BACKGROUND In recent years, electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time. To model dynamic dependence structures and feedback mechanisms between symptom-relevant variables, a multivariate time series method has to be applied. METHODS We propose to analyse the temporal interrelationships amon...

Network modeling of high-dimensional time series in presence of unobserved latent variables is an important problem in macroeconomics and finance. In macroeconomic policy making and forecasting, it is often impossible to observe and incorporate all the relevant series in the analysis. Failure to include these variables often results in spurious connectivity among the observed time series in str...

one of the most important economic factors is potential output. in macroeconomic models and structural studies, the estimation of potential output is necessary for projections and analyzing policy performances. there exist several methods for estimating potential output. meanwhile, its estimation is a difficult and complicated matter. empirical studies and researches show that using various tec...

We investigate the dynamic relationship between global oil prices, stock market, and gas (FTSE-OG) returns in UK through a structural vector autoregressive (VAR) framework during COVID-19 pandemic. The VAR results suggest that impact of shocks related to price on FTSE-OG index becomes less important loses its explanatory power However, market increase their variations returns.

In this paper, we develop a Bayesian approach to counterfactual analysis of structural change. Contrary to previous analysis based on classical point estimates, this approach provides a straightforward measure of estimation uncertainty for the counterfactual quantity of interest. We apply the Bayesian counterfactual analysis to examine the sources of the volatility reduction in U.S. real GDP gr...

F ollowing seminal work by Sims (1980a, 1980b), the economics profession has become increasingly concerned with studying sources of economic fluctuations. Sims’s use of vector autoregressions (VARs) made it possible to address both the relative importance and the dynamic effect of various shocks on macroeconomic variables. This type of empirical analysis has had at least two important consequen...

Relative to normal tissues, tumor microcirculation exhibits high structural and functional heterogeneity leading to hypoxic regions and impairing treatment efficacy. Here, computational simulations of blood vessel structural adaptation are used to explore the hypothesis that abnormal adaptive responses to local hemodynamic and metabolic stimuli contribute to aberrant morphological and hemodynam...

This paper explores the relationships behind previous findings that Federal Reserve interest rate policies have a larger impact on black unemployment compared to white unemployment. Most earlier studies employ a VAR methodology, which does not, however, permit a structural analysis of key relationships. This paper accounts for the endogeneity of unemployment, inflation, and the federal funds ra...

This paper contributes to the econometric literature on structural breaks by proposing a test for parameter stability in VAR models at a particular frequency ω, where ω ∈ [0, π]. When a dynamic model is affected by a structural break, the new tests allow for detecting which frequencies of the data are responsible for parameter instability. If the model is locally stable at the frequencies of in...

This paper investigates the sources of the substantial decrease in output growth volatility in the mid-1980s by identifying which of the structural parameters in a representative New Keynesian and Structural VAR models changed. Overturning conventional wisdom, we show that the Great Moderation was not only due to changes in shocks volatilities but also to changes in monetary policy parameters a...