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The purpose of this book is to introduce the reader to mechanisms useful for detection and avoidance of money-laundering activities (MLAs) and terrorist financing and suggest improvements to existing MLAs where appropriate. Money laundering may occur in every country. The significant factor is to diagnose the illegal MLA and apply regulations to mitigate them. To meet this objective, managers of financial institutions need to train their employees about anti-money laundering (AML) processes and how to diagnose and prevent money laundering. AML activities can also affect the financial systems of a country. "Money laundering destabilizes the foundation of a nation's financial system by reducing tax revenues and impeding fair competition by ultimately disrupting economic development" (World Compliance, 2008). MLAs can create a big gap between income classes. Money laundering can also decrease banks' or financial institutions' credibility. "In practice, criminals are trying to disguise the origins of money obtained through illegal activities so that it looks like it was obtained from legal sources" (Layton, 2005). This book may be of special interest to financial managers in the private and public sector. It also may be a useful guide for those involved in international financial transactions.

Money laundering. --- Terrorism --- Finance. --- logistic regression --- model building --- model diagnostics --- multiple regression --- regression model --- simple linear regression --- statistical inference --- time series regression

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Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointe.

Econometric models. --- Econometrics. --- Accuracy and precision. --- Asymptotic distribution. --- Autocorrelation. --- Autoregressive conditional heteroskedasticity. --- Autoregressive model. --- Bayesian statistics. --- Bayesian. --- Bernoulli distribution. --- Bias of an estimator. --- Calculation. --- Central limit theorem. --- Chow test. --- Cointegration. --- Conditional expectation. --- Conditional probability distribution. --- Confidence interval. --- Confidence region. --- Correlation and dependence. --- Correlogram. --- Count data. --- Cross-sectional data. --- Cross-sectional regression. --- Distribution function. --- Dummy variable (statistics). --- Econometric model. --- Empirical distribution function. --- Equation. --- Error term. --- Estimation. --- Estimator. --- Exogeny. --- Exploratory data analysis. --- F-distribution. --- F-test. --- Fair coin. --- Forecast error. --- Forecasting. --- Granger causality. --- Heteroscedasticity. --- Inference. --- Instrumental variable. --- Joint probability distribution. --- Law of large numbers. --- Least absolute deviations. --- Least squares. --- Likelihood function. --- Likelihood-ratio test. --- Linear regression. --- Logistic regression. --- Lucas critique. --- Marginal distribution. --- Markov process. --- Mathematical optimization. --- Maximum likelihood estimation. --- Model selection. --- Monte Carlo method. --- Moving-average model. --- Multiple correlation. --- Multivariate normal distribution. --- Nonparametric regression. --- Normal distribution. --- Normality test. --- One-Tailed Test. --- Opportunity cost. --- Orthogonalization. --- P-value. --- Parameter. --- Partial correlation. --- Poisson regression. --- Probability. --- Probit model. --- Quantile. --- Quantity. --- Quasi-likelihood. --- Random variable. --- Regression analysis. --- Residual sum of squares. --- Round-off error. --- Seemingly unrelated regressions. --- Selection bias. --- Simple linear regression. --- Skewness. --- Standard deviation. --- Standard error. --- Stationary process. --- Statistic. --- Student's t-test. --- Sufficient statistic. --- Summary statistics. --- T-statistic. --- Test statistic. --- Time series. --- Type I and type II errors. --- Unit root test. --- Unit root. --- Utility. --- Variable (mathematics). --- Variance. --- Vector autoregression. --- White test.