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A new approach for detecting gradual changes in non-stationary time series with seasonal effects
This paper proposes a new method of detecting the gradual changes of time series when the changes in time series are mixed with seasonality. The key...
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Markov switching quantile regression models with time-varying transition probabilities
Markov switching models are widely used in the time series field for their ability to describe the impact of latent regimes on the behaviour of...
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Applications of Reduced-Rank Regression in Financial Economics
In previous chapters, we have developed reduced-rank regression models of various forms, which have wide applications in a variety of contexts in the...
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Tail dependence and smoothness of time series
The risk of catastrophes is related to the possibility of occurring extreme values. Several statistical methodologies have been developed in order to...
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Financial Compliance in Clinical Trials
Financial compliance considerations are an important aspect of the design and funding of clinical trials. Such research often involves a mixture of...
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Importance-Performance Map Analysis of Capital Structure Using PLS-SEM: Evidence from Non-financial Sector
This study aims to identify the most important determinant that affects the capital structure decision of non-financial firms listed on the Pakistan...
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Methods for Multivariate Time Series
Most procedures for univariate time series from previous chapters can be generalized for multivariate time series, where instead of scalar values yt...
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A Dynamic Model for Ordinal Time Series: An Application to Consumers’ Perceptions of Inflation
This article discusses an innovative model for time series ordinal data, which develops the well-established CUB model to allow for time-varying...
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Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions
I discuss recent research advances in Bayesian state-space modeling of multivariate time series. A main focus is on the “decouple/recouple” concept...
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Statistically validated coeherence and intensity in temporal networks of information flows
We propose a method for characterizing the local structure of weighted multivariate time series networks. We draw intensity and coherence of network...
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Financial Contagion During COVID-19 Crisis: Intraday Analysis Using VAR-VECM Models
In this chapter, we investigate the contagion effects between equity markets over the period of the COVID-19 crisis. For that purpose, we use an...
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Finite-time expected present value of operating costs until ruin in a bivariate risk model under periodic observation
In this paper, we examine a bivariate insurance risk model that incorporates two distinct business lines. The model encompasses both independent...
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Doubly time-dependent Hawkes process and applications in failure sequence analysis
Since the Hawkes process is proposed in 1971, it has become increasingly widely applied in the field of event sequence analysis, such as social...
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A Time-Lagged Penalized Regression Model and Applications to Economic Modeling
In the arena of high-dimensional data analysis, variable selection has emerged as a significant subject. The simultaneous accomplishment of variable...
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Linear models with time-varying parameters: a comparison of different approaches
Estimation of linear models with time-varying parameters can be accomplished in a variety of ways, each making different assumptions, with varying...
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Volatility forecasting using deep recurrent neural networks as GARCH models
Estimating and predicting volatility in time series is of great importance in different areas where it is required to quantify risk based on...
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Estimation of Extreme Values for Financial Risk Assessment
In 1991 the Norwegian government decided that the power market should be deregulated allowing for power trading. Following this, the Nord Pool market...
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State Space Models of Time Series
Kalman filter presents a theoretical background for various recursive methods in (linear) systems, particularly in (multivariate) time series models....
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Symbolic interval-valued data analysis for time series based on auto-interval-regressive models
This study considers interval-valued time series data. To characterize such data, we propose an auto-interval-regressive (AIR) model using the order...
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Integer-valued Bilinear Model with Dependent Counting Series
The present work proposes a new stationary integer-valued bilinear time series model with dependent counting series. The model will enable one to...
