This paper investigates the extent to which modern DSGE models, which feature local currency pricing, home bias, nontraded goods and incomplete markets, can generate nonlinear real exchange rate dynamics that are consistent with those found in the time series literature using data from the current ‡oating period. Our key …ndings are as follows. First, if the solution to the DSGE model is approximated to the …rst order, then linearity tests that utilize univariate autoregressions of the real exchange rate su¤er from an omitted variables problem, which leads them to overestimate the true incidence of nonlinearity. Consequently, studies that fail to control for this problem may spuriously …nd evidence of nonlinearities in the data, despite the fact that the data generating process may be linear. Second, we propose a strategy that can largely eliminate this distortion. Finally, we …nd that DSGE models solved using higher order approximations are capable of generating true structural nonlinearities in real exchange rates both asymptotically and in short samples.JEL Classi…cation: C15 C32 F41 F47

We investigate the time series properties of both …ltered and un…ltered real exchange rate series produced by DSGE models that feature local currency pricing, home bias, nontraded goods, and incomplete markets. Detrended series produced by several speci…cations approach the empirically observed volatility, although none of the models generate enough persistence. Conversely, several models produce un…ltered series that possess the same degree of persistence as the data, but none can match its volatility.JEL Classi…cation: F41 F47 C15 C32

Background: Advances in medical domain has led to an increase of clinical data production which offers enhancement opportunities for clinical research sector. In this paper, we propose to expand the scope of Electronic Medical Records in the University Malaya Medical Center (UMMC) using different techniques in establishing interoperability functions between multiple clinical departments involving diagnosis, screening and treatment of breast cancer and building automatic systems for clinical audits as well as for potential data mining to enhance clinical breast cancer research in the future.Results: Quality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the inhouse EMR system used at UMMC, called i-Pesakit©. The completion of the i-Pesakit© Breast Cancer Module requires management of clinical data electronically, integration of clinical data from multiple internal clinical departments towards setting up of a research focused patient data governance model. The 14 QIF steps were performed in four main phases involved in this study which are (i) initial considerations regarding host setting, (ii) creating structure for implementation, (iii) ongoing structure once implementation begins, and (iv) improving future applications. The architectural framework of the module incorporates both clinical and research needs that comply to the Personal Data Protection Act.

A recent innovation in modeling exchange rates has been the use of nonlinear techniques such as threshold autoregressive models and its smooth transition variants. This paper investigates the smooth transition autoregressive (STAR) modeling strategy in an application to real exchange rates. The key findings are as follows. First, using the methodology advocated by Teräsvirta (1994), we find evidence of nonlinear dynamics for several of the spot dollar real exchange rates using monthly data on five of the G7 countries. However, once estimated, we find that the STAR specification is appropriate for only one of the three exchange rate series indicated to be an ESTAR process. Moreover, using simulations, we show that the underlying methodology used to detect nonlinearities in the data exhibit substantial size biases, which we attribute to influential observations. We also investigate an alternative nonlinear specification and find that we can model the dollar-sterling and the dollar-lira real exchange rates better as an open-loop TAR process instead of a SETAR process. JEL Classification: F30

This paper investigates Threshold Autoregressive (TAR) models that contain a limited number of observations in some regimes. Simulations show that within the context of the real exchange rate literature, parameter estimates exhibit signi…cant small sample bias even with long time series data. These distortions create substantial power losses in attempting to identify values of coe¢ cients from data.