This paper develops default priors for Bayesian analysis that reproduce familiar frequentist and Bayesian analyses for models that are exponential or location. For the vector parameter case there is an information adjustment that avoids the Bayesian marginalization paradoxes and properly targets the prior on the parameter of interest thus adjusting for any complicating nonlinearity the details ...

This study characterises the exchange rate risk premium in context of a small open economy with controlled floating regime. The empirical analysis applies time-varying coefficients Bayesian structural vector autoregressive (TVC-BSVAR) model on data from Vietnamese over sample period February 2012 to 2019. evidence shows that varies time, and increases inflation foreign direct investment capital...

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model...

This paper focuses on different methods of estimation and forecasting in first-order integer-valued autoregressive processes with Poisson-Lindley (PLINAR(1)) marginal distribution. For this purpose, the parameters of the model are estimated using Whittle, maximum empirical likelihood and sieve bootstrap methods. Moreover, Bayesian and sieve bootstrap forecasting methods are proposed and predict...

The United States Dollar (USD) replacement of the sterling as dominant currency is not only result "invisible hand," but also "visible hand." This study analyzes effect monetary policy in (U.S.) on international share USD from 1914 to 1945 using Bayesian technique, estimate time-varying parameter vector autoregressive (TVP-VAR) model. posits two main findings. First, time-point impulse response...

Forecasting of the Renewable Energy plays a major role in optimal decision formula for government and industrial sector in Bangladesh. This research is based on time series modeling with special application to solar energy data for Dhaka city. Three families of time series models namely, the autoregressive integrated moving average models, Holt’s linear exponential smoothing, and the autoregres...

Inferring Dynamic Bayesian Networks (DBNs) from multivariate time series data is a key step towards the understanding of complex systems as it reveals important dependency relationship underlying such systems. Most of the traditional approaches assume a “static” DBN. Yet in many relevant applications, such as those arising in biology and social sciences, the dependency structures may vary over ...

Monitoring economic conditions in real time, or nowcasting, and Big Data analytics share some challenges, sometimes called the three “Vs”. Indeed, nowcasting is characterized by use of a large number time series (Volume), complexity data covering various sectors economy, with different frequencies precision asynchronous release dates (Variety), need to incorporate new information continuously t...