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(ProQuest: ... denotes formulae omitted.)1.INTRODUCTIONThe aim of this study is to estimate the size of the shadow economy for 80 countries over the period 1975-2012 using the currency demand approach (CDA). The present study intends to contribute to the empirical literature in threefold. First, we highlight the need to incorporate a measure of macroeconomic uncertainty in computing the size of the shadow economy. The problem is that the impacts of macroeconomic uncertainty are not limited to the formal economy, but also extended to the shadow economy. It is argued that individuals tend to participate in the shadow economy in the period of economic hardship by under reporting their actual income as households are uncertain of the future income levels; by improving their personal finances in order to compensate for lost income (Schneider & Kearney, 2013); and by increasing their precautionary saving balances by holding more currency.Second, we modelled the macroeconomic uncertainty index by means of dynamic factor model. To reflect uncertainty about macroeconomic conditions, an array of indicators consisting of real GDP per capita, interest rate, total tax revenue to GDP ratio, inflation rate, and exchange rate were used to construct the index. As opposed to other quantitative measures, the macroeconomic uncertainty index is designed to be a better measure with greater coverage of a macroeconomic perspective. Factor models can deal with various variables without lack of degrees of freedom that usually encountered in regression analysis. Moreover, the idiosyncratic movements which contain measurement error and local shocks can be eliminated, yielding a more reliable signal for policy makers.Third, we use the pooled mean group (PMG) estimator proposed by Pesaran et al. (1999) to examine the long-run relationships between currency demand and its other determinants for the nine regions, namely; East Asia and Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Middle East and North, Africa (MENA), South Asia (SAS), Sub-Saharan Africa (SSA), high income OECD, high income non-OECD, and euro area (EURO) regions This estimator has advantage over the standard estimators since it allows the heterogeneity across countries in the short-run dynamics but imposes homogeneity restrictions in the long-run coefficients. However, empirical studies using larger panel (for instance, Alm and Embaye, 2013; Elgin and Oztunali, 2012 and Schneider et al., 2010) estimated the shadow economy without considering the heterogeneity across countries. In the case of panel data the estimation of the size of the shadow economy should be carried out by accounting for cross-country heterogeneity.This is because each region has its own characteristics and dynamics, especially concerning the differences of tax policies and socio-economic characteristics. For instance, based on the estimates of the size of shadow economy by Schneider et al. (2010), Latin America and the Caribbean (LAC), Sub-Saharan Africa (SSA), and Europe and Central Asia (ECA) have the largest estimates at 41.1%, 40.2%, and 38.8% and the OECD (high income) countries have the smallest size of about 17.1%, indicating that there is great heterogeneity between regions. More specifically, each region is also characterized by considerable heterogeneity where the size of the shadow economy in LAC (the largest share of the shadow economy) varies from 25.3% in Argentina to 66.1% in Bolivia; while in high income OECD countries the size of the shadow economy ranges from the lowest of 8.5% in Switzerland to 27.2% in Poland.The paper is organized as follows. In the next section we discuss the currency demand model use to estimate the size of the shadow economy. In section 3, we discuss the pooled mean group estimator used to estimates the currency demand model. In section 4, we discuss the empirical results. The last section contains our conclusion. …

SOME NEW ESTIMATES OF SHADOW ECONOMY FOR 80 COUNTRIES USING POOLED MEAN GROUP ESTIMATOR