Essays about: "Mixed-Frequency Models"
Showing result 1 - 5 of 6 essays containing the words Mixed-Frequency Models.
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1. Nowcasting Private Consumption in Switzerland using a Mixed-Frequency Dynamic Factor Model with High-Frequency Data
University essay from Handelshögskolan i Stockholm/Institutionen för nationalekonomiAbstract : Various empirical papers have provided evidence that dynamic factor models and the use of high- and mixed-frequency data yield good estimates for nowcasts. This thesis uses the dynamic factor model framework of Giannone et al. (2008) with daily, weekly, and monthly data to nowcast private consumption in Switzerland. READ MORE
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2. Nowcasting U.S. Inflation: The Role of Online Retail Prices
University essay from Handelshögskolan i Stockholm/Institutionen för nationalekonomiAbstract : We examine whether high-frequency online retail price data contributes to more accurate nowcasts of the U.S. inflation rate, as given by the monthly change in the Consumer Price Index, when other commonly considered variables for predicting inflation already have been taken into account. READ MORE
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3. Importance of daily data in long horizon inflation forecasting - a MIDAS approach
University essay from Göteborgs universitet/Institutionen för nationalekonomi med statistikAbstract : We examine the accuracy of forecast models for the monthly Euro area inflation, focusing on the MIDAS approach. We compare two mixed frequency models with four low frequency models, using fourteen mixed frequency variables sampled at daily or monthly frequency. READ MORE
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4. COMPARING THE FORECASTING PERFORMANCE OF VAR, BVAR AND U-MIDAS
University essay from Uppsala universitet/Statistiska institutionenAbstract : ThispaperaimstocomparetheforecastingperformanceofthewidelyusedVARandBayesian VAR model to the unrestricted MIDAS regression. The models are tested on a real-time macroeconomic data set ranging from 2000 to 2015. READ MORE
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5. MIDAS : Forecasting quarterly GDP using higher-frequency data
University essay from Uppsala universitet/Statistiska institutionen; Uppsala universitet/Statistiska institutionenAbstract : We forecast US GDP sampled quarterly over horizons ranging from one quarter to three years. Using AR-MIDAS models we study three lag polynomials: the Almon lag, the exponential Almon lag and the beta lag, and nine macroeconomic variables, sampled weekly or monthly. Our benchmark model is an AR(1) and we compare forecast errors using RMSE. READ MORE
