Simple general equilibrium growth model are used to analyse non-steady-state dynamic adjustments. Alternative methods of closing the efficiency surface structure result in prescriptive versus descriptive versions. A sequence of models is analysed to illustrate.

This paper computes parametric estimates of a time-varying risk premium model and compares the one-step-ahead forecasts implied by that model with those given by a nonparametric kernel estimator of the conditional mean function. The conditioning information used for the nonparametric analysis is...

This paper carries out a specification analysis of a test relation for the unbiasedness hypothesis using 30-day forward foreign exchange data from France, Italy, Japan, the United Kingdom, and West Germany. The results indicate that econometric problems exist for each country's test equation....

This paper tests the martingale hypothesis for daily data from the Deutschmark/US dollar futures and spot foreign exchange markets. Time-varying volatility of daily price changes is modelled as conditional heteroskedasticity which is a function of recent news or forecast errors, as in the ARCH...

This paper analyses the implications of simultaneous output, price and wage adjustment at finite rates. Firms use a Marshallian-type output adjustment which uses available information about notional magnitudes. With this adjustment structure, the Walrasian equilibrium is globally stable. This...

This paper uses input-output and census data from 1961, 1971 and 1981 to decompose the employment changes during each decade into several sources. Decompositions are performed at three levels of aggregation by occupation and by industry. The main influences on employment levels have been changes...

Many finance questions require the predictive distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term...