Nonparametric Identification of Dynamic Models with Unobserved State Variables

We consider the identification of a Markov process {Wt,Xt*} for t = 1, 2, ... , T when only {Wt} for t = 1, 2, ... , T is observed. In structural dynamic models, Wt denotes the sequence of choice variables and observed state variables of an optimizing agent, while Xt* denotes the sequence of serially correlated unobserved state variables. The Markov setting allows the distribution of the unobserved state variable Xt* to depend on Wt-1 and Xt-1*. We show that the joint distribution f Wt, Xt* | Wt-1, Xt-1* is identified from the observed distribution f Wt+1, Wt | Wt-1, Wt-2, Wt-3 under reasonable assumptions. Identification of f Wt, Xt*, Wt-1, Xt-1* is a crucial input in methodologies for estimating dynamic models based on the "conditional-choice-probability (CCP)" approach pioneered by Hotz and Miller.

International Evidence On Sticky Consumption Growth

We estimate the degree of 'stickiness' in aggregate consumption growth (sometimes interpreted as reflecting consumption habits) for thirteen advanced economies. We find that, after controlling for measurement error, consumption growth has a high degree of autocorrelation, with a stickiness parameter of about 0.7 on average across countries. The sticky-consumption-growth model outperforms the random walk model of Hall (1978), and typically fits the data better than the popular Campbell and Mankiw (1989) model. In several countries, the sticky-consumption-growth and Campbell-Mankiw models work about equally well.