An estimation of residential water demand using co-integration and error correction tec hniques

In this paper short- and long-run price elasticities of residential water demand are estimated using co-integration and error-correction methods. Unit root tests reveal that water use series and series of other variables affecting use are non-stationary. However, a long-run co-integrating relationship is found in the water demand model, which makes it possible to obtain a partial correction term and to estimate an error correction model. Using monthly time-series observations from Seville, Spain, we find that the price-elasticity of demand is estimated as around -0.1 in the short run and -0.5 in the long run. These results are robust to the use of different specifications.

Tourism in the Canary Islands: Forecasting Using Several Seasonal Time Series Models

This paper deals with the analysis of the number of tourists travelling to the Canary Islands by means of using different seasonal statistical models. Deterministic and stochastic seasonality is considered. For the latter case, we employ seasonal unit roots and seasonally fractionally integrated models. As a final approach, we also employ a model with possibly different orders of integration at zero and the seasonal frequencies. All these models are compared in terms of their forecasting ability in an out-of-sample experiment. The results in the paper show that a simple deterministic model with seasonal dummy variables and AR(1) disturbances produce better results than other approaches based on seasonal fractional and integer differentiation over short horizons. However, increasing the time horizon, the results cannot distinguish between the model based on seasonal dummies and another using fractional integration at zero and the seasonal frequencies.

Inventory performance under staggered deliveries and autocorrelated demand

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordProduction plans often span a whole week or month, even when independent production lots are completed every day and service performance is tallied daily. Such policies are said to use staggered deliveries, meaning that the production rate for multiple days are determined at a single point in time. Assuming autocorrelated demand, and linear inventory holding and backlog costs, we identify the optimal replenishment policy for order cycles of length P. With the addition of a once-per-cycle audit cost, we optimize the order cycle length P∗ via an inverse-function approach. In addition, we characterize periodic inventory costs, availability, and fill rate. As a consequence of staggering deliveries, the inventory level becomes cyclically heteroskedastic. This manifests itself as ripples in the expected cost and service levels. Nevertheless, the cost-optimal replenishment policy achieves a constant availability by using time-varying safety stocks; this is not the case with suboptimal constant safety stock policies, where the availability fluctuates over the cycle

An Estimation of Residential Water Demand Using Co-Integration and Error Correction Techniques

In this paper short- and long-run price elasticities of residential water demand are estimated using co-integration and error-correction methods. Unit root tests reveal that water use series and series of other variables affecting use are non-stationary. However, a long-run co-integrating relationship is found in the water demand model, which makes it possible to obtain a partial correction term and to estimate an error correction model. The empirical application uses monthly time-series observations from Seville (Spain). The price-elasticity of demand is estimated as around -0.1 in the short run and -0.5 in the long run. These results are robust to the use of different specifications.seasonal unit roots; residential water demand; price elasticity; time-series; co-integration; Error Correction Model

Empirical safety stock estimation based on kernel and GARCH models

Supply chain risk management has drawn the attention of practitioners and academics alike. One source of risk is demand uncertainty. Demand forecasting and safety stock levels are employed to address this risk. Most previous work has focused on point demand forecasting, given that the forecast errors satisfy the typical normal i.i.d. assumption. However, the real demand for products is difficult to forecast accurately, which means that-at minimum-the i.i.d. assumption should be questioned. This work analyzes the effects of possible deviations from the i.i.d. assumption and proposes empirical methods based on kernel density estimation (non-parametric) and GARCH(1,1) models (parametric), among others, for computing the safety stock levels. The results suggest that for shorter lead times, the normality deviation is more important, and kernel density estimation is most suitable. By contrast, for longer lead times, GARCH models are more appropriate because the autocorrelation of the variance of the forecast errors is the most important deviation. In fact, even when no autocorrelation is present in the original demand, such autocorrelation can be present as a consequence of the overlapping process used to compute the lead time forecasts and the uncertainties arising in the estimation of the parameters of the forecasting model. Improvements are shown in terms of cycle service level, inventory investment and backorder volume. Simulations and real demand data from a manufacturer are used to illustrate our methodology

An Estimation of Residential Water Demand Using Co-integration and Error Correction Techniques

The purpose of this paper is to measure the short- and long-run effect ofthe price of water on residential water use. Unit root tests reveal that water use series and series of other variables affecting use are non-stationary. However, a long-run co-integrating relationship is found in the demand model, which makes possible to obtain a partial correction term and to estimate an error correction model. The empirical application uses monthly time-series observations from Seville (Spain). The price-elasticity of demand is estimated as around -0.1 in the short run and -0.5 in the long run. These results are robust to the use of different specifications.seasonal unit roots, residential water demand, price elasticity, time-series, co-integration, Error Correction Model.

Empirical testing for bubbles during the inter-war European hyperinflations

In this thesis, I undertake an empirical search for the existence of price and exchange rate bubbles during the inter-war European hyperinflations of Germany, Hungary and Poland. Since the choice of an appropriate policy to control inflation depends upon the true nature of the underlying process generating the inflation, the existence or non-existence of inflationary bubbles has important policy implications. If bubbles do exist, positive action will be required to counter the public's self-fulfilling expectation of a price surge. Hyperinflationary episodes have been chosen as my case study because of the dominant role that such expectations play in price determination. In the literature, there are frequently expressed concerns about empirical research into bubbles. The existence of model misspecification and the nonlinear dynamics in the fundamentals under conditions of regime switching may lead to spurious conclusions concerning the existence of bubbles. Furthermore, some stochastic bubbles may display different collapsing properties and consequently appear to be linearly stationary. Thus, the evidence against the existence of bubbles may not be reliable. In my thesis, I attempt to tackle the above empirical problems of testing for the existence of bubbles using advances in testing procedures and methodologies. Since the number of bubble solutions is infinite in the rational expectations framework, I adopt indirect tests, rather than direct tests, for the empirical study. From the findings of my empirical research, the evidence for stationary specification errors and the nonlinearity of the data series cannot be rejected, but the evidence for the existence of price and exchange rate bubbles is rejected for all the countries under study. It leads to the conclusion that the control of the inter-war European hyperinflations was attributable to control of the fundamental processes, since the dynamics of prices and exchange rates for these countries might not be driven by self-fulfilling expectations

An Estimation of Residential Water Demand Using Co-Integration and Error Correction Techniques

In this paper short- and long-run price elasticities of residential water demand are estimated using co-integration and error-correction methods. Unit root tests reveal that water use series and series of other variables affecting use are non-stationary. However, a long-run co-integrating relationship is found in the water demand model, which makes it possible to obtain a partial correction term and to estimate an error correction model. The empirical application uses monthly time-series observations from Seville (Spain). The price-elasticity of demand is estimated as around -0.1 in the short run and -0.5 in the long run. These results are robust to the use of different specifications

An Estimation of Residential Water Demand Using Co-Integration and Error Correction Techniques

In this paper short- and long-run price elasticities of residential water demand are estimated using co-integration and error-correction methods. Unit root tests reveal that water use series and series of other variables affecting use are non-stationary. However, a long-run co-integrating relationship is found in the water demand model, which makes it possible to obtain a partial correction term and to estimate an error correction model. The empirical application uses monthly time-series observations from Seville (Spain). The price-elasticity of demand is estimated as around -0.1 in the short run and -0.5 in the long run. These results are robust to the use of different specifications

Dynamic econometric modeling of the U.S. wheat grain market

Structural-time series models have not gained much ground in commodity market modeling despite the overwhelming popularity of time series approaches in forecasting and dynamic analyses. This dissertation contributes by applying developments in seasonal cointegration and structural-time series analysis (e.g., Zellner and Palm (1974); Hsiao (1997); Lee (1992); Franses and Kunst (1999); Ghysels and Osborn, 2001) to the study of agricultural commodity markets. The focus is on three research themes. The first theme investigates the role of cointegration and seasonal cointegration for market data, an issue considered timely because most applications assume deterministic seasonal components. The second issue breaks new ground in agricultural commodity modeling by introducing a new dynamic simultaneous equation model (DSEM) that accounts for seasonal cointegration. Lastly, the research compares the out-of-sample forecasting performance and impulse responses of four multi-equation models for the U.S. wheat market. The forecasting comparisons apply recent developments on testing for differences in mean-squared-errors. The study adopts a structural model for the U.S. wheat market and estimates four econometric specifications: a vector error-correction model without seasonal cointegration (VECM), a VECM with seasonal cointegration (SVECM), a DSEM with cointegration (CDSEM), and a DSEM with seasonal cointegration (SCDSEM). The conclusions may be summarized as follows. First, quarterly data in the U.S. wheat market (1975:03-1999:04) have seasonal unit roots, therefore, a VECM or DSEM should be specified. Second, in a forecasting context, seasonally cointegrated VECMs perform uniformly better that their nonseasonal counterpart. DSEM with seasonal cointegration, however, perform better than VECMs at longer forecast horizons. Lastly, the impulse response analysis and dynamic multiplier comparisons lead to one salient conclusion, omission of seasonal cointegration components when significant generates unexpected response functions and dynamic multipliers. Of particular interest for future research is an assessment of the small sample properties of impulse response functions for structural-time series models with seasonal cointegration. From a more pure economic perspective, a similar structural-time series analysis to other agricultural markets seems timely given the new finding that these models may outperform other multiple time series models that are often used in empirical work