102 research outputs found
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Inspired by the success of deep learning techniques in the physical and
chemical sciences, we apply a modification of an autoencoder type deep neural
network to the task of dimension reduction of molecular dynamics data. We can
show that our time-lagged autoencoder reliably finds low-dimensional embeddings
for high-dimensional feature spaces which capture the slow dynamics of the
underlying stochastic processes - beyond the capabilities of linear dimension
reduction techniques
Real-time inflation forecasting using non-linear dimension reduction techniques
In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower-dimensional set of latent factors. We model the relationship between inflation and the latent factors using constant and time-varying parameter (TVP) regressions with shrinkage priors. Our models are then used to forecast monthly US inflation in real-time. The results suggest that sophisticated dimension reduction methods yield inflation forecasts that are highly competitive with linear approaches based on principal components. Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation. Zooming into model performance over time reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle or the current COVID-19 pandemic
Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques
In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower dimensional set of latent factors. We model the relationship between inflation and these latent factors using state-of-the-art time-varying parameter (TVP) regressions with shrinkage priors. Using monthly real-time data for the US, our results suggest that adding such non-linearities yields forecasts that are on average highly competitive to ones obtained from methods using linear dimension reduction techniques. Zooming into model performance over time moreover reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle
Non-linear dimension reduction in factor-augmented vector autoregressions
This paper introduces non-linear dimension reduction in factor-augmented
vector autoregressions to analyze the effects of different economic shocks. I
argue that controlling for non-linearities between a large-dimensional dataset
and the latent factors is particularly useful during turbulent times of the
business cycle. In simulations, I show that non-linear dimension reduction
techniques yield good forecasting performance, especially when data is highly
volatile. In an empirical application, I identify a monetary policy as well as
an uncertainty shock excluding and including observations of the COVID-19
pandemic. Those two applications suggest that the non-linear FAVAR approaches
are capable of dealing with the large outliers caused by the COVID-19 pandemic
and yield reliable results in both scenarios.Comment: JEL: C11, C32, C40, C55, E37. Keywords: Dimension reduction, machine
learning, non-linear factor-augmented vector autoregression, monetary policy
shock, uncertainty shock, impulse response analysis, COVID-1
River Sampling - a Fishing Expedition: A Non-Probability Case Study
The ease with which large amounts of data can be collected via the Internet has led to a renewed interest in the use of non-probability samples. To that end, this paper performs a case study, comparing two non-probability datasets - one based on a river-sampling approach, one drawn from an online-access panel - to a reference probability sample. Of particular interest is the single-question river-sampling approach, as the data collected for this study presents an attempt to field a multi-item scale with such a sampling method. Each dataset consists of the same psychometric measures for two of the Big-5 personality traits, which are expected to perform independently of sample composition. To assess the similarity of the three datasets we compare their correlation matrices, apply linear and non-linear dimension reduction techniques, and analyze the distance between the datasets. Our results show that there are important limitations when implementing a multi-item scale via a single-question river sample. We find that, while the correlation between our data sets is similar, the samples are composed of persons with different personality traits
Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders
High-dimensional data sets are often analyzed and explored via the
construction of a latent low-dimensional space which enables convenient
visualization and efficient predictive modeling or clustering. For complex data
structures, linear dimensionality reduction techniques like PCA may not be
sufficiently flexible to enable low-dimensional representation. Non-linear
dimension reduction techniques, like kernel PCA and autoencoders, suffer from
loss of interpretability since each latent variable is dependent of all input
dimensions. To address this limitation, we here present path lasso penalized
autoencoders. This structured regularization enhances interpretability by
penalizing each path through the encoder from an input to a latent variable,
thus restricting how many input variables are represented in each latent
dimension. Our algorithm uses a group lasso penalty and non-negative matrix
factorization to construct a sparse, non-linear latent representation. We
compare the path lasso regularized autoencoder to PCA, sparse PCA, autoencoders
and sparse autoencoders on real and simulated data sets. We show that the
algorithm exhibits much lower reconstruction errors than sparse PCA and
parameter-wise lasso regularized autoencoders for low-dimensional
representations. Moreover, path lasso representations provide a more accurate
reconstruction match, i.e. preserved relative distance between objects in the
original and reconstructed spaces
Impact of COVID-19 emergency on the psychological well-being of susceptible individuals
The current pandemic has exerted an unprecedented psychological impact on the world population, and its effects on mental health are a growing concern. The present study aims to evaluate psychological well-being (PWB) during the COVID-19 crisis in university workers with one or more diseases likely to increase the risk of severe outcomes in the event of SARS-CoV-2 infection, defined as susceptible. 210 susceptible employees of an Italian University (aged 25-71 years) were recruited during the COVID-19 second wave (October-December 2020). A group comprising 90 healthy university employees (aged 26-69 years) was also recruited. The self-report Psychological General Well Being Index (PGWBI) was used to assess global PWB and the influence on six sub-domains: anxiety, depressed mood, positive well-being, self-control, general health, and vitality. We applied non-linear dimension-reduction techniques and regression methods to 45 variables in order to assess the main demographic, occupational, and general-health-related factors predicting PWB during the COVID-19 crisis. PGWBI score was higher in susceptible than in healthy workers, both as total score (mean 77.8 vs 71.3) and across almost all subscales. Age and jobs involving high social interaction before the pandemic were inversely associated with the PWB total score, general health, and self-control subscores. The current data suggest no decline in PWB during the second wave of COVID-19 health emergency in susceptible individuals of working age. Critically, higher risk for mental-health issues appears to be inversely related to age, particularly among individuals deprived of their previous level of social interaction at work
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