Modelling currency demand: the case of the euro

The purpose of this study is to estimates the size of the shadow economy for 80 countries from nine regions spanning the period 1975-2012 based on Tanzi-type currency demand approach (CDA). This study contributes to the literature in three distinct ways. First, we augment CDA regression with a macroeconomic uncertainty index (MUI). Second, the construction of the uncertainty index is based on the dynamic factor model (DFM). Third, the pooled mean group (PMG) estimator allows in capturing the heterogeneity across countries in the short-run dynamics but imposing restrictions in the long-run parameters. The results confirm the existence of the longrun equilibrium relationship among the variables examined. All coefficients show expected signs along with statistical significance. More importantly, the macroeconomic uncertainty index variable show positive relationship, suggesting that public tend to hold more currency in an uncertain macroeconomic environment. In addition, we observe that developing regions (ranging from 19.9% to 37.3%) exhibit relatively large size of the shadow economy. On the contrary, developed regions have a considerable smaller estimate (ranging from 13.7% to 19.0%) of the size of shadow economy. On average, the world estimate of the shadow economy as a percentage of GDP is about 23.1%. Keywords: Shadow Economy; Currency Demand; Macroeconomic Uncertainty; Pooled Mean Group.

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'Here, Dollars, Dollars...' - Estimating Currency Demand and Worldwide Currency Substitution

SSRN Electronic Journal ◽

10.2139/ssrn.231859 ◽

2000 ◽

Cited By ~ 11

Author(s):

Brian M. Doyle

Keyword(s):

Currency Substitution ◽

Currency Demand

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Raising the Accuracy of Shadow Economy Measurements

Revista Hacienda Pública Española ◽

10.7866/hpe-rpe.21.4.3 ◽

2021 ◽

Vol 239 (4) ◽

pp. 71-125

Author(s):

Vicente Ríos ◽

◽

Antonio Gómez ◽

Pedro Pascual ◽

◽

...

Keyword(s):

Bayesian Model ◽

Latent Variable ◽

Bayesian Model Averaging ◽

Shadow Economy ◽

Electricity Consumption ◽

Model Averaging ◽

Hidden Sector ◽

Multiple Indicators ◽

Econometric Methods ◽

Currency Demand

This article estimates the size of the shadow economy in a Spanish region (Navarre) for the period 1986- 2016. To this end, we employ indirect macro-econometric methods such as the Currency Demand approach, Electricity Consumption (Physical Input) methods and the multiple indicators multiple causes (MIMIC) approach. A differential feature of our empirical analysis is that we incorporate various methodological innovations (e..g. Bayesian Model Averaging, a Time-Varying Parameter model, normalization of the latent variable) to refine and increase the measurement accuracy of each of the indirect methods considered. The temporal pattern of the shadow economy’s size that emerges from the different approaches is similar, which suggests that the estimates obtained are robust and capture the underlying dynamics of the hidden sector. After quantifying the shadow economy, we analyze its determinants by means of Bayesian Model Averaging techniques. We find that the evolution of the shadow economy in Navarre can be explained by a small and robust set of factors, specifically the tax burden, the share of employment in the construction sector, the inflation rate, euro area membership and the ratio of currency outside the banks to M1.

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Industrial China and its impact on Informality.

International Journal of Management Excellence ◽

10.17722/ijme.v8i2.879 ◽

2017 ◽

Vol 8 (2) ◽

pp. 895-906

Author(s):

Kevin Michael Fleary

Keyword(s):

Conceptual Model ◽

Informal Economy ◽

Turning Point ◽

National Development ◽

Policy Decision ◽

Decision Makers ◽

Central Theme ◽

Demand Model ◽

Economic Changes ◽

Currency Demand

The informal economy has been the topic of debate for some time now and given its significant effect on the revenue, economists and policy decision makers alike have sought greater control to their dismay. This can be contributed to the lack of informal statistics on China, fueling assumptions that have not born results. To address the problem, this paper seeks to estimate the size of China’s informal economy using the Currency Demand Model. The central theme forwarded in this research is that China is experiencing its Lewis Turning Point and the industry changes positively impact the growth of the underground economy. Given this statement, the authors present a conceptual model that depicts the growth and movement of the informal economy in relation to China’s economic changes. As a means to provide intuition for decision makers as China transition to the 13th National Development Plan.

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Modelling currency demand in a small open economy within a monetary union

Economic Modelling ◽

10.1016/j.econmod.2018.05.004 ◽

2018 ◽

Vol 74 ◽

pp. 88-96 ◽

Cited By ~ 4

Author(s):

António Rua

Keyword(s):

Monetary Union ◽

Open Economy ◽

Small Open Economy ◽

Currency Demand

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Currency Demand in the Underground Economy

The Curse of Cash ◽

10.2307/j.ctvc77m90.8 ◽

2017 ◽

pp. 58-79

Keyword(s):

Underground Economy ◽

Currency Demand

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The Comparison of the Shadow Economy in Turkey and European Countries

Comparative Economics and Regional Development in Turkey - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-4666-8729-5.ch004 ◽

2016 ◽

pp. 73-105 ◽

Cited By ~ 2

Author(s):

Coskun Karaca

Keyword(s):

Informal Economy ◽

Shadow Economy ◽

Reliable Data ◽

European Countries ◽

Measuring Methods ◽

Currency Demand ◽

Policy Proposals

As informal activities are considered as a crime, that kind of activities are being carried out secretly and their detection is difficult in most cases. Along with difficulties in determining the size of informal economy exactly, recently developed models and opportunities to reach reliable data enable making realistic estimations in regard to shadow economy. This study benefits from 11 different studies estimating informality in European countries and Turkey by using physical input, currency demand, DYMIMIC, and MIMIC methods. Common conclusion acquired from these studies is that informality rate in Turkey is higher than EU15 countries and EU13 countries –except for Hungary, Cyprus, Latvia, Croatia and Bulgaria. In addition to the comparison of these data, the reasons of the emergence of informal economy, measuring methods, and policy proposals in order to hamper informality in Turkey are also discussed.

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