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DC Field | Value | Language |
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dc.contributor.advisor | Denton, F.T. | en_US |
dc.contributor.advisor | Magee, L. | en_US |
dc.contributor.advisor | Butterfield, D.W. | en_US |
dc.contributor.author | Tahir, Rizwan | en_US |
dc.date.accessioned | 2014-06-18T16:39:40Z | - |
dc.date.available | 2014-06-18T16:39:40Z | - |
dc.date.created | 2010-07-22 | en_US |
dc.date.issued | 1993-09 | en_US |
dc.identifier.other | opendissertations/2807 | en_US |
dc.identifier.other | 3769 | en_US |
dc.identifier.other | 1406235 | en_US |
dc.identifier.uri | http://hdl.handle.net/11375/7535 | - |
dc.description.abstract | <p>Providing reasonable explanation for the business cycle has been the research agenda for many economists since the early 20th century. Most attempts to explain the sources of macroeconomic fluctuations attribute the variability in output and prices to only a few sources, sometimes to only one. While a significant amount of theoretical research has been undertaken on the business cycle, relatively little empirical work has been conducted that attempts to measure the qualitative importance of various sources of macroeconomic variability.</p> <p>Macroeconometric models are typically detailed enough to allow a decomposition of output variability into a variety of constituent shocks. In these models, all macroeconomic fluctuation can be traced ultimately to equation residuals or exogenous variables. Ray Fair (1988) has undertaken stochastic simulations using his macroeconometric model of the U.S. economy to estimate the quantitative importance of various sources of variability in U.S. output and prices.</p> <p>We have adopted Fair's methodology for application in a Canadian framework, using a quarterly Canadian macroeconometric model constructed specifically for the purpose. Fair's original technique and two variants of it are used. Bootstrapping, a distribution-free method, is used in addition to Fair's method, which assumes normally distributed shocks. In order to take into account shocks associated with exogenous variables, we have followed Fair and added autoregressive equations to the model. Using all these procedures, we have estimated the contribution of all the equation shocks in the model to the variances of three major endogenous variables, real GDP, the rate of change in the GDP price deflator and the rate of unemployment.</p> <p>The results shows that, in the case of the variance of real GDP, export and import equation shocks dominate and account for more than 55 percent of the variation over all quarters in eight-quarter simulations. Consumer expenditures on services, business fixed investment in machinery and equipment, consumer expenditures on nondurables, total imports, and consumer expenditures on semi-durables are among the other major contributors. The contributions of all the major contributors vary over the simulation period. While the contribution of consumer expenditures on durables, for example, increases from about -2 percent to about 10 percent, the contribution of consumer expenditures on services decreases from between 7 and 10 percent (depending on the method used) to about 3 or 4 percent. Since out simulations are limited to only eight periods, it is impossible to determine whether or not these contributions would stabilize over a longer simulation period.</p> <p>The variances of the rate of change in the GDP deflator (PDOT) and the rate of unemployment (UNR) are similar to each other in that there are only a few major sources contributing to their variances and, unlike real GDP, the major contributors are the same across simulation periods, regardless of the method. Their own shocks account for 100 percent of their variation in the first quarter of the simulation period, but a lesser proportion thereafter. The other main contributors are: domestic and U.S. rates of interest, the exchange rate, and the total private sector component of real GDP (in the case of PDOT) and total consumption, total investment and total exports and imports (in the case of UNR) While some contributions increase significantly over the simulation period, others decrease. Thus a source that is dominant in the short run may be unimportant in the longer run and vice versa.</p> | en_US |
dc.subject | Economics | en_US |
dc.subject | Economics | en_US |
dc.title | An Econometric Analysis of the Short-Run Variability in Canadian Output and Prices | en_US |
dc.type | thesis | en_US |
dc.contributor.department | Economics / Economic Policy | en_US |
dc.description.degree | Doctor of Philosophy (PhD) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
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fulltext.pdf | 4.67 MB | Adobe PDF | View/Open |
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