A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an empirical example relating to the uncertainty of the inflation rate is presented.

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In Belgium, welfare agencies receive a subsidy to employ welfare recipients for a period sufficiently long to entitle them to unemployment benefits. This work experience program is called Social Employment (SE). We investigate the effect of SE on the exit rate from welfare. We propose a grouping/IV estimator of the SE effect that eliminates selection bias. The estimator is consistent, even if the selection into SE depends on the average unobserved characteristics of welfare recipients in a region and in a welfare duration interval. The empirical analysis suggests that there is creaming in the selection process. Without correction for selectivity we find that SE reduces welfare dependence, but after correction this conclusion is reversed. These results are consistent with the adverse incentives faced by the welfare agencies.

A very general test statistic for contrasting two nested or nonnested dynamic nonlinear econometric models is presented. It is based on the idea of testing the specification of the first model by using the second model to generate moment conditions for the efficient method of moments (EMM) estimation procedure. The testing procedure also provides guidance as to what aspects of the model should be improved. A discussion of the large sample properties of the EMM estimator, the score function, and the associated specification test under model misspecification is also included. Key words and phrases: Nonnested Hypotheses, Structural Econometric Models, Method of Moments Estimation. 3 1. Introduction Testing separate or nonnested hypotheses may be appealing to econometricians since it allows them to contrast models arising from competing economic theories. Several approaches have been followed to accomplish this task, one may find Cox's idea of standardizing the difference of likelihoo...

to stimulate discussion and critical comment. References in publications to International Finance Discussion Papers (other than an acknowledgment that the writer has This paper develops a constant, data-coherent, error correction model for broad money demand (M3) in Greece. This model contributes to a better understanding of the e®ects of monetary policy in Greece, and of the portfolio consequences of ¯nancial innovation in general. The broad monetary aggregate M3 was targeted until recently, and current monetary policy still uses such aggregates as guidelines, yet analysis of this aggregate has been dormant for over a decade. In spite of large °uctuations in the in°ation rate, introduction of new ¯nancial instruments, and liberalization of the ¯nancial system, the estimated model is remarkably stable. The dynamics of money demand are important, with price and income elasticities being much smaller in the short run than in the long run.

Market-based climate change policy instruments have frequently been proposed as efficient means to stimulate industrial energy efficiency improvements and to reduce carbon emissions. This paper presents an assessment of the impacts that energy taxes and policies that increase cost of carbon may have on energy use and emission profiles of the US iron and steel industry. Time series data and engineering information are combined to endogenously specify changes in technologies, fuel mix, and production processes within a dynamic computer model. The results of the model indicate that energy taxes are likely to shift a slightly larger share of production to the electric arc furnace route and reduce total energy use more than combarable climate change policies that raise costs of carbon. However, both energy taxes and costs of carbon will result in a similar decrease in carbon emissions when compared to the absence of those policies. Key Words

The United States pulp and paper industry has experienced significant structural changes over the past twenty-five years, including reductions in the number of pulp and paper mills, lower rates of capacity growth, employment cutbacks, and a loss of market share to foreign competitors. These structural shifts portray an industry that increasingly has difficulty adapting to a more competitive environment and earning sufficient profits to generate a return on investment that covers opportunity costs. Based on aggregate data from 1965-1996, this paper estimates a translog cost function for the industry’s short run costs. Adjusting for first order serial correlation, the estimated model fits the data well and all sample points satisfy the monotonicity and concavity conditions. Among the findings, the industry operates at slightly increasing returns to capital utilization and labor and energy are complements in production whereas materials is a substitute in production for the other inputs. Technological progress generated 0.037 % reduction in annual operating costs at the mean but the effect was asymmetric with a much larger impact during the early part of the period. Consistent with an ailing industry, estimated marginal costs approximated average operating costs until 1981 after which marginal costs significantly diverged from average operating costs.

We examine the properties of automatic model selection, as embodied in PcGets, and evaluate its performance across different (unknown) states of nature. After describing the basic algorithm and some recent changes, we discuss the consistency of its selection procedures, then examine the extent to which model selection is non-distortionary at relevant sample sizes. The problems posed in judging performance on collinear data are noted. The conclusion notes how PcGets can handle more variables than observations, and hence how it can tackle non-linear models.

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The authors thank the participants in an ECB internal seminar and an anonymous referee for very valuable comments.We are especially grateful to Dieter Gerdesmeier for a number of interesting suggestions. All remaining errors are our own. The opinions expressed are exclusively those of the authors and should not be attributed to the European