Titlebook: Macroeconometrics and Time Series Analysis; Steven N. Durlauf,Lawrence E. Blume Book 2010 Palgrave Macmillan, a division of Macmillan Publ

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Bayesian time series analysis,eciation of the advantages that Bayesian inference entails. In particular, it provides us with a formal way to incorporate the prior information we often possess before seeing the data, it fits perfectly with sequential learning and decision making, and it directly leads to exact small sample result

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Central limit theorems,sses in a large number of experiments. Later on, Laplace generalized his results, but it took 20th century mathematics to give an exact and complete description of this subject. So let me now describe the modern approach. We assume that for each . we have given a sequence ..,…. . of random variables

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Forecasting,cisions are so important as a basis for these fields, a great deal of attention has been paid to the question of how best to forecast variables and occurrences of interest. There are several distinct types of forecasting situations, including event timing, event outcome, and time-series forecasts. E

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Functional central limit theorems,s, however, a more detailed analysis is necessary. When testing for structural constancy in models, we might be interested in the temporal evolution of our sums. So for random variables . we are interested in analysing the behaviour of as a function of . for .. It is convenient to normalize the time