Forecasting Canadas GDP Essays

Forecasting Canadas GDP Essays Forecasting Canadas GDP Essay Forecasting Canadas GDP Essay Two recessions can be observed from the Time Series plot from above, one in 1980 lasting up until 1982. The other recession was in 1989 lasting for 3 years till 1992. Canada had changed from a country producing and exporting mainly primary products to one that is increasingly producing and exporting manufactured goods. In the 1980s, machinery and equipment joined automotive products among the countrys leading exports; at the same time, the importance of natural resource product declined. Canada was hard hit by the recession of the early 1980s, with interest rates, unemployment, and inflation all running higher than in the United States. The effects of the recession on minerals and manufacturing were especially severe. By the end of 1982, all mining operations in the Yukon were closed, and throughout the country, more than 70,000 of 115,000 miners were unemployed. The economy recovered during the mid-1980s, and Canadas economic growth rate was amongst the highest of OECD countries during 1984-86. The recession of the early 1990s was an economic recession that hit much of the world in 1990-91. The Canadian economy had been affected by the gulf war. A Value Added Tax, the free trade agreement with the U.S and a tight monetary policy that culminated in a serious recession. The recession that occurs in the third quarter of 2008 was another economic recession that hit most of the world. : Between the third quarter of 2008 and the third quarter of last year, the countrys real GDP in Canada fell 3. 3 per cent, compared with 3. 7 per cent in the United States and bigger declines in Europe and Japan. 3 The Gross Domestic Product (GDP) in Canada expanded 0. 30 percent in the third quarter of 2010 over the previous quarter. From 1961 until 2010, Canadas average quarterly GDP Growth was 0.84 percent reaching an historical high of 3. 33 percent in December of 1963 and a record low of -1. 80 percent in March of 2009. Canadas economy is diversified and highly developed. Measures of Forecasting Errors The mean absolute deviation (MAD) measures forecast accuracy by averaging the magnitudes of the forecast errors (the absolute value of errors). The MAD is the same in the units as the original series and provides an average size of the miss regardless of direction. Equation 1: Mean Absolute Deviation MAD= The mean squared error (MSE) is another method for evaluating a forecasting technique. Each error or residual is squared; these are then summed and divided by the number of observations. This approach penalizes large forecasting errors, since errors are squared. This is important as the technique that produces moderate errors may well be preferable to one that usually has small errors but occasionally yields extremely large ones. Equation 2: Mean Squared Error MSE= The mean absolute percentage error (MAPE) is computed by finding the absolute error in each period, dividing this by the actual observed value for that period, and averaging these absolute percentage errors. The result is then multiplied by 100 and expressed as a percentage. This approach is useful when the error relative to the respective size of the time series value is important in evaluating the accuracy of the forecast. The MAPE is especially useful when the Yt values are large. The MAPE has no units of measurements (it is a percentage) and can be used to compare the accuracy of the same or different techniques on two entirely different series. MAPE cannot be calculated if any of the Yt are zero. Equation 3: Mean Absolute Percentage Error MAPE= To determine whether a forecasting method is biased (consistently forecasting low or high). The mean percentage error (MPE) is used in these cases. It is computed by finding the error in each period, dividing this by the actual value for that period, and then averaging these percentage errors. The result is typically multiplied by 100 and expressed as a percentage. If the forecasting approach is unbiased, the MPE will produce a number that is close to zero. If the result is a large negative percentage, the forecasting method is consistently overestimating. If the result is a large positive percentage, the forecasting method is consistently underestimating.