Performance of periodic time series models in forecasting

The paper provides a comparison of alternative univariate time series models that are advocated for the analysis of seasonal data. Consumption and income series from (West-) Germany, United Kingdom, Japan and Sweden are investigated. The performance of competing models in forecasting is used to assess the adequacy of a specific model. To account for nonstationarity first and annual differences of the series are investigated. In addition, time series models assuming periodic integration are evaluated. To describe the stationary dynamics (standard) time invariant parametrizations are compared with periodic time series models conditioning the data generating process on the season. Periodic models improve the in-sample fit considerably but in most cases under study this model class involves a loss in ex-ante forecasting relative to nonperiodic models. Inference on unit-roots indicates that the nonstationary characteristics of consumption and income data may differ. For German and Swedish data forecasting exercises yield a unique recommendation of unit roots in consumption and income data which is an important (initial) result for multivariate analysis. Time series models assuming periodic integration are parsimonious to specify but often involve correlated one-step-ahead forecast errors. First version received: April 1996/final version received: January 1998     

Wage differences between Austrian men and women: <Emphasis Type="Italic">semper idem?</Emphasis>

In most OECD countries, the wage gap between men and women has narrowed during the past two decades. Developments of the last 20 years, e.g., increased labour market attachment of women or the introduction of equal pay laws, may have reduced the gender wage gap. We investigate the extent, persistence, and socio-economic determinants of the gender wage gap in Austria, for the years 1983 and 1997. Using wage decomposition techniques, we find that the average gender wage gap was almost as high in 1997 as it was in 1983. Not accounting for differences, the gender wage gap dropped from 25.5 to 23.3% of men’s wages. Taking observable differences between men and women into account, we estimate that the mean gender wage gap that cannot be explained, i.e., discrimination against women, dropped from 17 to 14% of men’s wages. A decomposition of the gender wage gap over time indicates that both returns to human capital and less discrimination were responsible for the narrowing of the gender wage gap.

The gender wage gap in Austria: eppur si muove!

We examine the change of the gender wage gap in Austria between 2002 and 2007 using Juhn et al. (Workers and their wages, AEI Press, Washington DC, pp 107–143, 1991) decomposition. We analyze data from Austrian tax records which we merge with social security records and Austrian micro-censuses of the years 2002 and 2007. We find that the gender wage gap declined from 21 % in 2002 to 18 % in 2007. The main determinant of this decline is the relative improvement of women’s unobserved characteristics. The decline of the gender wage gap is also due to women having attained more formal education and to a convergence of men’s and women’s returns to education. Women’s improved educational attainments were partly offset by a shift in the demand for skilled workers that disadvantaged unskilled workers, most of whom are women, resulting in a moderate decrease of the gender pay gap over these years. Robustness checks confirm our results.     

Wage bargaining,productivity growth and long-run industry structure

This paper studies the innovation dynamics of an oligopolistic industry. The firms compete not only in the output market but also by engaging in productivity enhancing innovations to reduce labor costs. Rent sharing may generate productivity dependent wage differentials. Productivity growth creates intertemporal spillover effects, which affect the incentives for innovation at subsequent dates. Over time the industry equilibrium approaches a steady state. The paper characterizes the evolution of the industry's innovation behavior and its market structure on the adjustment path.     

Challenges for land system science

While considerable progress has been made in understanding land use change, land system science continues to face a number of grand challenges. This paper discusses these challenges with a focus on empirical land system studies, land system modelling and the analysis of future visions of land system change. Contemporary landscapes are contingent outcomes of past and present patterns, processes and decisions. Thus, empirical analysis of past and present land-use change has an important role in providing insights into the socio-economic and ecological processes that shape land use transitions. This is especially important with respect to gradual versus rapid land system dynamics and in understanding changes in land use intensity. Combining the strengths of empirical analysis with multi-scale modelling will lead to new insights into the processes driving land system change. New modelling methods that combine complex systems thinking at a local level with macro-level economic analysis of the land system would reconcile the multi-scale dynamics currently encapsulated in bottom-up and top-down modelling approaches. Developments in land use futures analysis could focus on integrating explorative scenarios that reflect possible outcomes with normative visions that identify desired outcomes. Such an approach would benefit from the broad and in-depth involvement of stakeholders in order to link scientific findings to political and societal decision-making culminating in a set of key choices and consequences. Land system models have an important role in supporting future land use policy, but model outputs require scientific interpretation rather than being presented as predictions. The future of land system science is strongly dependent on the research community's capacity to bring together the elements of research discussed in the paper, via empirical data collection and analysis of observed processes, computer simulation across scale levels and futures analysis of alternative, normative visions through stakeholder engagement.     

Determinants of Current Account Imbalances in 16 OECD Countries: An Out-Of-Sample Perspective

We employ panel data as well as country-specific models to uncover empirically the determinants of current account imbalances. We find evidence of slope heterogeneity likely rendering the fixed-effect estimator inconsistent. Mean group estimation is followed to overcome the latter difficulty. Both estimation techniques are evaluated by means of in- and out-of-sample criteria. The mean group estimator outperforms the fixed-effect approach and, moreover, only three economic variables are found to impact on the current account balance significantly. These are the government budget balance, the domestic output gap and the changes of the terms of trade. We finally propose a parsimonious dynamic model of the current account, including only these variables and illustrate that it has similar predictive accuracy as country-specific benchmark models. JEL no. F32, C23, C53     

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