Approximate Bayesian computation in state space models

经济和金融数据经常表现出动态模式，受到与经济主体行为或体制和技术变革有关的未观察到的过程的驱动。深入了解这种“潜在的”过程对于理解经济以及对其未来表现做出准确、及时的预测至关重要。利用贝叶斯方法，提出了一种新的基于仿真的统计方法来分析潜在变量模型。重点是发展适用于广泛的经验相关模型的相对简单的技术，以期改善非专业人员使用这种强有力的统计分析形式的机会。

Economic and financial data frequently exhibit dynamic patterns, driven by unobserved processes that relate to the behaviour of economic agents, or to institutional and technological change. To gain insight into such 'latent' processes is of paramount importance in terms of both understanding the economy and producing accurate, readily up-dated, forecasts of its future performance. Using a Bayesian approach, new simulation-based statistical methods for analysing latent variable models are proposed. Emphasis is given to the development of relatively simple techniques that are applicable to a wide range of empirically relevant models, with a view to improving the access of non-specialists to this powerful form of statistical analysis.