Boosting nonlinear predictability of macroeconomic time series

We apply the boosting estimation method in order to investigate to what extent and at what horizons macroeconomic time series have nonlinear predictability that comes from their own history. Our results indicate that the U.S. macroeconomic time series have more exploitable nonlinear predictability than previous studies have found. On average, the most favorable out-of-sample performance is obtained via a two-stage procedure, where a conventional linear prediction model is fitted first and the boosting technique is applied to build a nonlinear model for its residuals.     

Indonesian healthy living intentions: Segmentation study insights

Obesity is a global epidemic. The very rapid growth rates of obesity prevalence observed in developing countries is alarming. Segmentation is under applied in social marketing and, when it is applied, it is based primarily on demographic data in samples drawn from developed nations. The current study adopts psychographic segmentation to understand lifestyle attitudes and intentions towards exercise and weight loss among an Indonesian sample. TwoStep cluster analysis (n = 499) identified 4 distinct segments (At risk, Conscious healthy, Overweight, and Tryers). The results of this study demonstrate that different groups exist in the larger population and that consideration of these groups may assist social marketers in creating service/intervention offerings that meet the needs of a broader range of people in the market, thus extending penetration of campaigns that address obesity.     

Modelling nonlinearity in U.S. Gross national product 1889–1987

This paper considers modelling the annual logarithmed per capita gross national product of the United States in 1889–1987. Some authors have suggested that the parameters of the process generating the data have changed over time but formal parameter constancy tests do not support this argument. The series turns out to be nonlinear and can be adequately characterized by an exponential smooth transition autoregressive model. For comparison, a detrended series is also considered, found nonlinear and modelled using a logistic smooth transition autoregressive model. The behaviour of the estimated models is discussed, and it is seen that nonlinearity is needed to describe the response of the process to exceptionally large exogenous shocks. The properties of the models are further investigated by forecasting several years ahead, and the forecasts are compared with those from other linear and nonlinear models.     

Local environment as a pitfall in the performance measurement of multi-site operations

The purpose of this paper is to disseminate an empirical observation that the external environment influences the data from performance metrics in a multi-site field service setting. The observation is made from real performance measurement data from 29 local units of a machinery manufacturer. We propose that the heterogeneity of local environments increases Type I and Type II errors in evaluating performance against targets and reduces the comparability of local units. These findings imply distortions in the managerial control effect of performance measurement and in decision making on interventions and improvements. As a practical implication, this study emphasizes the need for contextual understanding in the proper use of performance measurement numbers.     

Shadow Price Approach to Total Factor Productivity Measurement: With an Application to Finnish Grass-Silage Production

This paper explores an intermediate route between the Fisher and the Malmquist productivity indexes so as to minimize data requirements and assumptions about economic behavior of production units and their production technology. Assuming quantity data of inputs and outputs and the behavioral hypothesis of allocative efficiency, we calculate the exact value of the Fisher ideal productivity index using implicit shadow prices revealed by the choice of input–output mix. The approach is operationalized by means of a nonparametric data envelopment analysis (DEA) model. Empirical application to Finnish grass silage farms suggests that the Malmquist and the Fisher productivity indices yield similar results when averaged over firms, but there can be major differences in the results of the two approaches at the level of individual firms.     

Nonparametric Efficiency Analysis under Price Uncertainty: A First-Order Stochastic Dominance Approach

This paper extends the nonparametric approach to efficiency analysis to deal with uncertainty of input-output prices. We generalize the notion of economic efficiency to derive necessary and sufficient first-order stochastic dominance (FSD) efficiency conditions. Interestingly, the FSD conditions include as limiting cases the traditional conditions for economic efficiency and technical efficiency. Furthermore, we propose empirical tests for these FSD conditions, which require minimal assumptions concerning the preferences of the decision-maker and the statistical distribution of the prices. From operational point of view, the FSD conditions can be tested empirically using standard mathematical programming techniques. An empirical application to the Dutch electricity distribution sector illustrates the approach.     

Eco-efficiency analysis of consumer durables using absolute shadow prices

We develop a method for eco-efficiency analysis of consumer durables that is based on Data Envelopment Analysis (DEA). In contrast to previous product efficiency studies, we consider the measurement problem from the policy perspective. The innovation of the paper is to measure efficiency in terms of absolute shadow prices that are optimized endogenously within the model to maximize efficiency of the good. Thus, the efficiency measure has a direct economic interpretation as a monetary loss due to inefficiency, expressed in some currency unit. The advantages as well as technical differences between the proposed approach and the traditional production-side methods are discussed in detail. We illustrate the approach by an application to eco-efficiency evaluation of Sport Utility Vehicles.     

Forecasting the consumption of alcoholic beverages in Finland: A box-Jenkins approach

In this report the results of identifying, estimating and diagnostically checking short-term rational distributed lag (transfer function) models for forecasting the consumption of alcoholic beverages other than beer in Finland are reviewed and compared with some other Scandinavian studies. The output of models is the logarithm of the monthly consumption of alcoholic beverages while the only specified input is the logarithm of the price index of alcoholic beverages. Other explanatory variables are included in the ARIMA noise. The importance of the price variable is demonstrated by comparing ex post predictions obtained using different methods.