Forecasting Canadian Time Series with the New Keynesian Model

The authors document the out-of-sample forecasting accuracy of the New Keynesian model for Canada. They estimate their variant of the model on a series of rolling subsamples, computing out-of-sample forecasts one to eight quarters ahead at each step. They compare these forecasts with those arising from simple vector autoregression (VAR) models, using econometric tests of forecasting accuracy. Their results show that the forecasting accuracy of the New Keynesian model compares favourably with that of the benchmarks, particularly as the forecasting horizon increases. These results suggest that the model could become a useful forecasting tool for Canadian time series. The authors invoke the principle of parsimony to explain their findings.Business fluctuations and cycles; Economic models; Econometric and statistical methods

Forecasting Canadian Time Series with the New-Keynesian Model

This paper documents the out-of-sample forecasting accuracy of the New Keynesian Model for Canada. We repeatedly estimate our variant of the model on a series of rolling subsamples, forecasting out-of-sample one to eight quarters ahead at each step. We then compare these forecasts to those arising from simple VARs, using econometric tests of forecasting accuracy. Our results show that the forecasting accuracy of the New Keynesian model compares favourably to that of the benchmarks, particularly as the forecasting horizon increases. These results suggest that the model can become a useful forecasting tool for Canadian time series. The principle of parsimony is invoked to explain our findings.New Keynesian Model, Forecasting accuracy

Forecasting Canadian Time Series With the New-Keynesian Model

This paper documents the out-of-sample forecasting accuracy of the New Keynesian Model for Canada. We estimate our variant of the model on a series of rolling subsamples, computing out-of-sample forecasts one to eight quarters ahead at each step. We compare these forecasts to those arising from simple vector autoregression (VAR) models, using econometric tests for forecasting accuracy. Our results show that the forecasting accuracy of the New Keynesian model compares favorably to that of the benchmarks, particularly as the forecasting horizon is increased. These results suggest that the model could become a useful forecasting tool for Canadian time series.

Semi-Automatic Method to Assist Expert for Association Rules Validation

Abstract-In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process

Contribution of MODIS image to hydrologic and erosion modeling

Erosion is the most dangerous phenomenon of environmental and economic threat to arable land. By FAO. 35 % of Tunisian land are threatened by water erosion and varies regionally.  Quantification and estimation of soil loss by water erosion is now essential to install the best management practices. In our study, we used a continuous physical based simulation version of a hydrological and water erosion model ANSWERS-2000. The aim of this study is to investigate the introduction of land use parameters extracted by MODIS image. This study is focused on El Azire watershed, is characterized by a moderate Mediterranean climate and a high spatial heterogeneity of soil and land use proprieties. Describing, monitoring, and predicting land-use and land-cover change in the watershed is a difficult process. However, the usefulness of MODIS image with their daily temporal resolution and spatial resolutions of 250 m and 500 m can make utilization of continuous physical-based models easier for great scale watersheds. A sensitivity analysis was conducted on each application such that the variability in erosion map output and can be assessed and incorporated into the interpretation of results with an acceptable level of confidence