Recently, the data analysts pay more attention to the assessment of rater agreement, especially in areas of medical sciences. In this context, the statistical indices such as kappa and weighted kappa are the most common choices. These indices are simple to calculate and interpret, although, they...

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...

Economic theories are often encoded in equilibrium models that cannot be directly estimated because they lack features that, while inessential to the theoretical mechanism that is central to the specific theory, would be essential to fit the data well. We propose an econometric approach that...