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Empirical Macroeconomics and DSGE Modeling in Statistical Perspective

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arXiv 2022年

作者： McDonald, Daniel J. Shalizi, Cosma Rohilla Department of Statistics University of British Columbia VancouverBC Canada Department of Statistics and Data Science and of Machine Learning Carnegie Mellon University PittsburghPA United States Santa Fe Institute Santa FeNM United States

Dynamic stochastic general equilibrium (DSGE) models have been an ubiquitous, and controversial, part of macroeconomics for decades. In this paper, we approach DSGEs purely as statstical models. We do this by applying two common model validation checks to the canonical Smets and Wouters (2007) DSGE: (1) we simulate the model and see how well it can be estimated from its own simulation output, and (2) we see how well it can seem to fit nonsense data. We find that (1) even with centuries' worth of data, the model remains poorly estimated, and (2) when we swap series at random, so that (e.g.) what the model gets as the inflation rate is really hours worked, what it gets as hours worked is really investment, etc., the fit is often only slightly impaired, and in a large percentage of cases actually improves (even out of sample). Taken together, these findings cast serious doubt on the meaningfulness of parameter estimates for this DSGE, and on whether this specification represents anything structural about the economy. Constructively, our approaches can be used for model validation by anyone working with macroeconomic time series. © 2022, CC BY.

关键词： Time series

Topic Modelling using Transfer learning: Issues and Challenges

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Topic Modelling using Transfer Learning: Issues and Challeng...

Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Rama Krishna K Kaipa Sandhya Praveen Gujjar J Raghavendra M Devadas Vani Hiremani Preethi Department of Artificial Intelligence and Machine Learning Impact college of Engineering and Applied Sciences Bengaluru India Department of Data Science Impact college of Engineering and applied sciences Bengaluru India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education (MAHE) Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

A machine learning method, transfer learning, uses information from one job or area to enhance effectiveness in a related but distinct task or area. Transfer learning enables using pre-trained models that were previously learned using a sizable and varied dataset in the original domain instead of creating a model from the beginning for the intended activity. Usually, using a lesser dataset, the pre-trained model's representations and knowledge may be improved upon or applied to the intended goal or area. In natural language processing (NLP) and machine learning, topic modeling is an approach that finds and extracts the fundamental concepts or subjects from a group of documents or textual data. Despite prior experience with the aspects, this unsupervised learning method seeks to uncover the text's latent semantic structure. This paper's primary goal is to investigate the possibilities of transfer learning for text topic modeling. Additionally, to assess the correctness of the pre-trained model's performance. The paper also describes the issues and challenges associated with the text for performing the topic modeling using transfer learning. This paper delves into the methodologies and techniques of topic modeling using transfer learning. It explores how this novel approach transcends the constraints of conventional topic modeling, enabling the extraction of meaningful topics from data-rich source domains to improve topic modeling performance in data-scarce or specialized target domains. By integrating the principles of transfer learning into the topic modeling framework, we unlock the potential to enhance the interpretability, accuracy, and applicability of topic modeling across diverse domains and applications. This paper sheds light on the transformative power of transfer learning in topic modeling.

关键词： Performance evaluation Adaptation models Accuracy Computational modeling Transfer learning Semantics data models Natural language processing data mining Unsupervised learning

Regression with comparisons: escaping the curse of dimensionality with ordinal information

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The Journal of Machine Learning Research

The Journal of machine learning Research 2020年第1期21卷 6480-6533页

作者： Yichong Xu Sivaraman Balakrishnan Aarti Singh Artur Dubrawski Machine Learning Department Department of Statistics and Data Science Machine Learning Department Auton Lab The Robotics Institute Carnegie Mellon University Pittsburgh PA

In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction. In many practical situations, we are able to obtain alternative feedback, possibly at a low cost. A broad goal is to understand the usefulness of, and to design algorithms to exploit, this alternative feedback. In this paper, we consider a semi-supervised regression setting, where we obtain additional ordinal (or comparison) information for the unlabeled samples. We consider ordinal feedback of varying qualities where we have either a perfect ordering of the samples, a noisy ordering of the samples or noisy pairwise comparisons between the samples. We provide a precise quantification of the usefulness of these types of ordinal feedback in both nonparametric and linear regression, showing that in many cases it is possible to accurately estimate an underlying function with a very small labeled set, effectively escaping the curse of dimensionality. We also present lower bounds, that establish fundamental limits for the task and show that our algorithms are optimal in a variety of settings. Finally, we present extensive experiments on new datasets that demonstrate the efficacy and practicality of our algorithms and investigate their robustness to various sources of noise and model misspecification.

关键词： pairwise comparison ranking regression interactive learning

Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics

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arXiv 2024年

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model’s ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024, CC BY-SA.

关键词： State space methods

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission Capacity with Optimized Fault Current Limiters and HVDC Circuit Breaker Parameters

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Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission ...

Electronics and Renewable Systems (ICEARS), International Conference on

作者： M. Siva Ramkumar Saranya. N Gokul Gopan Rahmath Ulla Baig Josha Daniel S Babu M Department of ECE SNS College of Technology India Department of Artificial Intelligence and Data Science Karpagam College of Engineering India Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Artificial Intelligence and Machine Learning Cambridge Institute of Technology K R Puram Bangalore Department of EEE Hindustan College of Engineering and Technology India Master of Engineering in Embedded Systems Hindusthan College of Engineering and Technology India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The multi-terminal HVDC system relies heavily on circuit breakers (CBs) and fault current limiters (FCLs) for protection and performance reliability. This research describes a new Giant Trevally optimizer-based strategy for improving the performance of 4-terminal HVDC systems by optimizing crucial circuit breakers and fault current limiters. The proposed method uses time domain estimation to correctly capture fault dynamics, such as traveling waves and reflection coefficients and finds the worst-case fault locations. The multi-objective optimization seeks to optimize the overcurrent, overvoltage handling, fault clearance time, and energy absorption in arresters. This technology provides an effective, dependable protection solution that reduces fault recovery time and increases system stability. The output pulse of the HVDC system shows very low THD results 1.98% for the minimum load condition, 0.87% for median load, and 0.04% for peak load conditions. The results established the simulation of the model in a MATLAB environment.

关键词： HVDC transmission Circuit breakers Stability analysis Circuit faults Fault current limiters Time-domain analysis Protection Voltage control Integrated circuit reliability Optimization

Disease and Medications Text Visualization Using Scattertext

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Disease and Medications Text Visualization Using Scattertext

Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Rama Krishna K Kaipa Sandhya Praveen Gujjar J Raghavendra M Devadas Vani Hiremani Sapna R Department of Artificial Intelligence and Machine Learning Impact college of Engineering and Applied Sciences Bengaluru India Department of Data Science Impact college of Engineering and applied sciences Bengaluru India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education (MAHE) Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Before text data can be analysed and visualised, it must be thoroughly cleaned due to its messy nature. data visualizations use the data to tell an engaging and simple-to-read story. That is what the Scattertext tool does. Scatter text allows which terms stand out in your corpora and how their frequency varies across categories. The dataset used in this paper includes reviews of drugs. This dataset offers a useful window into how people discuss their thoughts and experiences using various drugs. The drug names, user ratings, and the actual written reviews are among the details included in the dataset. This dataset consists of drug reviews collected from various sources, hence expressing a wide range of opinions and feelings. Each row in this dataset represents the experience a user had with a given medication and hence allows us to explore those emotions that are associated with any particular set of drugs. Understanding opinion expressed in the review about drugs might go a long way in helping decision-making processes in both healthcare and pharmaceuticals. The scatter plot distinguishes between labelled points and unlabelled ones by using a custom algorithm for Scattertext. By hovering their cursor over the labelled areas done client-side, users can interactively see which terms correspond to which points.

关键词： Drugs Reviews Decision making data visualization Medical services Market research Feature extraction Diseases Intelligent control

Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows

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arXiv 2022年

作者： Vaitl, Lorenz Nicoli, Kim Andrea Nakajima, Shinichi Kessel, Pan Machine Learning Group Department of Electrical Engineering & Computer Science Technische Universität Berlin Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Technische Universität Berlin Berlin Germany RIKEN Center for AIP Chuo City Tokyo103-0027 Japan

We propose an algorithm to estimate the path-gradient of both the reverse and forward Kullback–Leibler divergence for an arbitrary manifestly invertible normalizing flow. The resulting path-gradient estimators are straightforward to implement, have lower variance, and lead not only to faster convergence of training but also to better overall approximation results compared to standard total gradient estimators. We also demonstrate that path-gradient training is less susceptible to mode-collapse. In light of our results, we expect that path-gradient estimators will become the new standard method to train normalizing flows for variational inference. © 2022, CC BY.

关键词：

Path-Gradient Estimators for Continuous Normalizing Flows

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arXiv 2022年

Recent work has established a path-gradient estimator for simple variational Gaussian distributions and has argued that the path-gradient is particularly beneficial in the regime in which the variational distribution approaches the exact target distribution. In many applications, this regime can however not be reached by a simple Gaussian variational distribution. In this work, we overcome this crucial limitation by proposing a path-gradient estimator for the considerably more expressive variational family of continuous normalizing flows. We outline an efficient algorithm to calculate this estimator and establish its superior performance empirically. © 2022, CC BY.

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Evaluating Posterior Distributions by Selectively Breeding Prior Samples

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arXiv 2022年

作者： Shalizi, Cosma Rohilla Department of Statistics and Data Science and of Machine Learning Carnegie Mellon University PittsburghPA15213 United States The Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States

Using Markov chain Monte Carlo to sample from posterior distributions was the key innovation which made Bayesian data analysis practical. Notoriously, however, MCMC is hard to tune, hard to diagnose, and hard to parallelize. This pedagogical note explores variants on a universal non-Markov-chain Monte Carlo scheme for sampling from posterior distributions. The basic idea is to draw parameter values from the prior distributions, evaluate the likelihood of each draw, and then copy that draw a number of times proportional to its likelihood. The distribution after copying is an approximation to the posterior which becomes exact as the number of initial samples goes to infinity;the convergence of the approximation is easily analyzed, and is uniform over Glivenko-Cantelli classes. While not entirely practical, the schemes are straightforward to implement (a few lines of R), easily parallelized, and require no rejection, burn-in, convergence diagnostics, or tuning of any control settings. I provide references to the prior art which deals with some of the practical obstacles, at some cost in computational and analytical simplicity. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes