processes that link closely to models that have been studied in mathematical …nance, particularly with respect to option pricing. It is shown that some LSTUR parameterizations have a mean and variance which are the same as a random walk process but with a kurtosis exceeding 3, a feature which is consistent with much …nancial data. We develop limit theory and asymptotic expansions for the process and document how inference in LUR and STUR autoregressions is a¤ected asymptotically by ignoring one or the other component in the more general hybrid generating mechanism. In particular, we show how con…dence belts constructed from the LUR model are a¤ected by the presence of a STUR component in the generating mechanism. The import of these …ndings for empirical research are explored in an application to the spreads on US investment grade corporate debt.

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Section: Parameter Estimationsupporting

confidence: 79%

Section: Parameter Estimationmentioning

confidence: 99%

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Hybrid stochastic local unit roots

Lieberman

Phillips

2020

Journal of Econometrics

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“…25–27 The performance/reliability distribution pathway can be linear, logarithmic, exponential, stochastic or a combination of various distributions. Research done in the literature 28–32 supports the consistency of time-varying coefficient stochastic models in forecasting and predictive modelling.…”

Section: Methodsmentioning

confidence: 89%

Maintenance optimization for marine mechanical systems

Kimera

Nangolo

2019

Proceedings of the Institution of Mechanical Engineers, Part M:

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This article proposes a stochastic technique for determining the optimal maintenance policy for marine mechanical systems. The optimal maintenance policy output includes the average maintenance cost rate, maintenance interval and the performance thresholds for the three marine mechanical system classifications. The purpose of this study is to optimize maintenance, maintenance interval and performance thresholds based on maintenance and reliability data of the marine mechanical systems. Performance threshold and maintenance interval are used as the decision variables to determine the optimal maintenance policy. A stochastic model based on probability analysis is developed to trigger the maintenance action for mechanical systems. The model is later coded in MATLAB. Maintenance and failure data for a marine vehicle were statistically fitted using ReliaSoft, from which a three-parameter Weibull distribution best fitted all the mechanical system classifications. Model inputs were based on both the maintenance data and expert knowledge of the maintenance crew. Based on a 20-year marine vehicle life span, the optimal maintenance costs for plant and machinery are relatively the same. The model predicted annual total maintenance cost of US$183,029.24 is 11.11% more than the maintenance cost derived from experts’ threshold of US$164,726. Marine vehicle machinery presents a higher maintenance interval of 3.23 years compared to 2.92 years for marine vehicle plants. It was observed that for the performance thresholds greater than 84.54%, there is an insignificant difference between the plant and machinery maintenance costs. Sensitivity analysis results suggest there is little justification that changing maintenance costs will have an impact on the performance threshold [Formula: see text] and maintenance interval [Formula: see text]. A maintenance interval of 3 years results in a lower total annual maintenance cost deviation of 2.66% from the optimal total annual maintenance cost.

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“…Second, compared to active literature, which relies on a recursive method implemented in a mildly explosive discrete time model, we employ a novel econometric approach for detecting bubbles. Inspired by the work of Tao et al [25], the present paper estimates a continuoustime model, a model that allows for a random persistence parameter and its endogeneity. e advantages of extending a discrete time model to continuous time are threefold.…”

Section: Introductionmentioning

confidence: 99%

Booms and Busts in the Oil Market: Identifying Speculative Bubbles Using a Continuous‐Time Dynamic System

Zhang

2021

Complexity

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The sharp changes in oil prices since 2004 featured a nonlinear data-generating mechanism which displayed bubble-like behavior. A popular view is that such a salient pattern cannot be explained by shifts in economic fundamentals, but was driven by speculative bubbles as a consequence of the increased financialization of oil future markets. Testing this hypothesis, however, is challenging since the fundamental component of the oil price is unobservable. This paper attempts to isolate the contribution of speculative bubbles and fundamentals to the evolution of oil prices by providing a stylized model of commodity pricing. Motivated by our theoretical model, we adopt a continuous-time model with a random and time-varying persistence parameter to empirically investigate the presence of speculative bubbles in daily oil future prices over the period April 1983 to June 2020. We do not find any evidence in favor of speculative bubbles, although we indeed find that oil prices exhibit episodes of unstable behavior after 2004.

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Random coefficient continuous systems: Testing for extreme sample path behavior

Cited by 17 publications

References 54 publications

Hybrid stochastic local unit roots

Hybrid stochastic local unit roots

Maintenance optimization for marine mechanical systems

Booms and Busts in the Oil Market: Identifying Speculative Bubbles Using a Continuous‐Time Dynamic System

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