The archive.

How can an econometric model guide policy when the statistics needed to build it are missing? Gerhard Tintner, Isabella Consigliere, and José T. M. Carneiro confront this problem in their 1970 article, published in parallel Portuguese and English texts. Using Brazilian data for 1952–1964, they construct a compact Keynesian system to estimate how spending, wages, and population affect output, consumption, prices, and employment together. The revealing tension lies between policy ambition and restrictive assumptions: without capital-stock data, production depends on labor alone, and higher wages yield declines in real output and employment that the authors themselves question. Readers can trace both the appeal of simultaneous policy responses and the dependence of those responses on proxy data, theoretical choices, and what the model leaves out.

An investment policy that maximizes expected income need not offer the best protection against disappointing results. This divergence gives concrete force to Gerhard Tintner’s argument in this lecture: uncertainty belongs within economic theory, not merely in error terms added to deterministic equations. His distinctive concern is with economies whose probability distributions themselves change over time—a requirement that makes growth difficult to study with stationary models. Examples from income forecasting and Indian investment planning show what this shift changes in practice. Yet Tintner’s advocacy comes with statistical caution: small samples and potentially infinite variances undermine familiar assurances about estimation. Readers encounter both a case for stochastic economics and an account of why its mathematical tools cannot be taken for granted.

A model can suggest orderly growth and damped fluctuations without establishing that an economy is stable. This tension drives Gerhard Tintner, Gopal Kadekodi, and M. V. Rama Sastry’s essay, which treats trend and business cycle as dynamic properties of one econometric system. Applied to Indian annual data for 1954–66, their framework links agricultural production, industrial production, and manufacturing employment, with government expenditure and net imports as additional explanatory variables. The distinctive emphasis falls not simply on the trajectories implied by estimated coefficients, but on the uncertainty surrounding them. Readers can see how characteristic roots translate statistical relationships into claims about growth and oscillation—and why large sampling errors can leave those claims unresolved.

How much of an increase in demand becomes additional output, and how much becomes higher prices? Gerhard Tintner’s conference contribution addresses this policy question through a compact Keynesian model estimated from Austrian data for 1953–1972. Its distinctive economy of means joins consumption, production, wages, and national-accounting relations to calculate interconnected responses in prices, income, output, and employment. Readers can trace why higher public expenditure raises both prices and real activity within the model, while higher nominal wages or a larger capital stock produce different trade-offs. The interest lies as much in the construction as in the numerical results: economic assumptions supply information that correlated data cannot readily yield. Tintner’s static framework and locally valid elasticities make the conditions behind these policy calculations visible.

Does rising income increase household energy use, or change the equipment through which energy is consumed? In this exploratory conference contribution, Gerhard Tintner and Gabriele Wörgötter estimate demand for five fuels in Austria, finding sharply different relationships beneath the aggregate category of energy. Coal declines with income, while expanding ownership of electrical appliances and oil-fired central heating helps explain rising consumption elsewhere. Their petrol model makes the distinction especially concrete: income works chiefly through car ownership and engine capacity, with little additional effect once these are held constant. Readers can discover both the value and the limits of fuel-specific demand estimates: the authors connect coefficients to household circumstances while explicitly warning that statistical problems and changing supply conditions make their findings provisional.

How can economic planning accommodate uncertainty without surrendering the possibility of intervention? In this 1977 article, Guillermo L. Gómez M. and Gerhard Tintner model development as a multivariate diffusion process, applying their framework to Colombian national accounts for 1950–1972. Their distinctive move is to make expected economic change responsive to changes in government expenditure and private investment, rather than merely extrapolating growth over time. Contrasting projections show how different assumptions about these instruments produce sharply different trajectories. Readers can examine the bridge between stochastic estimation and policy design—and the gap that remains between conditional forecasts and an achievable optimum. The authors outline, but do not complete, an optimal-control programme, making the article an explicitly provisional attempt to connect mathematical uncertainty with deliberate development policy.

Why should proportional changes in prices leave economic choices unchanged—and what happens when investors learn from new evidence? In this conference contribution, Gerhard Tintner tests the assumptions behind the homogeneity of demand and supply and the separation of real from monetary economies. His distinctive move is to distinguish risk, where probability distributions are known, from uncertainty, where their parameters must be inferred. Portfolio models can preserve familiar invariance results when returns, variances, and covariances scale appropriately; Bayesian learning complicates them because observations need not scale with prior beliefs. Tintner also questions the empirical plausibility of the preference restrictions required for monetary and real economies to yield identical outcomes. The article offers a precise way to distinguish mathematical invariance from its behavioral and informational preconditions.

What information disappears when many economic relations become a single macroeconomic model? In this essay, Gerhard Tintner and A. Sondermann make that loss an explicit statistical problem: a reduced model is judged by how well it reconstructs individual outcomes, under specified weights and probability distributions. Their distinctive emphasis is on optimal approximation when exact aggregation fails—not on assuming that totals preserve the relations beneath them. Generalized inverses and eigenvector methods supply mathematical tools, while consumption examples show why differences among individuals and their association with income can alter aggregate coefficients. Readers can discover both the possibilities and the limits of statistical reduction: preserving dominant variation, minimizing reconstruction error, and reproducing economic structure are different objectives, and need not yield the same aggregate.

Observed employment records how many people work—not how many would work, or how many employers would hire, at the prevailing wage. Bernhard Böhm and Gerhard Tintner make this distinction the basis of their econometric study of Austria’s labour market in 1954–1975. Their model treats employment as constrained by whichever is smaller: labour supply or labour demand. The resulting estimates suggest that registered unemployment can conceal the extent of involuntary unemployment during downturns, even when average rates broadly coincide. Yet the authors also expose the fragility of that inference: different starting values change the estimated market regimes, and a higher statistical likelihood need not produce an economically credible result. This essay offers a concrete encounter with both the interpretive possibilities and the numerical limits of disequilibrium modelling.

Even complete knowledge of a system’s observable behavior may leave its underlying parameters undetermined. In this survey contribution, Manfred Deistler and Gerhard Tintner examine that obstacle before estimation begins, linking measurement-error models with multivariate ARMA systems. Their perspective brings probability theory into contact with the algebra of dynamic models: nonnormal distributions and lagged covariances can supply identifying information, while different polynomial representations can describe exactly the same observable process. Crucially, they distinguish restrictions expressing substantive knowledge from canonical conventions that merely select a unique representation. Readers can discover why more data alone cannot resolve some ambiguities—and why even a uniquely specified model needs a well-behaved parameterization if identification is to support consistent estimation.

Wage restraint may increase employment eventually while deepening unemployment first. Peter Rosner, Gerhard Tintner, Andreas Wörgötter, and Gabriele Wörgötter make this tension central to their analysis of a small open economy. Their modified Dornbusch model treats restraint as slower wage and price growth without shifting income from wages to profits. Under fixed exchange rates, reduced inflation raises real interest rates before improved competitiveness can stimulate demand; under flexible rates, the initial currency movement makes the transition less predictable. Yet the model’s eventual employment gains are the same under either regime. The article offers a precise way to distinguish a policy’s destination from the costs of reaching it—and to understand why demand support and coordinated policy matter when wage adjustment promises recovery but initially worsens recession.

A wage increase cannot be assessed through wages alone: its effects on prices, employment, and production depend on the accompanying policy choices. In this 1977 article, Guillermo L. Gómez M. and Gerhard Tintner build an aggregate model of Colombia that combines Keynesian consumption relationships with neoclassical production and labor demand. Their estimates make the case for coordinating investment, public expenditure, and trade policy rather than treating economic targets separately. Yet the practical ambition encounters unreliable data, restrictive assumptions, and the unresolved choice of a welfare objective. Readers can examine both the numerical trade-offs the authors identify and their refusal to turn modeled wage effects into a mechanical recommendation for wage freezes—a concrete encounter between econometric calculation and political feasibility.