Governments, multinational companies, and researchers today collect unprecedented amounts of data on human feelings. These data provide information on citizens' happiness, levels of customer satisfaction, employees' satisfaction, mental stress, societal trust, and other important variables. Yet...

Governments, multinational companies, and researchers today collect unprecedented amounts of data on human feelings. These data provide information on citizens' happiness, levels of customer satisfaction, employees' satisfaction, mental stress, societal trust, and other important variables. Yet...

We use available methods for testing macro models to evaluate a model of China over the period from Deng Xiaoping's reforms up until the crisis period. Bayesian ranking methods are heavily influenced by controversial priors on the degree of price/wage rigidity. When the overall models are tested...

Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC...

A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....

This paper describes a package which uses MATLAB functions and routines to estimate VARs, local projections and other models with classical or Bayesian methods. The toolbox allows a researcher to conduct inference under various prior assumptions on the parameters, to produce point and density...