State-Space Modelling

State-space models deal with dynamic time series problems that involve unobserved variables or parameters that describe the evolution in the state of the underlying system. This area of mathematical statistics is relevant to many areas of econometric research, as we often encounter unobserved variables that may be included in a model: output gaps, business cycles, expectational values of certain variables, permanent income streams, ex ante real interest rates, reservation wages, etc. In addition, this framework is also relevant to those who are interested in financial research, as they are used in the application of the many variants of stochastic volatility models. The basic approach to state-space modelling assumes that the development over time of a system under investigation is determined by an unobserved series of vectors, { α 1 , … , α n } { 𝛼 1 , … , 𝛼 𝑛 } , that are associated an observed series of observations, { y 1 , … , y n } { 𝑦 1 , … , 𝑦 𝑛 } . The relationship bet

State-space models deal with dynamic time series problems that involve unobserved variables or parameters that describe the evolution in the state of the underlying system. This area of mathematical statistics is relevant to many areas of econometric research, as we often encounter unobserved variables that may be included in a model: output gaps, business cycles, expectational values of certain variables, permanent income streams, ex ante real interest rates, reservation wages, etc. In addition, this framework is also relevant to those who are interested in financial research, as they are use