We consider a continuous-time neoclassical one-sector stochastic growth model of Ramsey-type with CRRA utility and Cobb-Douglas technology, where each of the following components are exposed to exogeneous uncertainties (shocks): capital stock K, effectiveness of labor A, and labor force L; the...

We show by Monte Carlo simulations that the jackknife estimation of QUENOUILLE (1956) provides substantial bias reduction for the estimation of short-term interest rate models applied in CHAN ET AL. (1992) - hereafter CKLS (1992). We find that an alternative estimation based on NOWMAN (1997)...

In the recent years, di usion models for interest rates became very popular. In this paper, we try to do a selection of a suitable diffusion model for the Italian interest rates. Our data set is given by the yields on three-month BOT, from 1981 to 2001, for a total of 470 observations. We...

In light of the recent financial and real economic crisis, it seems clear that macroeconomists need to better account for the influence of financial markets. This paper explores the consequences of treating the interaction between different financial markets, monetary policy, and the real...

Abstract In this paper, we develop a stochastic model to analyze how financial contagion may affect economic activity. In the deterministic case, we show that, according to specific parameter values, the economy may converge either to a stress-free equilibrium or to a stressed equilibrium: in...

Stochastic di®erential equations (SDEs) are　central to much of modern finance theory and have been widely used to model the behaviour of key variables such as the　instantaneous short-term interest rate, asset prices, asset returns and their volatility.　The explanatory and/or predictive...

In geographical epidemiology, disease counts are typically available in discrete spatial units and at discrete time-points. For example, surveillance data on infectious diseases usually consists of weekly counts of new infections in pre-defined geographical areas. Similarly, but on a different...

describes the use of the Gallant-Tauchen efficient method of moments (EMM) technique for diagnostic checking of stochastic differential equations (SDEs) estimated from financial market data. The EMM technique is a simulation-based method that uses the score function of an auxiliary model as the...

Maximum-likelihood estimates of the parameters of stochastic differential equations are consistent and asymptotically efficient, but unfortunately difficult to obtain if a closed-form expression for the transitional probability density function of the process is not available. As a result, a...

We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs), by taking large time steps. The SDE discretization is built up by means of the polynomial chaos expansion method, on the basis of accurately determined stochastic collocation (SC) points. By...