SENSITIVITY AND UNCERTAINTY ANALYSIS IN MRIO MODELLING; SOME EMPIRICAL RESULTS WITH REGARD TO THE DUTCH CARBON FOOTPRINT

Environmental multi-regional input–output (MRIO) models require large amounts of data that all have their specific uncertainties. This paper presents a sensitivity and uncertainty analysis in order to gain an understanding of the directions in which efforts should be made to reduce these uncertainties. The analyses were carried out for an MRIO model to calculate the Dutch carbon footprint. A sensitivity analysis of the technical coefficients showed that changes in the coefficients in the domestic blocks and in the Dutch import blocks had the largest effects on the calculated footprint. The uncertainty analysis consisting of a Monte Carlo simulation based on probability distributions around the model coefficients showed a relatively low degree of uncertainty in the total Dutch carbon footprint; uncertainties in the carbon emissions allocated to regions, sectors and products were larger. Both analyses showed that, in certain cases, it is justified to apply a partial MRIO analysis.     

Imputing relevant information from multi-day GPS tracers for retail planning and management using data fusion and context-sensitive learning

It is well known that the right location of shopping centres is of paramount importance. Unless stores succeed in attracting their own clientele, they rely to a large extent on the impulse behaviour of shoppers. To evaluate alternative locations, models of pedestrian behaviour may be useful. Modern technologies such as GPS and RFID offer new possibilities providing data on routes and stops, which are required as input for such models. An automatic interpretation of GPS tracers with respect to the activities being conducted could enhance the applicability of such technologies to retail management applications. This paper reviews this rapidly growing literature, and shows how automatic data imputation can be established by using Bayesian belief networks and how GPS traces can be fused with land use data of retail location.     

Pace,rhythm, and scope: process dependence in building a profitable multinational corporation

Many potential benefits of foreign expansion have been identified in the literature, yet empirical support that multinational firms perform better than domestic firms is mixed. This paper takes a longitudinal perspective and argues that how much a firm benefits from having foreign subsidiaries depends on its process of internationalization. We argue that a firm's capacity to absorb expansion is subject to constraints: some expansion patterns increase profitability less than others, owing to diseconomies of time compression. We hypothesize that the speed of internationalization, the spread of the geographical and product markets entered, and the irregularity of the expansion pattern negatively moderate a firm's increase in profitability resulting from international expansion. Model estimations based on panel data raised strong support for these predictions. Copyright © 2002 John Wiley & Sons, Ltd.     

International Competition and the Degree of Monopoly in Australian Manufacturing

Pricing behaviour in open economies is examined by adapting Kalecki's analysis of pricing in closed economies. The parameters of the resulting pricing equation are estimated using data for a sample of Australian manufacturing industries. These estimates are then used to calculate measures reflecting the degree of monopoly for an open economy, which are compared with the ratio of aggregate proceeds to aggregate direct costs as a measure reflecting the degree of monopoly for a closed economy.     

Differencing of random walks and near random walks

The traditional rationale for differencing time series data is to attain stationarity. For a nearly non-stationary first-order autoregressive process—AR (1) with positive slope parameter near unity—we were led to a complementary rationale. If one suspects near non-stationarity of the AR (1) process, if the sample size is ‘small’ or ‘moderate’, and if good one-step-ahead prediction performance is the goal, then it is wise to difference the data and treat the differences as observations on a stationary AR (1) process. Estimation by Ordinary Least Squares then appears to be at least as satisfactory as nonlinear least squares. Use of differencing for an already stationary process can be motivated by Bayesian concepts: differencing can be viewed as an easy way to incorporate non-diffuse prior judgement—that the process is nearly non-stationary—into one's analysis. Random walks and near random walks are often encountered in economics. Unless one's sample size is large, the same statistical analyses apply to either.     

An empirical comparison of bankruptcy models

Four types of bankruptcy prediction models based on financial statement ratios, cash flows, stock returns, and return standard deviations are compared. Based on a sample of bankruptcies from 1980 to 1991, results indicate that no existing model of bankruptcy adequately captures the data. During the last fiscal year preceding bankruptcy, none of the individual models may be excluded without a loss in explanatory power. If considered in isolation, the cash flow model discriminates most consistently two to three years before bankruptcy. By comparison, the ratio model is the best single model during the year immediately preceding bankruptcy. Quasi-jack-knifing procedures suggest that none of the models can reliably predict bankruptcy more than two years in advance.