[PDF] Top 20 Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
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Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
... Macroeconomic data sets typically involve monthly, quarterly or annual ob- servations and, thus are only of moderate size. But VARs have a great number of parameters to estimate. This is particularly true if the number ... See full document
66
An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series
... Two empirical applications are ...The multivariate hierarchical structural break models produce superior density forecasts for several forecast horizons compared to a number of popular benchmark ...data ... See full document
42
Dynamic Bayesian models for vector time series analysis & forecasting
... Finally , in section 8.4 of this chapter , one of the most popular methods in the econometric literature about multivariate time series modelling and forecasting , the so called BVAR Ba[r] ... See full document
191
ESSAYS ON EMPIRICAL ASSET PRICING USING BAYESIAN METHODS
... Alternative risk measures could be obtained by generalising the assumption of the CAPM, i.e. the unrealistic mean-variance assumption based on normality or quadratic utility function. Assume that asset returns are ... See full document
187
Multivariate Bayesian Structural Time Series Model
... The analysis of “Big Data” through the application of a new breed of analytical tools for manipulating and analyzing vast caches of data, is one of the cutting edge new areas. As a byproduct of the extensive use of the ... See full document
33
Essays in Empirical Macroeconomics
... a Bayesian time-varying parameter structural VAR with stochastic volatility following the works of Primiceri (2005), Canova and Gambetti (2005), Gambetti, Pappa and Canova (2006) and Benati and Mum- taz ... See full document
126
An Empirical Comparison of Distance Measures for Multivariate Time Series Clustering
... the multivariate time series clustering ...in multivariate time series data clustering context that has drawn the most attention from data mining ...existing methods. On ... See full document
13
Bayesian methods for empirical macroeconomics with big data
... when time-variation in parameters is allowed ...of methods, some discussed in this paper, which have been found to work well in regressions where the number of explanatory variables is large relative to (or ... See full document
24
Bayesian Mixture Models with Applications in Macroeconomics
... macroeconomic time series [Stock and Watson, 1996; Cogley and Sargent, 2002; Kim et ...for time-variation in both conditional mean coefficients and ...study: time-varying parameters models ... See full document
115
Essays on empirical macroeconomics
... forecasting methods, the most familiar are the standard time series processes developed by Box and Jenkins and their posterior refinements, including multivariate time series ... See full document
109
Discovery of Temporal Association Rules in Multivariate Time Series
... Considering that similar patterns may reflect the same fact, for example the two patterns: and , both indicate that the data of corresponding time series first goes up and then goes down. Thus, DTW [12] ... See full document
7
Locally Adaptive Factor Processes for Multivariate Time Series
... a time-varying covariance matrix ...single time-constant smoothing parameter 0 < < 1, with extensions to accommodate locally varying smoothness not straightforward due to the need to maintain positive ... See full document
30
A similarity of multivariate time series in stocks network analysis
... This is because, in daily stock market activity, the price information for each stock is recorded in opening, highest, lowest, and closing prices. Thus, stock is a multivariate time series of those ... See full document
36
Goodness-of-Fit Tests for Copulas of Multivariate Time Series
... In this section, we investigate the asymptotic behavior of four well-known rank-based dependence measures constructed from the residuals: Kendall’s tau, Spearman’s rho, van der Waerden and Blomqvist’s coefficients. The ... See full document
23
Air Pollution Prediction System for Smart City using Data Mining Technique: A Survey
... using Multivariate Multistep Time series prediction using Random Forest ...A time series is a series of data points indexed (or listed or graphed) in time ...a time ... See full document
6
Taylor-Series Expansion Methods for Multivariate Hammerstein Integral Equations
... Abstract—A new Taylor-series method which was originally developed for the solution of one-dimensional integral equations is extended to solve multivariate nonlinear integral equations. In this paper, a new ... See full document
5
Bayesian inference for nonlinear structural time series models
... Table 5 also shows the variance of each filter’s loglikelihood estimates, evaluated by taking 75 repeated loglikelihood estimates at the true value of θ. These results are broadly consistent with the asymptotic analysis ... See full document
30
Bayesian Inference about Some Geometric Aspects of Nonparametric Functions.
... So far the study of filaments is limited only to densities only using the kernel approach. Our study supplements the current literature in two ways. First, we provide a Bayesian approach to the filament estimation ... See full document
125
Self labeling techniques for semi supervised time series classification: an empirical study
... of time series, which involve self-labeled ...a time series context so far, to the best of our ...to time series classification tasks, the use of different learning approaches as ... See full document
37
Empirical essays in international macroeconomics
... the time dimension of the panel is fixed and the cross-section dimension tends to infinity, this correlation produces a bias in the coefficient of the lagged dependent ...Schwarz Bayesian Information ... See full document
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