scholarly journals Forecasting Baden‐Württemberg's GDP Growth: MIDAS Regressions versus Dynamic Mixed‐Frequency Factor Models

2020 ◽  
Author(s):  
Konstantin Kuck ◽  
Karsten Schweikert
Keyword(s):  
Frequency Factor ◽  
Factor Models ◽  
Gdp Growth ◽  
Mixed Frequency

NOWCASTING GDP GROWTH IN A SMALL OPEN ECONOMY

2021 ◽  
Vol 256 ◽  
pp. 127-161
Author(s):  
Massimiliano Marcellino ◽  
Vasja Sivec
Keyword(s):  
Open Economy ◽  
Small Open Economy ◽  
Factor Models ◽  
Economic Conditions ◽  
Dynamic Factor ◽  
Gdp Growth ◽  
Learning Models ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Machine Learning Models

Nowcasting, that is, forecasting the current economic conditions, is a key ingredient for decision making, but it is complex, even more so for a small open economy, due to the higher volatility of its GDP. In this paper, we review the required steps, taking Luxembourg as an example. We consider both standard and alternative indicators, used as inputs in several nowcasting methods, including various factor and machine learning models. Overall, mixed frequency dynamic factor models and neural networks perform well, both in absolute terms and in relative terms with respect to a benchmark autoregressive model. The gains are larger during problematic times, such as the financial crisis and the recent Covid period.

FORECASTING WITH MIXED-FREQUENCY FACTOR MODELS IN THE PRESENCE OF COMMON TRENDS

2013 ◽  
Vol 19 (4) ◽  
pp. 753-775 ◽  
Author(s):  
Peter Fuleky ◽  
Carl S. Bonham
Keyword(s):  
Factor Model ◽  
Low Frequency ◽  
Frequency Factor ◽  
Factor Models ◽  
Common Trends ◽  
Mixed Frequency ◽  
Stochastic Trends ◽  
Forecasting Performance ◽  
The Common ◽  
Transfer Of Information

We analyze the forecasting performance of small mixed-frequency factor models when the observed variables share stochastic trends. The indicators are observed at various frequencies and are tied together by cointegration so that valuable high-frequency information is passed to low-frequency series through the common factors. Differencing the data breaks the cointegrating link among the series and some of the signal leaks out to the idiosyncratic components, which do not contribute to the transfer of information among indicators. We find that allowing for common trends improves forecasting performance over a stationary factor model based on differenced data. The “common-trends factor model” outperforms the stationary factor model at all analyzed forecast horizons. Our results demonstrate that when mixed-frequency variables are cointegrated, modeling common stochastic trends improves forecasts.

scholarly journals Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 56-90
Author(s):  
Monica Defend ◽  
Aleksey Min ◽  
Lorenzo Portelli ◽  
Franz Ramsauer ◽  
Francesco Sandrini ◽  
...  
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Estimation Method ◽  
Selection Criterion ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Model Selection Criterion ◽  
Mixed Frequency Data

This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns.

High-Frequency Factor Models and Regressions

2019 ◽  
Author(s):  
Yacine Ait-Sahalia ◽  
Ilze Kalnina ◽  
Dacheng Xiu
Keyword(s):  
High Frequency ◽  
Frequency Factor ◽  
Factor Models

scholarly journals Forecasting GDP growth using mixed-frequency models with switching regimes

2015 ◽  
Vol 31 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Fady Barsoum ◽  
Sandra Stankiewicz
Keyword(s):  
Gdp Growth ◽  
Mixed Frequency ◽  
Switching Regimes

scholarly journals Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data

2019 ◽  
Vol 78 (1) ◽  
pp. 19-35
Author(s):  
Heiner Mikosch ◽  
◽  
Laura Solanko ◽  
Keyword(s):  
Gdp Growth ◽  
Frequency Data ◽  
Mixed Frequency ◽  
Mixed Frequency Data

Forecasting China's GDP growth using dynamic factors and mixed-frequency data

2017 ◽  
Vol 66 ◽  
pp. 132-138 ◽  
Author(s):  
Yu Jiang ◽  
Yongji Guo ◽  
Yihao Zhang
Keyword(s):  
Gdp Growth ◽  
Frequency Data ◽  
Mixed Frequency ◽  
Dynamic Factors ◽  
Mixed Frequency Data
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