By Jean-Marie Dufour, Baldev Raj
This publication includes 11 articles which offer empirical purposes in addition to theoretical extensions of a few of the main fascinating contemporary advancements in time-series econometrics. The papers are grouped round 3 vast topics: (I) the modeling of multivariate instances sequence; (II) the research of structural switch; (III) seasonality and fractional integration. due to the fact those subject matters are heavily inter-related, numerous different subject matters coated also are worthy stressing: vector autoregressive (VAR) types, cointegration and error-correction types, nonparametric tools in time sequence, and fractionally built-in types. Researchers and scholars drawn to macroeconomic and empirical finance will locate during this assortment a remarkably consultant pattern of contemporary paintings during this area.
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Sample text
It should be pointed out that Baumol does not claim any such correspondence. In fact, he gives a long list of reasons why this cannot be the case. VAR Modelling and Haavelmo's Probability Approach to Macroeconomic Modelling 43 Examples of violation of this principle are easy to find in macroeconomic applications. The many unit-root tests of GDP time-series to discriminate between different real growth theories can be mentioned in this respect. Though data collected by passive observation do not generally qualify for the purpose of testing "deep" theoretical models, most empirical macroeconomic models are based on such data.
Two explanations why empirical results are often considered unpersuasive are given here. i) Not Enough Care is Taken to Ensure that the Specification of Empirical Models Mimics the General Characteristics of the Data In such cases the validity of the empirical outcome cannot be assessed and the test results remain unpersuasive. Therefore, the additional concept of a statistically well-defined model that accounts for all basic features of the data is required. Valid test procedures can then be derived from the analysis of the likelihood function and inferences about the specified hypotheses are valid.
Haavelmo suggests how they should be measured in an ideal situation, and introduces in this context the concepts of true and theoretical variables as opposed to observable variables. According to Haavelmo, p. 5: We may express the difference [between the "true" and the theoretical variables] by saying that the "true" variables (or time functions) represent our ideal as to accurate measurements of reality "as it is in fact" while the variables defined in theory are the true measurements that we should make if reality were actually in accordance with our theoretical model.