The archive.

What can an estimated demand curve tell policymakers—and what can it never decide for them? In this 1949 article, Gerhard Tintner connects the methodological claims of econometrics to the difficulties of measuring American agricultural demand and supply. Prices and quantities do not identify those relationships unaided: economic assumptions must first make them distinguishable. His empirical results sharpen the point. Demand estimates yield interpretable elasticities, while insignificant supply coefficients expose missing influences, including weather and possibly lagged prices. Tintner treats such failures as grounds for revising models, not concealing uncertainty. The article offers a concrete account of how theory becomes a testable numerical claim, while keeping estimates of policy consequences distinct from judgements about which social objectives deserve priority.

Different statistical aims can lead to closely related estimating equations without making the methods interchangeable. In this mathematical article, Gerhard Tintner connects canonical correlation, principal components, weighted regression, and discriminant analysis through constrained optimization. His distinctive move is to identify the matrices and normalization conditions that give a shared algebraic structure its different statistical meanings: a covariance matrix paired with the identity in principal components, for example, becomes sample covariance paired with error covariance in weighted regression. Readers can discover how apparently separate procedures meet through reductions to common stationary equations—and why their objectives and assumptions still matter. Tintner confines the comparison to estimation, leaving sampling distributions outside the argument.

Gerhard Tintner’s 1950 review of Jan Tinbergen’s The Dynamics of Business Cycles asks what specialized econometric research can offer economists without advanced mathematical training. His answer rests on Tinbergen’s combination of accessible exposition, statistical investigation, and experience in Dutch economic planning. Reviewing the translation adapted for American readers by J. J. Polak, Tintner values a Keynesian-influenced approach that nevertheless considers other theoretical systems. His particular attention to hog and coffee markets gives the review a concrete agricultural emphasis: aggregate fluctuations matter alongside cycles in individual commodities. This short, favorable assessment shows why Tintner regarded Tinbergen’s synthesis as useful for teaching and policy discussion, without offering a detailed critique of its models or prescriptions.

Writing in tribute to Abraham Wald, Gerhard Tintner asks what mathematical rigor can secure for economics—and where its assumptions limit practical use. This 1952 memorial survey distinguishes the formulation of equilibrium equations from proofs that economically admissible solutions exist. It also shows how cost-of-living comparisons depend on information about preferences that observed prices and purchases alone cannot supply. Tintner’s appreciation is not uncritical: he questions minimax decision rules that treat an indifferent Nature as an adversary, and the feasibility of measuring social losses for policy decisions. The article offers a compact encounter with Wald’s achievements through an economist’s discriminating perspective, revealing both the power of explicit assumptions and the empirical work still needed to make formal results useful.

A fitted relationship need not be the economic relationship an investigator seeks. In Econometrics, first published in 1952, Gerhard Tintner makes this gap between statistical calculation and economic interpretation a central teaching problem. Prices and quantities are jointly determined, so reversing a simple regression cannot by itself recover supply or demand; smoothing a time series can introduce dependence rather than merely uncover it. Tintner’s emphasis is statistical, but his tests of method remain tied to economic questions and worked applications. Readers can discover why assumptions about structure, measurement and temporal dependence change what an estimate warrants—and why describing past observations is not enough to justify a forecast. This reprint preserves a textbook concerned as much with the limits of quantitative inference as with its procedures.

A straight-line demand curve makes calculation possible—but what does it leave out? Gerhard Tintner’s textbook trains the prospective econometrician to connect mathematical convenience with economic interpretation and statistical evidence. Economic problems motivate the techniques: income distributions introduce logarithms, marginal costs give derivatives their meaning, and family-income sampling exposes the consequences of selection bias. Represented here by its 1954 second printing, the book joins elementary mathematical instruction to the practical judgment required for estimation and testing. Readers can discover how an economic relationship becomes a calculable model, while learning why an approximation is not an exact description and a significance threshold does not eliminate the possibility of error.

Accurate forecasts of Swedish food consumption anchor Gerhard Tintner’s favorable assessment of Herman Wold’s Demand Analysis, written in association with Lars Juréen. In this short review, Tintner weighs the study’s combination of ordinal utility theory, classical least-squares estimation, and time-series corrections against its empirical results. He singles out the predictions for 1949–50 as evidence of stable consumption patterns and contrasts their success with other econometric forecasting efforts. His judgement also distinguishes the mathematical expertise needed to assess the book’s original contributions from the accessibility of its introductory and empirical sections. The review offers a concise view of what Tintner valued in demand analysis: theoretical foundations, statistical methods, and forecasts tested against observed consumption.

How should a theory of choice under uncertainty be judged: by its mathematical consistency, its account of actual conduct, or its rules for rational action? In this 1954 review of the proceedings of the 1952 Paris econometrics colloquium, Gerhard Tintner admires Maurice Allais’s mathematical work without accepting that psychological objections settle the dispute over von Neumann–Morgenstern utility theory. His distinctive concern is methodological: axioms need not be self-evident, descriptions of behavior must be separated from prescriptions, and an idealized model should not be rejected merely because it leaves some motives unexplained. Yet abstraction does not excuse neglect of evidence. The review offers a pointed assessment of where mathematical economics illuminates uncertainty—and where an ostensibly econometric discussion falls short of empirical inquiry.

It is disappointing that a colloquium entitled Econometrics should contain so little empirical material.

A model chosen for its economic usefulness may still yield misleading measures of statistical precision. In this brief discussion of three agricultural-marketing papers, Gerhard Tintner welcomes modern econometric methods while warning that trying several models before selecting one introduces biases that affect reported standard errors. He also suggests that restrictive linearity assumptions may help explain modest forecasting results. His perspective is constructive rather than merely corrective: he connects agricultural decision models with economic and statistical theory, and values methods that make the choice among alternative technologies part of production analysis. The contribution offers a compact distinction between fitting data, forecasting reliably, and representing the decisions producers actually face.

Computational progress does not settle the question of how reliable economic estimates are. In this brief discussion, Gerhard Tintner welcomes work by Nerlove, Suits and Koizuma on neglected supply functions and praises Ladd’s demonstration of the value of large-scale digital computers. Yet his approval carries a reservation: viewed against Morgenstern’s concern with the accuracy of economic observations, might Ladd’s model allow too little observational error? Tintner’s compact intervention offers a concrete distinction between advances in technique and adequacy of empirical assumptions, while pointing to cost functions and multicollinearity as subjects still needing attention.

How much mathematics should an introduction to economic modelling teach, and which tools can it afford to leave out? In this 1958 review of E. F. Beach’s Economic Models—An Exposition, Gerhard Tintner judges accessibility against technical coverage. He values Beach’s use of economic examples to make mathematical and statistical methods intelligible to readers with substantial economic knowledge but limited mathematical training. Yet he finds the explanations compressed and challenges the omission of matrices, determinants, input-output models and linear programming: these, he argues, are no harder than topics already included. This brief assessment offers a concrete view of Tintner’s pedagogical priorities, showing why an example-rich introduction can succeed while still leaving readers without useful analytical tools.

How much can a small sample tell agricultural administrators before a full census is available? In this brief 1958 review of Heinrich Strecker’s Moderne Methoden in der Agrarstatistik, Gerhard Tintner singles out a striking result: a sample covering 2% of West German agricultural enterprises produced preliminary census figures, nine-tenths of whose deviations from complete enumeration were below 10%. Tintner’s interest lies in the practical performance of statistical methods, rather than a separate examination of their theory. His favorable assessment connects this example with ongoing surveys of farm labor and agricultural production, while noting Strecker’s command of international research. The review offers a compact account of the concrete evidence on which Tintner based his confidence in agricultural sampling.