scholarly journals Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth)

2021 ◽  
Author(s):  
Roberto S. Mariano ◽  
Suleyman Ozmucur
Keyword(s):  
Predictive Performance ◽  
Gdp Growth ◽  
Mixed Frequency ◽  
Forecasting Models

scholarly journals Predictive performance of international COVID-19 mortality forecasting models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joseph Friedman ◽  
Patrick Liu ◽  
Christopher E. Troeger ◽  
Austin Carter ◽  
Robert C. Reiner ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Absolute Error ◽  
Decision Makers ◽  
Government Interventions ◽  
World Region ◽  
Mortality Forecasting ◽  
Forecasting Models ◽  
Pandemic Response ◽  
Alternative Scenarios ◽  
Absolute Percent Error

AbstractForecasts and alternative scenarios of COVID-19 mortality have been critical inputs for pandemic response efforts, and decision-makers need information about predictive performance. We screen n = 386 public COVID-19 forecasting models, identifying n = 7 that are global in scope and provide public, date-versioned forecasts. We examine their predictive performance for mortality by weeks of extrapolation, world region, and estimation month. We additionally assess prediction of the timing of peak daily mortality. Globally, models released in October show a median absolute percent error (MAPE) of 7 to 13% at six weeks, reflecting surprisingly good performance despite the complexities of modelling human behavioural responses and government interventions. Median absolute error for peak timing increased from 8 days at one week of forecasting to 29 days at eight weeks and is similar for first and subsequent peaks. The framework and public codebase (https://github.com/pyliu47/covidcompare) can be used to compare predictions and evaluate predictive performance going forward.

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 A GENETIC PROGRAMMING APPROACH FOR ESTIMATING ECONOMIC SENTIMENT IN THE BALTIC COUNTRIES AND THE EUROPEAN UNION

2021 ◽  
Vol 27 (1) ◽  
pp. 262-279
Author(s):  
Oscar Claveria ◽  
Enric Monte ◽  
Salvador Torra
Keyword(s):  
European Union ◽  
Fitness Function ◽  
Predictive Performance ◽  
Programming Approach ◽  
Gdp Growth ◽  
The European Union ◽  
Baltic Countries ◽  
Non Linear ◽  
Out Of Sample ◽  
The Baltic

In this study, we introduce a sentiment construction method based on the evolution of survey-based indicators. We make use of genetic algorithms to evolve qualitative expectations in order to generate country-specific empirical economic sentiment indicators in the three Baltic republics and the European Union. First, for each country we search for the non-linear combination of firms’ and households’ expectations that minimises a fitness function. Second, we compute the frequency with which each survey expectation appears in the evolved indicators and examine the lag structure per variable selected by the algorithm. The industry survey indicator with the highest predictive performance are production expectations, while in the case of the consumer survey the distribution between variables is multi-modal. Third, we evaluate the out-of-sample predictive performance of the generated indicators, obtaining more accurate estimates of year-on-year GDP growth rates than with the scaled industrial and consumer confidence indicators. Finally, we use non-linear constrained optimisation to combine the evolved expectations of firms and consumers and generate aggregate expectations of of year-on-year GDP growth. We find that, in most cases, aggregate expectations outperform recursive autoregressive predictions of economic growth.

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

scholarly journals A Multi-Country BVAR Model for the External Sector

2020 ◽  
Vol 79 (4) ◽  
pp. 98-112
Author(s):  
Olga Korotkikh ◽  
Keyword(s):  
Monetary Policy ◽  
Euro Area ◽  
Predictive Performance ◽  
Gdp Growth ◽  
Financial Indicators ◽  
Wide Range ◽  
The Usa ◽  
External Sector ◽  
Macro Variables ◽  
Growth Forecasts

This paper describes a multi-country BVAR model developed and used by the Monetary Policy Department of the Bank of Russia. The model makes it possible to build coordinated scenario forecasts for the main macro-variables of the USA, the euro area, and China. The simultaneous modelling for the three economies makes it possible to take into account multi-country interactions of the variables and, thus, improve the predictive performance of the model compared to VAR analogues intended for individual countries. The model is based on the deviations of the variables from their potential values, which enhances GDP growth forecasts compared to a non-detrended design. A wide range of macroeconomic and financial indicators in the model makes the forecast of overall inflation more accurate against simpler benchmarks.

scholarly journals Large mixed-frequency VARs with a parsimonious time-varying parameter structure

2021 ◽  
Author(s):  
Thomas B Götz ◽  
Klemens Hauzenberger
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Computational Complexity ◽  
Common Factor ◽  
Time Varying ◽  
Frequency Vector ◽  
Mixed Frequency ◽  
Forecasting Models ◽  
Time Varying Parameter ◽  
Joint Dynamics

Summary In order to simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural change, we introduce a time-varying parameter mixed-frequency vector autoregression (VAR). Time variation enters in a parsimonious way: only the intercepts and a common factor in the error variances can vary. Computational complexity therefore remains in a range that still allows us to estimate moderately large VARs in a reasonable amount of time. This makes our model an appealing addition to any suite of forecasting models. For eleven U.S. variables, we show the competitiveness compared to a commonly used constant-coefficient mixed-frequency VAR and other related model classes. Our model also accurately captures the drop in the gross domestic product during the COVID-19 pandemic.

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