This paper numerically solves the decision problem of a multiperiod constant relative risk aversion individual who faces transaction costs and has access to two risky assets, both with predictable returns. With proportional transaction costs and independent and identically distributed returns, we numerically find the rebalancing rule to be a no-trade region for the portfolio weights with rebalancing to the boundary. The shape of the no-trade region depends on the correlation between the two risky assets. With predictable returns, there is instead a no-trade region for each state. We also examine several important economic questions, including the utility cost of not being able to buy on margin or short stock.

We find that the Campbell–Cochrane external-habit model can generate a value premium if the persistence of the consumption surplus is sufficiently low. Such low persistence is supported by micro evidence on consumption. If the mean and conditional volatility of consumption growth are highly persistent, as in the Bansal–Yaron long-run risk model, then fast-moving habit can also generate, without eroding the value premium: i) empirically sensible long horizon return predictability; and ii) a price–dividend ratio for market equity that exhibits the high autocorrelation found in the data. Fast-moving habit also delivers several empirical properties of market-dividend strips.

This paper describes estimation methods, based on the generalized method of moments (GMM), applicable in settings where time series have different starting or ending dates. We introduce two estimators that are more efficient asymptotically than standard GMM. We apply these to estimating predictive regressions in international data and show that the use of the full sample affects inference for assets with data available over the full period as well as for assets with data available for a subset of the period. Monte Carlo experiments demonstrate that reductions hold for small-sample standard errors as well as asymptotic ones.