[If returns are not predictable, dividend growth must be predictable, to generate the observed variation in divided yields. I find that the absence of dividend growth predictability gives stronger evidence than does the presence of return predictability. Long-horizon return forecasts give the same strong evidence. These tests exploit the negative correlation of return forecasts with dividend-yield autocorrelation across samples, together with sensible upper bounds on dividend-yield autocorrelation, to deliver more powerful statistics. I reconcile my findings with the literature that finds poor power in long-horizon return forecasts, and with the literature that notes the poor out-of-sample R² of return-forecasting regressions.]

[I report a bound on the variance of price-dividend ratios and a decomposition of their variance into terms that reflect changes in dividend growth and discount rates. The specification is not restrictive. The test statistics do not require construction of ex post present values; instead, they are restrictions on means, variances, and covariances of price-dividend ratios, dividend growth, and discount rates. I consider implications for the mean price-dividend ratio, and I evaluate whether a low mean discount rate can rationalize the mean and variance of price-dividend ratios. The results do not indicate any striking rejections of present-value models. However, the bulk of the variance of price-dividend ratios must be accounted for by changing forecasts of discount rates, and discount rates must possess some unusual characteristics.]

Suppose a consumer sets consumption equal to income each period, rather than follow the optimal permanent income decision rule. How much utility does he lose? This paper finds that the answer is typically less than 10-$1 per quarter in environments specified by popular tests on aggregate data, and concludes that the theory does not make predictions in those environments that are robust to small costs of information, transactions, etc.

This paper describes a production-based asset pricing model. It is analogous to the standard consumption-based model, but it uses producers and production functions in the place of consumers and utility functions. The model ties stock returns to investment returns (marginal rates of transformation), which are inferred from investment data via a production function. The production-based model is used to examine forecasts of stock returns by business-cycle related variables and the association of stock returns with subsequent economic activity.

I examine a factor pricing model for stock returns. The factors are returns on physical investment, inferred from investment data via a production function. I examine the model's ability to explain variation in expected returns across assets and over time. The model is not rejected. It performs about as well as the CAPM and the Chen, Roll, and Ross factor model, and it performs substantially better than a simple consumption-based model. I also provide an easy technique for estimating and testing dynamic, conditional asset pricing models--one simply includes factors and returns scaled by instruments in an unconditional estimate--and for comparing such models.

Currently available health insurance contracts often fail to insure long-term illnesses: sick people can suffer large increases in premiums or denial of coverage. I describe insurance contracts that solve this problem. Their key feature is a severance payment. A person who is diagnosed with a long-term illness and whose premiums are increased receives a lump sum equal to the increased present value of premiums. This lump sum allows him or her to pay the higher premiums required by any insurer. People are not tied to a particular insurer or a group, and the improvement is free: insurance companies can operate at zero economic profits, and consumers can pay exactly the same premium they do with standard contracts.

Are consumers effectively insured against idiosyncratic shocks to income or wealth, either by formal institutions such as charities, private insurance, and government programs or by informal mechanisms such as gifts and "loans" from relatives, friends, and neighbors? Under full insurance, consumption growth should be cross-sectionally independent of idiosyncratic variables that are exogenous to consumers. This proposition is tested by cross-sectional regressions of consumption growth on a variety of exogenous variables. Full insurance is rejected for long illness and involuntary job loss, but not for spells of unemployment, loss of work due to strike, and an involuntary move.

This paper presents a measure of the persistence of fluctuations in GNP based on the variance of its long differences. That measure finds little long-term persistence in GNP. Previous research on this question found a great deal of persistence in GNP, suggesting models such as a random walk. A reconciliation of this paper's results with previous research shows that conventional criteria for time-series model building can produce misleading estimates of persistence.

Conventional models of production under uncertainty specify that output is produced in fixed proportions across states of nature. I investigate a representation of technology that allows firms to transform output from one state to another. I allow the firm to choose the distribution of its random productivity from a convex set of such distributions described by a limit on a moment of productivity scaled by a natural productivity shock. The model produces a simple discount factor that is linked to productivity and that can be used to price a wide variety of assets, without regard to preferences. Received November 26, 2019; editorial decision May 23, 2020 by Editor Jeffrey Pontiff.

How should long-term investors form portfolios in our time-varying, multi-factor and friction-filled world? Two conceptual frameworks may help: first, look directly at the stream of payments that a portfolio and payout policy can produce. Second, include a general equilibrium view of the markets’ economic purpose, and the nature of investors’ different preferences, risk-taking ability, and function in that equilibrium. These perspectives can rationalize some of investors’ behaviors, suggest substantial revisions to standard portfolio theory, and help us to apply portfolio theory in a way that is useful in practice.