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Several methods have been devised to deal with the problem of temporal disaggregation of economic time series (a) either when related series are available or (b) when only aggregate figures exist. In this article, we propose a statistical model-based approach to temporal disaggregation of economic time series by related series. The proposed approach is performed in two stages. In the first stage, we evaluate a preliminary estimate of the disaggregated series using a regression model for the disaggregated series and related series observed in the same frequency. The preliminary estimate of disaggregated series obtained in the first step is not consistent with aggregate figures. To ensure consistency we propose in the second stage, the use of a modified benchmarking approach based on signal extraction (Hillmer and Trabelsi, 1987;Trabelsi and Hillmer, 1990) to adjust the preliminary estimate of disaggregate series. The approach developed here is used for Seasonally Adjusted (SA) and Not Seasonally Adjusted (NSA) data. A comparison with previous temporal disaggregation methods has been done.

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The main purpose of this paper is to analyze the sensitivity of tropospheric ozone and particulate matter concentrations to changes in local scale meteorology with the aid of meteorological variables (wind speed, wind direction, relative humidity, solar radiation and temperature) and intensity of traffic using hourly concentration of NOX, which are measured in three different locations in Tunis, (i.e. Gazela, Mannouba and Bab Aliwa). In order to quantify the impact of meteorological conditions and precursor concentrations on air pollution, a general model was developed where the logarithm of the hourly concentrations of O3 and PM10 were modeled as a sum of non-linear functions using the framework of Generalized Additive Models (GAMs). Partial effects of each predictor are presented. We obtain a good fit with R² = 85% for the response variable O3 at Bab Aliwa station. Results show the aggregate impact of meteorological variables in the models explained 29% of the variance in PM10 and 41% in O3. This indicates that local meteorological condition is an active driver of air quality in Tunis. The time variables (hour of the day, day of the week and month) also have an effect. This is especially true for the time variable “month” that contributes significantly to the description of the study area.