scholarly journals A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?

2017 ◽  
Vol 26 (1) ◽  
Author(s):  
Francisco Corona ◽  
Graciela González-Farías ◽  
Pedro Orraca
2018 ◽  
pp. 17-22
Author(s):  
Larysa Zomchak ◽  
Anastasiia Rakova

Introduction. The short-term (quarterly) forecast of GDP is based on factor variables of the financial and non-financial sectors of the economy, indicators of foreign economic activity, indicators of economic activity, etc. Although the statistics of these indicators are available on a monthly basis, but its disclosure comes with a certain lag, and values over time can be reviewed and clarified. These data can be used to estimate the quarterly value of GDP before the official information about its empirical volume is published. Purpose. The article aims to forecast the quarterly real GDP of Ukraine by means of a dynamic factor model on the basis of the quarterly and monthly values of the main social and economic macro indicators of Ukraine. The method (methodology). To achieve the task, we have used the econometric methods of macroeconomic modelling, namely the dynamic factor model, the Kalman filter, the method of the main components, etc. Results. The forecast of GDP of Ukraine for the first two quarters of 2018 has been obtained with the help of a dynamic factor model. On the basis of comparison of the obtained forecast with the empirical values of Ukraine's GDP for the similar period, which is published by the Ministry of Finance of Ukraine, it has been proven the adequacy of the model and the high quality of the results has been concluded.


2018 ◽  
Vol 118 ◽  
pp. 281-317 ◽  
Author(s):  
Tao Ma ◽  
Zhou Zhou ◽  
Constantinos Antoniou

2018 ◽  
Vol 33 (5) ◽  
pp. 625-642 ◽  
Author(s):  
Mario Forni ◽  
Alessandro Giovannelli ◽  
Marco Lippi ◽  
Stefano Soccorsi

2021 ◽  
pp. 1-45
Author(s):  
Matteo Barigozzi ◽  
Matteo Luciani

Abstract We propose a new measure of the output gap based on a dynamic factor model that is estimated on a large number of U.S. macroeconomic indicators and which incorporates relevant stylized facts about macroeconomic data (co-movements, non-stationarity, and the slow drift in long-run output growth over time). We find that, (1) from the mid-1990s to 2008, the U.S. economy operated above its potential; and, (2) in 2018:Q4, the labor market was tighter than the market for goods and services. Because it is mainly data-driven, our measure is a natural complementary tool to the theoretical models used at policy institutions.


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