scholarly journals Forecasting Economic Time Series Using Score-Driven Dynamic Models with Mixed-Data Sampling

2018 ◽  
Author(s):  
Paolo Gorgi ◽  
Siem Jan Koopman ◽  
Mengheng Li
Keyword(s):  
Time Series ◽  
Dynamic Models ◽  
Mixed Data ◽  
Data Sampling ◽  
Economic Time Series ◽  
Mixed Data Sampling ◽  
Economic Time

Mixed Frequency Models

2018 ◽  
Author(s):  
Eric Ghysels
Keyword(s):  
Mixed Data ◽  
Space Representation ◽  
Data Sampling ◽  
Economic Time Series ◽  
Mixed Frequency ◽  
State Space Representation ◽  
Econometric Methods ◽  
Mixed Data Sampling ◽  
Economic Time ◽  
Mixed Frequency Data

The majority of econometric models ignore the fact that many economic time series are sampled at different frequencies. A burgeoning literature pertains to econometric methods explicitly designed to handle data sampled at different frequencies. Broadly speaking these methods fall into two categories: (a) parameter driven, typically involving a state space representation, and (b) data driven, usually based on a mixed-data sampling (MIDAS)-type regression setting or related methods. The realm of applications of the class of mixed frequency models includes nowcasting—which is defined as the prediction of the present—as well as forecasting—typically the very near future—taking advantage of mixed frequency data structures. For multiple horizon forecasting, the topic of MIDAS regressions also relates to research regarding direct versus iterated forecasting.

Mathematical and Quantitative Methods: Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series

2013 ◽  
Vol 51 (4) ◽  
pp. 1190-1192
Author(s):  
Timo Teräsvirta
Keyword(s):  
Time Series ◽  
Quantitative Methods ◽  
Heavy Tails ◽  
Dynamic Models ◽  
Economic Time Series ◽  
Corpus Christi ◽  
Economic Time ◽  
Heavy Tailed ◽  
The University ◽  
Changes Over Time

Timo Terasvirta of Aarhus University reviews, “Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series” by Andrew C. Harvey. The Econlit abstract of this book begins: “Presents a theory for a class of nonlinear time series models that can deal with dynamic distributions, with an emphasis on models in which the conditional distribution of an observation may be heavy-tailed and the location and/or scale changes over time. Discusses statistical distributions and asymptotic theory; location; scale; location/scale models for nonnegative variables; dynamic kernel density estimation and time-varying quantiles; multivariate models, correlation, and association; and further directions in dynamic models. Harvey is Professor of Econometrics at the University of Cambridge and Fellow of Corpus Christi College, the Econometric Society, and the British Academy.”

Measuring the Information Content of the Beige Book: A Mixed Data Sampling Approach

2008 ◽  
Author(s):  
Michelle T. Armesto ◽  
Ruben Hernandez-Murillo ◽  
Michael Owyang ◽  
Jeremy M. Piger
Keyword(s):  
Information Content ◽  
Mixed Data ◽  
Data Sampling ◽  
Mixed Data Sampling ◽  
Sampling Approach

scholarly journals COVID-19 Outbreak and CO2 Emissions: Macro-Financial Linkages

2020 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Julien Chevallier
Keyword(s):  
Co2 Emissions ◽  
Fossil Fuels ◽  
Mixed Data ◽  
Energy Information Administration ◽  
Time Varying ◽  
Data Sampling ◽  
Quantitative Finance ◽  
Financial Variables ◽  
Mixed Data Sampling ◽  
The One

In the Dynamic Conditional Correlation with Mixed Data Sampling (DCC-MIDAS) framework, we scrutinize the correlations between the macro-financial environment and CO2 emissions in the aftermath of the COVID-19 diffusion. The main original idea is that the economy’s lock-down will alleviate part of the greenhouse gases’ burden that human activity induces on the environment. We capture the time-varying correlations between U.S. COVID-19 confirmed cases, deaths, and recovered cases that were recorded by the Johns Hopkins Coronavirus Center, on the one hand; U.S. Total Industrial Production Index and Total Fossil Fuels CO2 emissions from the U.S. Energy Information Administration on the other hand. High-frequency data for U.S. stock markets are included with five-minute realized volatility from the Oxford-Man Institute of Quantitative Finance. The DCC-MIDAS approach indicates that COVID-19 confirmed cases and deaths negatively influence the macro-financial variables and CO2 emissions. We quantify the time-varying correlations of CO2 emissions with either COVID-19 confirmed cases or COVID-19 deaths to sharply decrease by −15% to −30%. The main takeaway is that we track correlations and reveal a recessionary outlook against the background of the pandemic.

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