Zobrazeno 1 - 10
of 3 734
pro vyhledávání: '"Chan, A P C"'
Autor:
Chan, Joshua C. C., Qi, Yaling
We consider Bayesian tensor vector autoregressions (TVARs) in which the VAR coefficients are arranged as a three-dimensional array or tensor, and this coefficient tensor is parameterized using a low-rank CP decomposition. We develop a family of TVARs
Externí odkaz:
http://arxiv.org/abs/2409.16132
Conditional forecasts, i.e. projections of a set of variables of interest on the future paths of some other variables, are used routinely by empirical macroeconomists in a number of applied settings. In spite of this, the existing algorithms used to
Externí odkaz:
http://arxiv.org/abs/2407.02262
Stroke is one of the leading causes of disability worldwide. The efficacy of recovery is determined by a variety of factors, including patient adherence to rehabilitation programs. One way to increase patient adherence to their rehabilitation program
Externí odkaz:
http://arxiv.org/abs/2402.11590
We develop an efficient sampling approach for handling complex missing data patterns and a large number of missing observations in conditionally Gaussian state space models. Two important examples are dynamic factor models with unbalanced datasets an
Externí odkaz:
http://arxiv.org/abs/2302.03172
Autor:
Chan, Joshua C. C.
Large Bayesian vector autoregressions with various forms of stochastic volatility have become increasingly popular in empirical macroeconomics. One main difficulty for practitioners is to choose the most suitable stochastic volatility specification f
Externí odkaz:
http://arxiv.org/abs/2208.13255
Autor:
Chan, Joshua C. C., Yu, Xuewen
We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of large Bayesian VARs. In contrast to existing approaches that are based on local approximations, the new proposal provides a global
Externí odkaz:
http://arxiv.org/abs/2206.08438
Autor:
Ikeda, Ryota, Tadaki, Ken-ichi, Iono, Daisuke, Kodama, Tadayuki, Chan, Jeffrey C. C., Hatsukade, Bunyo, Hayashi, Masao, Izumi, Takuma, Kohno, Kotaro, Koyama, Yusei, Shimakawa, Rhythm, Suzuki, Tomoko L., Tamura, Yoichi, Tanaka, Ichi
We present new Atacama Large Millimeter/submillimeter Array (ALMA) results obtained from spatially resolved CO $J$=2-1 line ($0.4''$ resolution) and 870 $\mu$m continuum ($0.2''$ resolution) observations of cluster galaxies in XMMXCS J2215.9-1738 at
Externí odkaz:
http://arxiv.org/abs/2205.05731
Autor:
Chan, Joshua C. C.
Time-varying parameter VARs with stochastic volatility are routinely used for structural analysis and forecasting in settings involving a few endogenous variables. Applying these models to high-dimensional datasets has proved to be challenging due to
Externí odkaz:
http://arxiv.org/abs/2201.07303
State-space mixed-frequency vector autoregressions are now widely used for nowcasting. Despite their popularity, estimating such models can be computationally intensive, especially for large systems with stochastic volatility. To tackle the computati
Externí odkaz:
http://arxiv.org/abs/2112.11315
Many popular specifications for Vector Autoregressions (VARs) with multivariate stochastic volatility are not invariant to the way the variables are ordered due to the use of a Cholesky decomposition for the error covariance matrix. We show that the
Externí odkaz:
http://arxiv.org/abs/2111.07225