The Observed Effects of Money Laundering for Jamaica - Caribbean ...

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The additive values of these two variables were used as fraud and drug crimes necessitate a lot of money laundering(Unger 2005). Therefore if there is an increase of any of these two crimes will imply that there is an increase of money laundering within Jamaica. Furthermore the large amounts of illegal funds gained from these activities need to be legitimize hence the need to launder these funds. EMPIRICAL MODEL Augmented Dickey Fuller Firstly, to established the presence of a unit root in this dataset by performing the Augmented Dickey Fuller test. The ADF test will be performed using the following regression model for each variable. t− 1 n ∑ s= 1 ΔY = constan t + ∂Y + _ T + ∂ ΔY + ε t Where Yt is the time series, εt is the residual and T is the time trend. H : δ = 0 0 H : δ ≠ 0 1 The null hypothesis is that the variables have a unit root, if we have a unit root the dataset is not stationary. This follows the distribution of 1 t−s t 28
the tau statistic. This test is done until we have stationary dataset, whether at differences or at levels. Here it is important that the dataset is stationary as a stationary dataset means that the mean and he variance is not time varying and we can use it to make predictions for the future of a study that is carried out during a different time period. Cointegration and Error Correction Mechanism In order to avoid the result of spurious regression by regressing non- stationary time series on non-stationary series determine whether money laundering has any long run effect on economic growth, this paper adopts the concept of cointegration. If the variables are cointegrated the model will yield long run relations among the variables. Firstly, using Ordinary Least Square, regress the time series. After which we shall subject each time series to separate unit root. Secondly, we subject the error term of the OLS regression to unit root analysis and if it is stationary at levels then we conclude that the regression is a cointegrating regression and not spurious, which makes the OLS regression it long run estimates. Thirdly using the error correction mechanism we use the following model Δ t = α0 + α1ΔX t + α2Δu t−1 Y + e t 29
Page 1 and 2: The Observed Effects of Money Laund
Page 3 and 4: The objectives of this paper are to
Page 5 and 6: through an outside party to conceal
Page 7 and 8: larger elements that are easier to
Page 9 and 10: There is a vast amount of literatur
Page 11 and 12: speak of is the impact of money lau
Page 13 and 14: MONEY LAUNDERING IN THE CARIBBEAN:
Page 15 and 16: In the Caribbean the low risks of d
Page 17 and 18: existence of offshore banks and tax
Page 19 and 20: to be used by launderers by Peter L
Page 21 and 22: launderers purchasing the lottery w
Page 23 and 24: productivity and therefore which wi
Page 25 and 26: EMPIRICAL The effects of money laun
Page 27: the skills necessary for increased
Page 31 and 32: The above results of the p-value ar
Page 33 and 34: R-squared 0.413033 Mean dependent v
Page 35 and 36: financial institutions will be defr
Page 37 and 38: • DEMAND FOR MONEY 37
Page 39 and 40: The relationship between money laun
Page 41 and 42: R-squared 0.779530 Mean dependent v
Page 43 and 44: Jamaica. The results obtained are c
Page 45 and 46: The goal of this study is to give a
Page 47 and 48: Recently the Jamaican Government ha
Page 49 and 50: money laundering which is not deeme
Page 51 and 52: Bartlett, B.L. (2002). “The Negat
Page 53: Unger, B., Seigal, M., Ferwerd, J.,

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