Money growth, uncertainty and macroeconomic activity: a multivariate GARCH analysis

Cronin, David; Kelly, Robert J.; Kennedy, Bernard

doi:10.1007/s10663-010-9124-5

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…A common approach is to produce proxies using the estimated conditional volatility of any variable. This approach only pays attention to the individual uncertainty of the macroeconomic variables, such as inflation uncertainty, exchange rate uncertainty, interest rate uncertainty, money growth uncertainty, and stock index uncertainty (Serven 1998, Goel and Ram 2001, Bredin and Fountas 2005, Kumo 2006, Cronin et al 2011, Guglielminetti 2013).…”

Section: Introductionmentioning

confidence: 99%

Measuring the optimal macroeconomic uncertainty index for Turkey

Erdem

Yamak

2016

EKON ANAL

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The aim of this study is to calculate the optimal macroeconomic uncertainty index for the Turkish economy. The data used in the study are quarterly and cover the period 2002-2014. In this study the index is formed based on the small structural macroeconomic model. The study uses three important econometric processes. First, the model is estimated separately using generalized method of moments (GMM), seemingly unrelated regressions (SUR), and ordinary least squares (OLS). Secondly, the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is applied as an optimization algorithm. The BFGS algorithm calibrates the model using GMM, SUR, and OLS parameter estimations of the benchmark parameters. Next, the index variables are weighted under the estimated optimal coefficients and, finally, are aggregated to produce the optimal macroeconomic uncertainty index.

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Section: Introductionmentioning

confidence: 99%

Measuring the optimal macroeconomic uncertainty index for Turkey

Erdem

Yamak

2016

EKON ANAL

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“…They find that, in general, greater inflation uncertainty leads to higher inflation, while greater output uncertainty leads to lower output. Cronin et al (2011) fit a GARCH-M model to the real money stock of the US to derive a measure of economic uncertainty. Kumo (2006) derives single-variable estimates of economic uncertainty from the real effective exchange rate, the real interest rate, producer price inflation, the terms of trade, and the GDP growth rate respectively, for South Africa, to analyse the effect that uncertainty has on aggregate private fixed investment.…”

Section: Introductionmentioning

confidence: 99%

Estimating an optimal macroeconomic uncertainty index for Australia

Erdem

Tawadros

2022

Applied Economics

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In this article, an optimal macroeconomic uncertainty index is constructed for the Australian economy. This index is derived from a small structural macroeconomic model. The structural model is first estimated using GMM to extract the parameter estimates, which are then used to initialise maximum likelihood techniques in order to obtain the optimal coefficient values for the relevant variables. The relevant variables are then weighted by the obtained optimal coefficients and, finally, are aggregated to produce the optimal macroeconomic uncertainty index for Australian economy. The empirical results show that the uncertainty index constructed is a good indicator of the optimal economic conditions in Australia, providing a useful tool to assist the Reserve Bank of Australia in its decision-making process.

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“…Starting from the position of classical Knightian uncertainty 1 (Knight, 1921 ), the first class of uncertainty indicators uses macroeconomic volatility as a proxy for macro uncertainty (e.g. Asgharian et al, 2015 ; Cronin et al, 2011 ). Other authors quantify uncertainty using forecasting disagreement among economic agents (e.g.…”

Section: Introductionmentioning

confidence: 99%

Economic Policy Uncertainty Index Meets Ensemble Learning

2021

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We utilize a battery of ensemble learning techniques [ensemble linear regression (LM), random forest], as well as two gradient boosting techniques [Gradient Boosting Decision Tree and Extreme Gradient Boosting (XGBoost)] to scrutinize the possibilities of enhancing the predictive accuracy of Economic Policy Uncertainty (EPU) index. Applied to a data-rich environment of the Newsbank media database, our LM and XGBoost assessments mostly outperform the other two ensemble learning procedures, as well as the original EPU index. Our LM and XGBoost estimates bring EPU closer to the stylized facts of uncertainty than other uncertainty estimates. LM and XGBoost indicators are more countercyclical and have more pronounced leading properties. We find that EPU is more strongly correlated to financial volatility measures than to consumers' assessments of uncertainty. This corroborates that the media place a much higher weight on the financial sector than on the economic issues of consumers. Further on, we considerably widen the scope of search terms included in the calculation of EPU index. Using ensemble learning techniques on such a rich set of keywords, we mostly manage to outperform the standard EPU in terms of correlation with standard uncertainty proxies. We also find that the predictive accuracy of EPU index can be considerably increased using a more diversified set of uncertainty-related terms than the original EPU framework. Our estimates perform much better in a monthly setting (targeting the industrial production growth) than targeting quarterly GDP growth. This speaks in favor of uncertainty as a purely short-term phenomenon.

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Money growth, uncertainty and macroeconomic activity: a multivariate GARCH analysis

Abstract: Money growth, Uncertainty, MV GARCH, Monetary analysis,

Cited by 6 publications

References 19 publications

Measuring the optimal macroeconomic uncertainty index for Turkey

Measuring the optimal macroeconomic uncertainty index for Turkey

Estimating an optimal macroeconomic uncertainty index for Australia

Economic Policy Uncertainty Index Meets Ensemble Learning

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