Are business cycles mainly the result of permanent shocks to productivity? This paper uses a long-run restriction implied by a large class of real-business-cycle models-identifying permanent productivity shocks as shocks to the common stochastic trend in output, consumption, and investment

Abstract. Most of economic and financial time series have a nonstationary behavior. There are different types of nonstationary processes, such as those with stochastic trend and those with deterministic trend. In practice, it can be quite difficult to distinguish between the two processes

Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests involve the roots of the ordinary least squares coefficient

Abstract: We study the limiting behaviour of the prominent V/S and R/S test statistics, aimed at detecting long–range dependence, under the presence of a stochastic trend, which is given by cumulative random shocks. Depending on the size of these shocks, the asymptotic distribution is characterized

Techniques for determining the number of stochastic trends generating a set of non-stationary panel data are applied to budget shares for a number of commodity groups from the family expenditure survey (FES) for the UK for the years 1973-2001. It is argued that some stochastic trends in macro data

account for this correlation. What is required is a model where the stochastic nature of the trend is endogenous and growth dynamics is an important component of the transmission of business cycles. We present a stylized endogenous growth model with exogenous cyclical shocks where, despite the cyclical

to 1985, identified six common stochastic trends in a vector of seven nominal exchange rates implying the existence of one cointegrating vector. Cointegration implies that (Granger) causality must run in at least one direction, that is, at least one of the exchange rates is predictable using

Abstract: We study the limiting behaviour of the prominent R/S test statistic, aimed at detecting long–range dependence, if instead of long memory a stochastic trend given by cumulative random shocks is present. As the main result we derive the convergence rate of the R/S statistic to its limit.

We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters

We used a no-arbitrage, cost-of-carry pricing model to examine whether equity spot and futures markets are cointegrated. A stock index and its futures price should be cointegrated if the cost of carry is stationary. Otherwise, the appropriate cointegrating relationship is trivariate. We found that the Standard and Poor's 500 index, associated futures series, and interest rate are all nonstationary. We further found that the cointegrating relationship includes the index, futures price, and cost of carry. Our findings are consistent with the no-arbitrage pricing model and do not appear to be sensitive to the presence of structural breaks in the series.