SIMFIRMS - SIMULATING THE SPATIAL DEMOGRAPHY OF FIRMS, WITH AN APPLICATION IN THE NETHERLANDS

Recently, there is an increasing demand in spatial planning for models based on the demographic concepts of birth and death of firms. This paper describes the structure of a spatial demographic simulation model of firms, and its application within the Netherlands. The model structure is essentially of the familiar demographic cohort component type, where an initial cohort of firms ages in a number of discrete steps, and where in each step additions and subtractions to and from the population are modelled using birth, death and migration components. Apart from the central processes of birth, death and migration, the type of economic activity and firm size are highly important for understanding firm behaviour over time. The paper describes the transition functions for each of the demographic components and for firm growth. In addition, some empirical results are presented of a number of model simulations in the Netherlands. The results were partly validated using observed economic demographic data. It is concluded that a substantial amount of work remains to be done in this new field. The model presented here has direct implications for the research agenda of the study of the demography of the firm.

Forecasting the equity risk premium: The role of technical indicators

Forecasting the Equity Risk Premium: The Role of Technical Indicators Abstract Do existing equity risk premium forecasts ignore useful information, such as technical indicators? Although academics have extensively used macroeconomic variables to forecast the U.S. equity risk premium, they have paid relatively little attention to the technical stock market indicators widely employed by practitioners. Our paper fills this gap by studying the forecasting ability of technical indicators relative to popular macroeconomic variables. We find that technical indicators display statistically and economically significant out-of-sample forecasting power and generate substantial utility gains; moreover, technical indicators tend to detect the typical decline in the equity risk premium near cyclical peaks, while macroeconomic variables more readily pick up the typical rise near cyclical troughs. In line with this cyclical behavior, utilizing information from both technical indicators and macroeconomic variables substantially increases out-of-sample forecasting performance relative to either alone. JEL classification: C53, C58, E32, G11, G12, G1