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Theory often specifies a particular cointegrating vector among integrated variables, and testing for a unit root in the known cointegrating vector is often required. Although it is common to simply [...]

This article describes a new Monte Carlo algorithm, dynamically weighted importance sampling (DWIS), for simulation and optimization. In DWIS, the state of the Markov chain is augmented to a population. At each iteration, the population is subject to two move steps, dynamic weighting and population control. These steps ensure that DWIS can move across energy barriers like dynamic weighting, but with the weights well controlled and with a finite expectation. The estimates can converge much faster than they can with dynamic weighting. A generalized theory for importance sampling is introduced to justify the new algorithm. Numerical examples are given to show that dynamically weighted importance sampling can perform significantly better than the Metropolis--Hastings algorithm and dynamic weighting in some situations. KEY WORDS: Dynamic weighting; Markov chain Monte Carlo; Metropolis--Hastings algorithm; Sequential importance sampling.

We consider the implications of an alternative to the classical measurement-error model, in which the observed, mismeasured data are optimal predictions of the true values, given some information set. In [...]

We strongly reject the full-insurance hypothesis, using testing variables that are not decision variables for the households under investigation. We find that households are not insured against changes in the [...]

Philip Hans FRANSES. Cambridge, U.K.: Cambridge University Press, 1998. ISBN 0-521-58404-3. x + 280 pp. $69.95. The main purposes of analyzing time series are to test economic hypotheses or generate [...]

According to Ragnar Frisch (1933), econometrics involves the mutual penetration of quantitative economic theory and statistical observation. Today, econometric research rarely has all the ingredients spelled out in Frisch's definition. [...]