572 research outputs found
Testing the New Keynesian Phillips Curve Without Assuming Identification
We re-examine the evidence on the new Phillips curve model of Gali and Gertler (Journal of Monetary Economics 1999) using the conditional score test of Kleibergen (Econometrica 2005), which is robust to weak identification. In contrast to earlier studies, we find that US postwar data are consistent both with the view that inflation dynamics are forward-looking, and with the opposite view that they are predominantly backward-looking. Moreover, the labor share does not appear to be a relevant determinant of inflation. We show that this is an important factor contributing to the weak identification of the Phillips curve.
An Evaluation of Score Level Fusion Approaches for Fingerprint and Finger-vein Biometrics
Biometric systems have to address many requirements, such as large population
coverage, demographic diversity, varied deployment environment, as well as
practical aspects like performance and spoofing attacks. Traditional unimodal
biometric systems do not fully meet the aforementioned requirements making them
vulnerable and susceptible to different types of attacks. In response to that,
modern biometric systems combine multiple biometric modalities at different
fusion levels. The fused score is decisive to classify an unknown user as a
genuine or impostor. In this paper, we evaluate combinations of score
normalization and fusion techniques using two modalities (fingerprint and
finger-vein) with the goal of identifying which one achieves better improvement
rate over traditional unimodal biometric systems. The individual scores
obtained from finger-veins and fingerprints are combined at score level using
three score normalization techniques (min-max, z-score, hyperbolic tangent) and
four score fusion approaches (minimum score, maximum score, simple sum, user
weighting). The experimental results proved that the combination of hyperbolic
tangent score normalization technique with the simple sum fusion approach
achieve the best improvement rate of 99.98%.Comment: 10 pages, 5 figures, 3 tables, conference, NISK 201
Automatic Detection of Malware-Generated Domains with Recurrent Neural Models
Modern malware families often rely on domain-generation algorithms (DGAs) to
determine rendezvous points to their command-and-control server. Traditional
defence strategies (such as blacklisting domains or IP addresses) are
inadequate against such techniques due to the large and continuously changing
list of domains produced by these algorithms. This paper demonstrates that a
machine learning approach based on recurrent neural networks is able to detect
domain names generated by DGAs with high precision. The neural models are
estimated on a large training set of domains generated by various malwares.
Experimental results show that this data-driven approach can detect
malware-generated domain names with a F_1 score of 0.971. To put it
differently, the model can automatically detect 93 % of malware-generated
domain names for a false positive rate of 1:100.Comment: Submitted to NISK 201
Identification at the Zero Lower Bound
I show that the Zero Lower Bound (ZLB) on interest rates can be used to
identify the causal effects of monetary policy. Identification depends on the
extent to which the ZLB limits the efficacy of monetary policy. I propose a
simple way to test the efficacy of unconventional policies, modelled via a
`shadow rate'. I apply this method to U.S. monetary policy using a
three-equation SVAR model of inflation, unemployment and the federal funds
rate. I reject the null hypothesis that unconventional monetary policy has no
effect at the ZLB, but find some evidence that it is not as effective as
conventional monetary policy
Weak identification of forward-looking models in monetairy economics
Recently, single-equation GMM methods have become popular in the monetary economics literature, for estimating forward-looking models with rational expectations. We discuss a method for analyzing the empirical identification of such models that exploits their dynamic structure and the assumption of rational expectations. This allows us to judge the reliability of the resulting GMM estimation and inference and reveals the potential sources of weak identification. With reference to the New Keynesian Phillips curve of Galà and Gertler (1999) and the forward-looking Taylor rules of Clarida, GalÃ, and Gertler (2000), we demonstrate that the usual 'weak instruments' problem can arise naturally, when the predictable variation in inflation is small relative to unpredictable future shocks (news). Hence, we conclude that those models are less reliably estimated over periods when inflation has been under effective policy control
Anomaly Detection for imbalanced datasets with Deep Generative Models
Many important data analysis applications present with severely imbalanced
datasets with respect to the target variable. A typical example is medical
image analysis, where positive samples are scarce, while performance is
commonly estimated against the correct detection of these positive examples. We
approach this challenge by formulating the problem as anomaly detection with
generative models. We train a generative model without supervision on the
`negative' (common) datapoints and use this model to estimate the likelihood of
unseen data. A successful model allows us to detect the `positive' case as low
likelihood datapoints.
In this position paper, we present the use of state-of-the-art deep
generative models (GAN and VAE) for the estimation of a likelihood of the data.
Our results show that on the one hand both GANs and VAEs are able to separate
the `positive' and `negative' samples in the MNIST case. On the other hand, for
the NLST case, neither GANs nor VAEs were able to capture the complexity of the
data and discriminate anomalies at the level that this task requires. These
results show that even though there are a number of successes presented in the
literature for using generative models in similar applications, there remain
further challenges for broad successful implementation.Comment: 15 pages, 13 figures, accepted by Benelearn 2018 conferenc
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