Volume 9, Issue 3

Business cycles in different industries have a tendency to synchronize with one another in what appears to be a national business cycle. Using simulation and time series techniques in the time and frequency domain, we offer econometric support for the industrial sector mode-locking hypothesis, extending recent work by Selover, Jensen and Kroll (2003). In addition, we propose an economic motivation of the underlying nonlinear model.

In this paper we give an account of the approach to nonlinear econometric modelling proposed by Hamilton (2001) and briefly describe some of the methods of nonlinear optimization that may be used in the Gauss computer program provided by Hamilton for the implementation of his methodology. The performance of this program is investigated using data relating to Hamilton's example concerning the US Phillips curve, two versions of the Gauss software, and a range of alternative numerical optimization options and values for the Gauss parameter _oprteps. The impact of changes in initial parameter estimates and the use of pairs of optimization algorithms are also briefly examined. Finally, the effects of changes in the sample data on the results produced by Hamilton's procedure are explored. The results presented suggest some clear conclusions, which will be of value to those contemplating working with Hamilton's new method.

The paper by Bond, Harrison, and O'Brien illustrates the role that convergence criteria, search algorithms, and starting values can play in influencing the success of numerical optimization. One aspect contributing to the importance of these choices in their results appears to be a multivariate generalization of the "pile-up" phenomenon for maximum likelihood estimation of moving average processes. Their results suggest advantages in some settings of using either Bayesian methods or an alternative specification of the random field.

We describe a new and flexible framework for modeling school effects. Like previous work in this area, we introduce an empirical model that evaluates school performance on the basis of student level test-score gains. Unlike previous work, however, we introduce a flexible model that relates follow-up student test scores to baseline student test scores and explore for possible nonlinearities in these relationships.Using data from High School and Beyond (HSB) and adapting the methodology described in Koop and Poirier (2004a), we test and reject the use of specifications that have been frequently used in research and as a basis for policy. We find that nonlinearities are important in the relationship between intake and follow-up achievement, that rankings of schools are sensitive to the model employed, and importantly, that commonly used specifications can give different and potentially misleading assessments of school performance. When estimating our preferred semiparametric specification, we find small but ``significant'' impacts of some school quality proxies (such as district-level expenditure per pupil) in the production of student achievement.

This paper analyzes possible cointegration relations among the sub-indexes of the Istanbul Stock Exchange series - services sector, industry sector and financial sector - for the period from February 1, 1997 to September 24, 2003. The data is analyzed by using various methods initiated by Engle and Granger (1987), Johansen (1988) and Akdi (1995). The basic finding of this study is that none of these methods suggest the presence of cointegrating relationships among these indexes.