The archive.

When does a statistical model of business cycles warrant conclusions about economic policy? In this 1941 review of J. Tinbergen’s study of the United States in 1919–1932, Gerhard Tintner admires the ambition of linking economic theory to an extensive system of estimated equations, but scrutinizes the grounds for trusting its results. His objections are concrete: time-series residuals require testing, regression coefficients may change, and expectations remain insufficiently explicit. Findings that public investment dampens fluctuations while price stabilization increases them depend on restrictive conditions, including the absence of a stock-exchange boom. The review offers a compact encounter with econometric judgment: Tintner distinguishes the achievement of constructing a comprehensive model from the reliability of its causal explanations and policy deductions.

A useful statistical textbook need not settle the theoretical problems behind its procedures. In this brief review of the second edition of Mordecai Ezekiel’s Methods of Correlation Analysis, Gerhard Tintner distinguishes genuine practical improvements from questions the discipline itself has yet to resolve. His qualified praise turns especially on the reliability of time-series forecasts: a sketchy treatment of error formulas disappoints him, but he refuses to blame Ezekiel for the absence of generally accepted solutions. Tintner also notes the edition’s reliance on Fisher’s fiducial approach rather than newer theories of estimation and hypothesis testing. The review offers a concise appraisal of what improved statistical instruction can provide—and what remains beyond its reach.

More observations do not necessarily mean more information: this difficulty gives Gerhard Tintner’s review of Harold T. Davis’s The Analysis of Economic Time Series a focus beyond its assessment of a statistical handbook. Tintner asks what mathematicians miss when economic statistics remains outside their field of attention. He distinguishes lucid exposition from genuine methodological innovation, praising Davis’s work on serial correlation while treating forecasting and business-cycle explanation as unresolved problems. Particularly revealing is his account of how dependence among observations can undermine a naïve count of degrees of freedom. The review offers a concise encounter with Tintner’s standards of judgement: mathematical ingenuity matters, but so do the limits of inference and the connection between statistical technique and economic interpretation.

What should a textbook in the new field of econometrics teach—and which difficulties should it refuse to evade? In this 1943 review of Harold T. Davis’s The Theory of Econometrics, Gerhard Tintner weighs classroom usefulness against original research. He praises the exposition of economic statics but locates Davis’s strongest contributions in dynamics, particularly the treatment of serial correlation and forecasting, problems he considers too often avoided in economic statistics. His judgement draws on teaching experience: students with some calculus can, he argues, work through the book’s problems and acquire a substantial grounding in the subject. This compact review offers a concrete view of Tintner’s standards for econometric education, joining mathematical theory to the complications of observed economic data.

Should a farmer invest in new buildings and machinery, or spend more on feed, fuel, repairs, and supplies? Gerhard Tintner and O. H. Brownlee bring this practical allocation problem to production-function analysis using 1939 records from 468 Iowa farms. Their estimates suggest that these farms were, on average, over-improved: additional operating expenditure and productive assets promised greater marginal returns than further improvements. The interest lies as much in how they reach that judgement as in the recommendation itself. Translating accounts into inputs exposes unmeasured differences in land and labor and, crucially, the absence of any satisfactory measure of management. Readers can see how elasticities become investment comparisons—and why findings from unusually prosperous farms in a single year cannot become general rules for Iowa agriculture.

India in 1940, England in 1830–48, and Germany in 1865–70 form one of the historical “equations” Gerhard Tintner examines in this brief review of Benoy Kumar Sarkar’s book. Tintner clarifies that the title promises no mathematical economics: Sarkar instead compares countries at supposedly equivalent stages of economic development. The review’s interest lies in Tintner’s discriminating judgement of that approach. He credits Sarkar with perceptive observations and wide multilingual reading, while qualifying the depth of his scholarship and finding his treatment of post-war problems unilluminating and journalistic. This compact assessment separates the appeal of comparative economic history from the book’s less convincing promise of guidance for reconstruction.

Calling the economy a machine does not, for Gerhard Tintner, make economics an exact science. In this brief, sharply dismissive review of Jerome Levy’s book, he questions the unconventional terminology and assumed linear relationships behind Levy’s model. He also asks what social philosophy supports its prescriptions for “just” wages and profits, finding little explicit justification. The interest lies in the grounds of Tintner’s rejection: mathematical form and appeals to justice are not substitutes for defensible economic concepts and stated policy principles. His closing astonishment that the book found a publisher during a paper shortage gives the methodological objections a distinctly caustic edge.

When observed variables contain measurement error, strong correlations alone cannot establish how many independent relationships underlie them. Gerhard Tintner’s 1945 note links that question to a second: how should those relationships be estimated once their number is determined? His distinctive move is to make one generalized eigenvalue problem serve both purposes, using an independently estimated disturbance covariance matrix to separate systematic dependence from noise. The emphasis is structural estimation rather than prediction: the aim is to recover coefficients of underlying relations, not simply improve a forecast. Readers can follow how error-weighted least squares leads back to the roots used in rank testing—and see why the resulting unity depends on assumptions about disturbance estimation and large samples.

A mathematics textbook can be stronger in coverage yet less suitable for its students. This distinction shapes Gerhard Tintner’s review of W. L. Crum and Joseph A. Schumpeter’s Rudimentary Mathematics for Economists and Statisticians. He welcomes calculus taught through costs, demand and utility rather than classical mechanics, but questions whether compression helps beginners when differentiation rules appear without proof. His objection is concrete: introducing the binomial theorem could clarify differentiation while also serving students of probability, whose needs he finds neglected. The review offers a compact example of judging a textbook by more than mathematical breadth: Tintner weighs explanatory clarity, disciplinary relevance and prior preparation, ultimately favouring its teaching suitability over the fuller coverage of competing introductions.

A concise introduction can teach statistical reasoning well yet leave students poorly equipped to continue their studies. In this brief review of S. T. Bok’s textbook for medical students, Gerhard Tintner praises its Fisherian perspective, medical and biological examples, and emphasis on methodology rather than mathematical proofs. His reservations are concrete: nomographs seem less accurate than tables, replacing χ² with G² needlessly obstructs access to English-language literature, and the absence of references leaves no route to further reading. The review offers a compact account of Tintner’s standards for statistical teaching: clarity and brevity matter, but so do computational accuracy, shared notation, and guidance beyond the introductory text.

Useful measurements can confirm economic expectations without adequately explaining the relationships they record. This distinction drives Gerhard Tintner’s review of Frederick C. Mills’s study of prices and quantities in business cycles. Tintner acknowledges the study’s suggestive findings, including stronger cyclical patterns in prices than in quantities and marked differences between farm products and capital equipment. Yet he questions the National Bureau’s reference-cycle methods and warns that Mills’s measures of elasticity and flexibility differ from their usual meanings in economic theory. The review offers a compact example of methodological criticism that does not dismiss empirical results: Tintner’s demand is for a more explicit theoretical foundation and more refined statistical techniques, not merely additional measurements.

Gerhard Tintner’s 1948 review of Paul Anthony Samuelson’s Foundations of Economic Analysis turns admiration for mathematical rigor into a pointed question: what can maximizing behavior alone tell us about actual economic conduct? Writing from an econometric perspective, Tintner praises Samuelson’s theoretical achievement while challenging the narrow assumptions behind its treatment of dynamics. His most concrete objection concerns expectations and uncertainty: understanding how anticipations form, he argues, offers a more useful bridge from statics to dynamics than formal relations among equilibria alone. This short review lets readers examine the distinction Tintner draws between powerful economic mathematics and empirically informative economics—and why he regards statistical investigation as a necessary complement, rather than an alternative, to theory.