The paper investigates a change point estimation problem in the context of high dimensional
Markov random-field models. Change points represent a key feature in many dynamically …

Gaussian vector autoregressive (VAR) processes have been extensively studied in the
literature. However, Gaussian assumptions are stringent for heavy-tailed time series that …

Multivariate time series are of interest in many fields including economics and environment.
The dynamical processes occurring in these domains often exhibit a mixture of different …

The vector autoregression (VAR), has long proven to be an effective method for modeling
the joint dynamics of macroeconomic time series as well as forecasting. One of the major …

The identification of causal effects between two groups of time series has been an important
topic in a wide range of applications such as economics, engineering, medicine …

This paper provides an empirical comparison of various selection and penalized regression
approaches for forecasting with vector autoregressive systems. In particular, we investigate …

This paper presents a general theoretical framework of penalized quasi-maximum likelihood
(PQML) estimation in stationary multiple time series models when the number of parameters …

Time series forecasting is a research domain that has its origin in the field of statistics and
econometrics. Since there are many prediction problems involving a time component, the …

Formålet med denne masteravhandlingen er å studere norsk finanspolitikk og dens effekt på
et utvalg makroøkonomiske størrelser i perioden 1980-2018. Dette gjøres ved å estimere en …

The practice of macroeconomic forecasting was spearheaded by Klein and Goldberger
(1955), whose eponymous simultaneous equation system jointly forecasted the behavior of …