Hidden semi-Markov-switching quantile regression for time series

This chapter discusses how the effects of structural labour market reforms depend on whether the economy is in expansion or recession. Based on an empirical time series model with Markov switching that draws on search and matching theory, we propose a novel identification of reform outcomes and distinguish the effects of structural reforms that increase the flexibility of the labour market in distinct phases of the business cycle. We find in applications to Germany and Spain that reforms which are implemented in recessions have weaker expansionary effects in the short run. For policymakers, these results emphasize the costs of introducing labour market reforms in recessions.

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Assessing marine operations with a Markov-switching autoregressive metocean model

Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment ◽

10.1177/1475090220916084 ◽

2020 ◽

Vol 234 (4) ◽

pp. 785-802

Author(s):

Jack Paterson ◽

Philipp R Thies ◽

Roman Sueur ◽

Jérôme Lonchampt ◽

Federico D’Amico

Keyword(s):

Time Series ◽

Wind Speed ◽

Wave Height ◽

Selection Process ◽

Markov Switching ◽

Transfer Task ◽

Weather Conditions ◽

Weather Model ◽

Wide Range ◽

Modelling Methodology

This article presents a metocean modelling methodology using a Markov-switching autoregressive model to produce stochastic wind speed and wave height time series, for inclusion in marine risk planning software tools. By generating a large number of stochastic weather series that resemble the variability in key metocean parameters, probabilistic outcomes can be obtained to predict the occurrence of weather windows, delays and subsequent operational durations for specific tasks or offshore construction phases. To cope with the variation in the offshore weather conditions at each project, it is vital that a stochastic weather model is adaptable to seasonal and inter-monthly fluctuations at each site, generating realistic time series to support weather risk assessments. A model selection process is presented for both weather parameters across three locations, and a personnel transfer task is used to contextualise a realistic weather window analysis. Summarising plots demonstrate the validity of the presented methodology and that a small extension improves the adaptability of the approach for sites with strong correlations between wind speed and wave height. It is concluded that the overall methodology can produce suitable wind speed and wave time series for the assessment of marine operations, yet it is recommended that the methodology is applied to other sites and operations, to determine the method’s adaptability to a wide range of offshore locations.

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High-quantile regression for tail-dependent time series

Biometrika ◽

10.1093/biomet/asaa046 ◽

2020 ◽

Author(s):

Ting Zhang

Keyword(s):

Time Series ◽

Quantile Regression ◽

Limit Theorems ◽

Tail Dependence ◽

Serial Dependence ◽

Response Distribution ◽

Tail Region ◽

Strongly Mixing ◽

Regression Estimators ◽

High Quantile

Summary Quantile regression is a popular and powerful method for studying the effect of regressors on quantiles of a response distribution. However, existing results on quantile regression were mainly developed for cases in which the quantile level is fixed, and the data are often assumed to be independent. Motivated by recent applications, we consider the situation where (i) the quantile level is not fixed and can grow with the sample size to capture the tail phenomena, and (ii) the data are no longer independent, but collected as a time series that can exhibit serial dependence in both tail and non-tail regions. To study the asymptotic theory for high-quantile regression estimators in the time series setting, we introduce a tail adversarial stability condition, which had not previously been described, and show that it leads to an interpretable and convenient framework for obtaining limit theorems for time series that exhibit serial dependence in the tail region, but are not necessarily strongly mixing. Numerical experiments are conducted to illustrate the effect of tail dependence on high-quantile regression estimators, for which simply ignoring the tail dependence may yield misleading $p$-values.

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