Economic design of an X chart for short-run production

The aim of this paper is to present an economical design of an X chart for a short-run production. The process mean starts equal to mu(0) (in-control, State I) and in a random time it shifts to mu(1) > mu(0) (out-of-control, State II). The monitoring procedure consists of inspecting a single item at every m produced ones. If the measurement of the quality characteristic does not meet the control limits, the process is stopped, adjusted, and additional (r - 1) items are inspected retrospectively. The probabilistic model was developed considering only shifts in the process mean. A direct search technique is applied to find the optimum parameters which minimizes the expected cost function. Numerical examples illustrate the proposed procedure. (C) 2009 Elsevier B.V. All rights reserved.

Capacity utilisation and profitability: A decomposition of short-run profit efficiency

The principal aim of this paper is to measure the amount by which the profit of a multi-input, multi-output firm deviates from maximum short-run profit, and then to decompose this profit gap into components that are of practical use to managers. In particular, our interest is in the measurement of the contribution of unused capacity, along with measures of technical inefficiency, and allocative inefficiency, in this profit gap. We survey existing definitions of capacity and, after discussing their shortcomings, we propose a new ray economic capacity measure that involves short-run profit maximisation, with the output mix held constant. We go on to describe how the gap between observed profit and maximum profit can be calculated and decomposed using linear programming methods. The paper concludes with an empirical illustration, involving data on 28 international airline companies. The empirical results indicate that these airline companies achieve profit levels which are on average US$815m below potential levels, and that 70% of the gap may be attributed to unused capacity. (C) 2002 Elsevier Science B.V. All rights reserved.

Agriculture and short run macroeconomics

Neste trabalho, formula-se um modelo macroeconômico de curto prazo a fim de se derivar as interações entre os setores agrícola e não-agrícola por ocasião da aplicação de políticas de estabilização. As variáveis exógenas são mudanças nas políticas fiscal, monetária e cambial e nos preços internacionais. As variáveis endógenas explicitamente analisadas são renda real para cada setor e preços relativos. Os principais resultados são: (a) os preços relativos tendem a variar quando as variáveis exógenas variam; (b) a produção agrícola e os preços relativos da gricultura tendem a se reduzir face a políticas fiscais e monetárias expansivas mesmo quando a elasticidade-renda de demanda para produtos agrícolas for zero; (c) embora o efeito inflacionario de políticas monetárias e fiscais expansivas seja maior quando a elasticidade de oferta de produtos agrícolas é baixa, os preços nominais da agricultura tendem a crescer no máximo tanto quanto os preços nominais não-agrícolas. Os efeitos de diversas pressuposições a respeito da elasticidades de demanda e de oferta sobre os resultados do modelo são também derivados.

Short-Run Strategies For Attracting Foreign Direct Investment

This paper empirically investigates the effectiveness and feasibility of two FDI policies, fiscal incentives and deregulation, aimed at improving the attractiveness of a country in the short run. Using disaggregated data on sales by US MNEs’ foreign affiliates in 43 developed and developing countries over the 1982-1994 period, results show that the provision of fiscal incentives or the deregulation of the labour market would exert a positive impact on total FDI. Given the drawbacks frequently associated with the use of incentive packages, economy-wide policies which ease firing procedures and reduce severance payments would certainly be the best policy option. This paper also highlights the different aggregation and omitted variable biases that have affected results of previous studies and provides some support to recent theoretical models of FDI by showing that third country effects and spatial interdependence influence respectively the location of export-platform FDI and vertical FDI.

Short-Run Strategies For Attracting Foreign Direct Investment

This paper empirically investigates the effectiveness and feasibility of two FDI policies, fiscal incentives and deregulation, aimed at improving the attractiveness of a country in the short run. Using disaggregated data on sales by US MNEs’ foreign affiliates in 43 developed and developing countries over the 1982-1994 period, results show that the provision of fiscal incentives or the deregulation of the labour market would exert a positive impact on total FDI. Given the drawbacks frequently associated with the use of incentive packages, economy-wide policies which ease firing procedures and reduce severance payments would certainly be the best policy option. This paper also highlights the different aggregation and omitted variable biases that have affected results of previous studies and provides some support to recent theoretical models of FDI by showing that third country effects and spatial interdependence influence respectively the location of export-platform FDI and vertical FDI.

Short run and long run causality in time series: Inference

We propose methods for testing hypotheses of non-causality at various horizons, as defined in Dufour and Renault (1998, Econometrica). We study in detail the case of VAR models and we propose linear methods based on running vector autoregressions at different horizons. While the hypotheses considered are nonlinear, the proposed methods only require linear regression techniques as well as standard Gaussian asymptotic distributional theory. Bootstrap procedures are also considered. For the case of integrated processes, we propose extended regression methods that avoid nonstandard asymptotics. The methods are applied to a VAR model of the U.S. economy.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian in ation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in di¤erent measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.

Mixed models for short-run forecasting of electricity prices: Application for the Spanish market

Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every profit maximization strategy. In this article a new and very easy method to compute accurate forecasts for electricity prices using mixed models is proposed. The main idea is to develop an efficient tool for one-step-ahead forecasting in the future, combining several prediction methods for which forecasting performance has been checked and compared for a span of several years. Also as a novelty, the 24 hourly time series has been modelled separately, instead of the complete time series of the prices. This allows one to take advantage of the homogeneity of these 24 time series. The purpose of this paper is to select the model that leads to smaller prediction errors and to obtain the appropriate length of time to use for forecasting. These results have been obtained by means of a computational experiment. A mixed model which combines the advantages of the two new models discussed is proposed. Some numerical results for the Spanish market are shown, but this new methodology can be applied to other electricity markets as well