ENERGY SAVING

Implementing Energy Saving Measures

Implementing Energy Saving Measures

Introduction

There is currently much interest in the potential for public policies to reduce energy consumption because of concerns about global climate change linked with the combustion of fossil fuels. Basic economic theory suggests that if the price of energy relative to other goods rises, the energy intensity of the economy will fall as a result of a series of behavioral changes: people would turn down their thermostats and drive slower; they would replace their furnaces and cars with more efficient models available on the market; and over the long run, the pace and direction of technological change would be affected, so that the menu of capital goods available for purchase would contain more energy-efficient choices.

As the world's economy is going through some very difficult times, governments and businesses around the world find it ever more important to have accurate assessments of the future. This will enable them to do their planning and will facilitate better decision making. This project involves a large company which deals with real estate. The management of the company wants to arrive at an accurate low in complexity inexpensive and time-saving forecasting model that could be used to predict sales of houses for a particular area of a large city.

Aim and objective

The aim of this project is to apply appropriate time series forecasting methods in order to help the management of the company to choose an appropriate forecasting model that could be used to predict house sales in the short term with a reasonable level of accuracy. The company has provided quarterly data which covers the period from the beginning of 1989 until the middle of 2007. The requirements of work are specified in the next section.

Discussion

Autoregressive Integrated Moving Average (ARIMA) model was introduced by Box and Jenkins (hence also known as Box-Jenkins model) in 1960s for forecasting a variable. An effort is made in this paper to develop an ARIMA model for Total houses sold per quarter and to apply the same in forecasting Total houses sold for the three leading years. ARIMA method is an extrapolation method for forecasting and, like any other such method, it requires only the historical time series data on the variable under forecasting (Abry, 1994). Among the extrapolation methods, this is one of the most sophisticated methods, for it incorporates the features of all such methods, does not require the investigator to choose the initial values of any variable and values of various parameters a priori and it is robust to handle any data pattern. As one would expect, this is quite a difficult model to develop and apply as it involves transformation of the variable, identification of the model, estimation through non-linear method, verification of the model and derivation of forecasts. In what follows, we first explain the ARIMA model, then develop the same for Total houses sold using quarterly data during 1989 to 2007 and finally apply the same to forecast the values of the variable during the future 3 years. 

Theoretical Basis of Time-Series Analysis:

A time series is a set of ...