The archive.

A distance between population means and a regression fitted to error-bearing observations may seem to answer different questions. In this four-page contribution, Gerhard Tintner connects them through the same inverse-covariance weighting: discrepancies matter relative to the variability and dependence of the errors. His focus is on estimating linear relations among underlying systematic components when several observed variables contain error, rather than treating explanatory variables as error-free. The connection yields a generalized eigenvalue problem whose smallest roots identify candidate relations, with approximate sequential tests used to estimate their number. Readers can discover how the geometry of Mahalanobis distance becomes an estimation criterion—and why distinguishing error covariance from observed covariance matters to that procedure.

How much economic detail can a planning model responsibly promise when national statistics are scarce and unreliable? Gerhard Tintner and Oswaldo Dávila address this problem through a deliberately compact, five-equation Keynesian model of Ecuador, estimated from 1950–1961 data. They defend aggregation as a safeguard against false precision while arguing that coherent development policy requires explicit econometric relationships. The article’s concrete interest lies in its comparison of policy effects: within the model, investment and government consumption plus net exports can produce similar gains in output yet opposite movements in employment and wages. Readers can examine how limited evidence becomes a tool for distinguishing policy choices—and where that tool needs caution, particularly when reported numerical interpretations do not consistently match the equations.

An operating policy can remain optimal despite errors in its coefficients while its returns—and the ranking of alternative policies—change. J. K. Sengupta, C. Millham, and Gerhard Tintner make this distinction central to their study of stochastic linear programming. Their numerical example preserves the winning selection under ten-percent coefficient errors yet allows inferior selections to exchange places, giving concrete force to the question of what, exactly, is stable. They then compare the variability of best, second-best, and third-best returns, exploring a conditional trade-off between higher returns and lower dispersion through an Iowa farm example. The article offers readers a precise way to distinguish persistence of an optimal policy from stability of its payoff, while its variance argument requires closer scrutiny of the assumptions supporting that trade-off.

Economic variables may move together, but how can that dependence enter a stochastic model rather than appear only as a correlation after estimation? Gerhard Tintner and R. Narayanan answer by allowing events to change several variables simultaneously: shared increments become the source of covariance. Their compact article connects this mathematical construction to post-war United States gross national product and private consumer expenditures, culminating in a joint uncertainty ellipse for a 1965 forecast. The distinctive payoff is a concrete link between assumptions about shared events and the shape of forecast uncertainty. Readers can also examine the limits of that link: the constant-parameter application permits only upward increments, while the supplied observations include declines. The model offers a tractable account of interdependent trends, not a causal explanation of output and consumption.

A sugar magnate and a banker occupy an unusual place in Gerhard Tintner’s account of early Austrian economics: mathematical innovators whose work found a stronger following outside their own school. This concise encyclopedia entry reassesses Rudolf Auspitz and Richard Lieben through their 1889 study of price theory. Tintner highlights their independent development of indifference curves and their treatment of markets between free competition and monopoly, while contrasting Menger’s hostility with their influence on Pareto, Edgeworth, and Fisher. Their exchange with Walras brings a precise methodological question into view: what can partial-equilibrium analysis explain, and where do its limits lie? The entry offers a compact route into the distinction between belonging to an economic school and contributing to its analytical development.

A growing population need not supply more work. In this 1968 article, Gerhard Tintner and Yves Aureille examine that tension in France’s postwar demographic revival, separating the number of workers from the hours they contribute. Their accounting method distinguishes employment growth from changes in sectoral composition and working time: movement out of agriculture, with its longer hours, can reduce total labour input even as employment expands. They propose a quality-adjusted measure of labour services, while acknowledging that available French statistics permit only a simpler calculation of hours. Their conditional projection of declining labour input in 1965–70 gives the methodological problem a concrete edge. Readers can discover why schooling, retirement, holidays, and occupational shifts complicate any direct inference from demographic growth to labour supply.

Smoothing an economic series can create dependence where none existed; removing its trend can conceal a change that still needs explaining. These difficulties give Gerhard Tintner’s 1968 encyclopedia article its methodological edge. He examines time-series techniques from the economist’s predicament: observations are interdependent, measurements imperfect, and available records often too short to support the large-sample results on which inference relies. Rather than treating trend, cycle, and random disturbance as independent components, Tintner favours stochastic models capable of generating them together. His survey helps readers distinguish a convenient statistical transformation from a defensible account of the process behind the data—and understand why a close historical fit need not warrant a forecast.

A development plan that promises higher average income need not offer greater security against disappointing results. Gerhard Tintner and N. S. Raghavan make this tension explicit by introducing uncertain productive returns into the Mahalanobis two-sector planning model for India. Rather than calculate a single income target, they estimate distributions of terminal national income and compare how investment allocations alter expected performance, low-income thresholds, and relative variability. Their tested policies show why these measures cannot be treated as interchangeable: an allocation preferred for its average return may lose its appeal when stability becomes the priority. The article offers a concrete encounter with planning as a choice among risk criteria, while openly acknowledging the limitations of its aggregated model, independence assumption, and rough numerical approximations.

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.

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.