Abstract:
Given the existence of non-normality and nonlinearity in the data generating process of real
house price returns over the period of 1831-2013, this paper compares the ability of various
univariate copula models, relative to standard benchmarks (naive and autoregressive models) in
forecasting real US house price over the annual out-of-sample period of 1859-2013, based on an
in-sample of 1831-1873. Overall, our results provide overwhelming evidence in favor of the
copula models (Normal, Student’s t, Clayton, Frank, Gumbel, Joe and Ali-Mikhail-Huq) relative
to linear benchmarks, and especially for the Student’s t copula, which outperforms all other
models both in terms of in-sample and out-of-sample predictability results. Our results highlight
the importance of accounting for non-normality and nonlinearity in the data generating process
of real house price returns for the US economy for nearly two centuries of data.
