[PDF] Top 20 Bayesian compressed vector autoregressions

... Vector autoregressions (VARs) have been an important tool in macroeconomics since the seminal work of Sims ...flexible Bayesian priors that result in shrinkage of high-dimensional parameter spaces ... See full document

... According to the theory of probability knowledge, the mathematical expectation of h Africa zero position number is NP [11-13]. Channel vector h in the zero position number and nearly equal to the probability of NP ... See full document

... There is a vast literature that speci…es Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large dimensions. In this paper I argue that many of these priors are not appropriate ... See full document

... proposed Bayesian estimator is that it decides (based on information in the likelihood) which coefficients are time-varying or not, as well as which coefficients are zero or not; see Belmonte, Koop and Korobilis ... See full document

... Aside from theoretical considerations, estimation remains the main challenge for time-varying VAR models. Except for a marginal number of contributions building on frequentist methods (Gorgi et al. (2017)), the ... See full document

... Different strategies have been adopted to overcome this inefficiency issue. Carriero et al. (2016) propose to estimate their large Bayesian VAR model equation by equation rather than jointly. Doing so considerably ... See full document

... signal vector in a way similar to the decoding of low-density parity check (LDPC) ...Other Bayesian CS algorithms also have been developed in [3, 4, 19, ...A Bayesian approach for Sparse Component ... See full document

... one-bit compressed sensing with sensing matrix ...a Bayesian hypothesis test is used to detect the active samples of sparse ...sparse vector, similar to [16], we utilize an ML ... See full document

... information, Bayesian VAR inference can be thought of as adopting ‘non-informative’ (or ‘diffuse’ or ‘flat’) priors, that express complete ignorance about the model parameters, in the light of the sample evidence ... See full document

... "A Statistical Approach to Economic Forecasting", Journal of Business and Economic Statistics, Vol... "Forecasting with Bayesian Vector Autoregressions — Five Years of Experience", Journ[r] ... See full document

... These reasons justify the current trend in applied modeling with large datasets. The modern econometrician has tools adequate enough to successfully extract information from hundreds of predictor variables and compute ... See full document

... The Bayesian inference offers the potential to exactly estimate the ori- ginal signal or effectively reduce the radiation dose by compress ...the Bayesian method can provide a variety of probability ... See full document

... of Bayesian vector autoregressive (VAR) ...The Bayesian implementation of these models allows for rich, flexible modeling and improved forecast performance in high ...the Bayesian priors can ... See full document

... Hence, Thm. 2 of Doukhan et al. (2012) applies and shows that the process is is geometrically β-mixing process. This results has been proved by Neumann (2011) for the case L = 1 and and some more recent related work has ... See full document

... We introduce into Bayesian decipherment a base distribution derived from similari- ties of word embeddings. We use Dirich- let multinomial regression (Mimno and McCallum, 2012) to learn a mapping be- tween ... See full document

... plicated empirical issue than in conventional causal autoregressions. Which model is selected is also of great economic interest, as it tells us to what extent the economic variable depends on its past and ... See full document

... Econometric modelling and statistical inference are considerably complicated by the possibility of correlation across data data recorded at different locations in space. A major branch of the spatial econometrics ... See full document

... In this chapter we deal with testing for multiple structural breaks in a vector error correction model as a problem of model selection and approximate the Bayes factors by Schwarz's Baye[r] ... See full document

... weight vector [16]. In these techniques, robustness to steering vector uncertainty is increased at the expense of a reduction in noise and interfer- ence ...steering vector is less than a certain ... See full document

... desirable. Compressed sensing is offers suitable method for estimating short sequence type of sparse with unknown statistics identified in [11, 12, 23, ...25], Bayesian estimation scores over other ... See full document