Studies of risk preference have empirically established two regularities that are inconsistent with the canonical expected utility model: (1) risk aversion over small gambles greatly exceeds risk aversion over larger stakes and (2) insurance buyers play the lottery. This paper characterizes risk preferences both theoretically and empirically in a world with two consumption goods, one of which involvesacommitmentinthatanadjustmentcostmustbepaidwhenthegood is sold. In this model, utility over wealth is more curved locally than globally: individuals are more risk averse with respect to moderate-scale income fluctuations than they are to large income fluctuations. Commitments also create a gambling motive. The empirical importance of commitments is tested using the labor-supply method of estimating risk aversion of Chetty (2003a). Global curvature is imputed using existing labor supply elasticities, and variations in unemployment insurance laws are used to estimate local curvature in a dynamic job search model. Commitments significantly change preferences over wealth: The local coefficient of relative risk aversion is an order of magnitude larger than the global one. Implications for a broad set of questions such as optimal social insurance policies and portfolio choice are discussed.

We estimate a structural model of optimal life-cycle housing and consumption in the presence of realistic labor income and house price uncertainties. The model postulates constant elasticity of substitution between housing service and nonhousing consumption, and explicitly incorporates a house adjustment cost. Our estimation fits the crosssectional and time-series household wealth and housing profiles from the Panel Study of Income Dynamics quite well, and suggests an intra-temporal elasticity of substitution between housing and nonhousing consumption of 0.33 and a housing adjustment cost that amounts to about 15 percent of house value. Policy experiments with estimated preference parameters imply that households respond nonlinearly to house price changes with large house price declines leading to sizable decreases in both the aggregate homeownership rate and aggregate non-housing consumption. The average marginal propensity to consume out of housing wealth changes ranges from 0.4 percent to 6 percent. When lending conditions are tightened in the form of a higher down payment requirement, interestingly, large house price declines result in more severe drops in the aggregate homeownership rate but milder decreases in nonhousing consumption. Key Words: Life-cycle, housing adjustment costs, intratemporal substitution, methods of simulated moments

The household’s labor margin has a substantial effect on risk aversion, and hence asset prices, in dynamic equilibrium models even when utility is additively separable between consumption and labor. This paper derives simple, closed-form expressions for risk aversion that take into account the household’s labor margin. Ignoring this margin can wildly overstate the household’s true aversion to risk. Risk premia on assets priced with the stochastic discount factor increase essentially linearly with risk aversion, so measuring risk aversion correctly is crucial for asset pricing in the model. Closed-form expressions for risk aversion in models with generalized recursive preferences and internal and external habits are also derived.

Many households devote a large fraction of their budgets to “consumption commitments” – goods that involve transaction costs and are infrequently adjusted. This paper characterizes risk preferences in an expected utility model with commitments. We show that commitments a¤ect risk preferences in two ways: (1) they amplify risk aversion with respect to moderate-stake shocks and (2) they create a motive to take large-payo ¤ gambles. The model thus helps resolve two basic puzzles in expected utility theory: the discrepancy between moderate-stake and large-stake risk aversion and lottery playing by insurance buyers. We discuss applications of the model such as the optimal design of social insurance and tax policies, added worker e¤ects in labor supply, and portfolio choice. Using event studies of unemployment shocks, we document evidence consistent with the consumption adjustment patterns implied by the model.

I evaluate the effects of long-run consumption growth risk and housing consumption risk on asset prices. Current asset values are affected by the risk-return tradeoff in the long-run. Housing plays an important role in the economy both as an asset and as a consumption component. As an asset, it is particularly sensitive to long-run riskreturn trade off; as a consumption component, it accounts for one fifth of the total expenditures in non durable goods and services. The investment horizon for housing is usually distant in the future. Investors fear shocks that affect the value of their house for a long period of time. Such shocks vary substantially the services obtained from the house and its price as an asset as well. I use a non-separable utility function with non-housing consumption and consumption of housing services, which generates an intertemporal composition risk, besides the traditional consumption growth risk. The composition risk determines the valuation of cash flow growth fluctuations far into the future due to the persistence of consumption growth. I provide a closed form solution for the valuation function and discount factor of the consumers. This allows me to quantify the price of risk in the long-run with inputs from vector autoregressions. I evaluate the different exposure to long-run risk of a cross section of portfolios of securities and characterize the price of risk for different investment horizons. The model explains the spread of the returns to different portfolios sorted by book to market and housing returns, at different investment horizons.

This paper shows that state control of some industries may have contributed to the increase in European unemployment from the 1970s to the early 1990s. We develop a simple model with both publicly-run and privately-run enterprises and show that when economic turbulence increases, higher unemployment rates may result in economies that have a larger public sector. JEL Classification: E240, J450, J640

Every year during the second and third quarters (the “hot season”) housing markets in the UK and the US experience systematic above-trend increases in both prices and transactions. During the fourth and first quarters (the “cold season”), house prices and transactions fall below trend. We propose a search-and-matching framework that sheds new light on the mechanisms governing housing market fluctuations. The model has a “thick-market ” effectthatcangenerate substantial differences in the volume of transactions and prices across seasons, with the extent of seasonality in prices depending crucially on the bargaining power of sellers. The model can quantitatively mimic the seasonal fluctuations in transactions and prices observed in the UK and the US. Key words: housing market, thick-market effects, search-and-matching, seasonality, house price

This paper explores the asset pricing implications of introducing housing into a dynamic general equilibrium model. The model that is proposed can explain key asset markets phenomena such as the high equity premium, the low mean risk-free rate, and the high volatility of house prices. The main business cycle regularities can also be reproduced. Combined with internal habit formation, the introduction of investment frictions amplifies the uncertainty generated by business cycle fluctuations. Housing increases the potential for intertemporal smoothing and allows agents to better insure themselves against shocks. Introducing housing into business cycle models has important asset pricing implications and permits to jointly study the determinants of equity returns and house prices within a unified general equilibrium framework.

August, 2008Until recently, the conventional wisdom in the portfolio choice literature held that anyone who simply added housing to the vector of assets and claimed to identify the optimal portfolio as the vector of asset holding that achieved mean-variance efficiency was “incorrect”. As is often the case, the conventional wisdom is valid in a particular set of circumstances. In this case, the conventional wisdom-- that problems arise when applying a standard mean-variance optimization framework to a portfolio problem incorporating housing – is valid if the problem is set up as the choice over the quantities of all assets (the quantity of housing as well as the quantities of each financial asset) in a sequence of repeated mean-variance optimizations. The issue is that capital gains on housing are essentially different from capital gains on financial assets in the sense that an increase in the asset price of housing is strongly correlated with the price of a good which is quantitatively important in the household’s future consumption bundle (future housing services), and therefore do not represent gains in wealth exactly comparable to the gains which come from increases in the price of a financial asset. From a personal perspective, I may enjoy reading the real estate section of the paper every Sunday to note how much the value of my San Diego home has increased from 2000 to 2005 (measured in dollar

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