Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the Effect of Hungarian Monetary Policy within a Structural VAR Framework

A standard approach in measuring the effect of monetary policy on output and prices is to estimate a VAR model, characterise somehow the monetary policy shock and then plot impulse responses. In this paper I attempt to do this exercise with Hungarian data. I compare two identification approaches. One of them involves the ‘sign restrictions on impulse responses’ strategy applied recently by several authors. I also propose another approach, namely, imposing restrictions on implied shock history. My argument is that in certain cases, especially in the case of the Hungarian economy, the latter identification scheme may be more credible. In order to obtain robust results I use two datasets. To tackle possible structural breaks I make alternative estimates on a shorter sample as well. The main conclusions are the followings: (1) although the two identification approaches produced very similar results, imposing restrictions on history may help to dampen counterintuitive reaction of prices; (2) after 1995 a typical unanticipated monetary policy contraction (a roughly 25 basis points rate hike) resulted in an immediate 1 per cent appreciation of the nominal exchange rate (3) followed by a 0.3% lower output and 0.1-0.15% lower consumer prices; (4) the impact on prices is slower than on output; it reaches its bottom 4-6 years after the shock, resembling the intuitive choreography of sticky-price models; (5) using additional observations prior to 1995 makes identification more difficult indicating the presence of a marked structural break.
Essays on corporate governance

might be reluctant to interfere because they are uncertain whether or not managerial failure has been timely corrected by the monitor. This uncertaintyy is exactly the reason why a lack of objectivity can occur. The lack of substitutabilityy between the objectivity of the monitor and control by non-monitoring parties iss therefore inherent to the incentive problem of the monitor. We further show that the probabilityy of observing shareholder activism can easily be highest when the need to correct
What Happens in Acquisitions? Evidence from Brand Ownership Changes and Advertising Investment

We study a large sample of brands that are acquired and find that new owners display an abnormal propensity to sharply increase or decrease advertising spending, with large decreases being particularly common. Increased private ownership is associated with a significant downward shift in advertising relative to other deals, primarily reflecting the behavior of private equity purchasers. New owners appear to alter advertising investment to counteract any systematic under or overinvestment by prior owners, and they tend to cut advertising in existing brands that closely overlap with purchased brands. Combined buyer plus seller announcement returns are positively related to measures of post-acquisition cuts in advertising spending and also to indicators of corrections to past investment biases by prior owners. Acquired brands do not on average experience significant losses in market share, even when advertising spending is revised downwards. Our evidence is consistent with the hypothesis that the identity and characteristics of an asset's owner are important determinants of investment policy. The evidence generally supports the notion that changes in investment brought about by an acquisition are efficiency enhancing.
Organic farming and measures of European agricultural policy

Haring, A. M. , Dabbert, S. , Aurbacher, J., Bichler, B. , Eichert, C. , Gambelli, D. , Lampkin, N. , Offermann, F. , Olmos, S. , Tuson, J., Zanoli, R. (Eds) (2004). Organic farming and measures of European Agricultural Policy Organic Farming in Europe: Economics and Policy, 11, 270pp
Older Male Labour Force Participation: The Role of Social Security and Hidden Unemployment

This paper examines the role played by social security provisions and hidden unemployment in influencing trends in older male labour force participation in Australia. The paper concludes that labour demand constraints have been the dominant explanatory force behind the decline in older male labour force participation rates over recent decades in Australia. The results presented in the paper challenge the consensus developed in previous Australian studies that labour supply incentives have been the primary force influencing movements in older male labour force participation rates. Furthermore, various estimates of older male hidden unemployment presented in the paper suggest that potentially large unemployment rates are concealed beneath relatively modest 'official' recorded estimates. The findings presented in the paper challenge the supply side policy stance toward older workers' labour force participation adopted for an ageing society setting.
Entrepreneurship

The article considers economic freedom as a fundamental principle of creating a favorable business climate, developing entrepreneurship, trust or distrust from foreign investors, creditors, and its impact on economic development in the country and improving the economic well-being of the population in today's global world. The article demonstrated the relationship between the level of GDP, as an indicator of the well-being of the population of a certain country and the level of economic freedom achieved by it, which is calculated using the Index of Economic Freedom at the theoretical level. The components of this index are characterized, the methodology of its calculation is given, and its analytical capabilities are shown in the analysis of economies around the world. The groups of countries classified by the Index of Economic Freedom are characterized. The article analyzes economic freedom in Ukraine. Ukraine's place in the world economic freedom rating is explored. The discrepancy between the ideal of economic freedom and the state of affairs in Ukraine is revealed. The monitoring of the state of economic freedom has identified the major gaps that have taken place in the national economy and hindered the development of entrepreneurship and slow down economic growth. The dynamics of the main components of the Economic Freedom Index in Ukraine are analyzed. The comparative characteristic of the index of economic freedom in Ukraine and other post-socialist countries in the dynamics of 2016–2019 is con-ducted. It is established that Ukraine has the worst index of economic freedom, and in the last 3 years it was the least able to improve it. This indicates the absence of real reforms of socio-economic relations and their implementation mainly as imitation.
Who Cares - And Does It Matter? Measuring Wage Penalties for Caring Work

Economists and sociologists have proposed arguments for why there can exist wage penalties for work involving helping and caring for others, penalties borne disproportionately by women. Evidence on wage penalties is neither abundant nor compelling. We examine wage differentials associated with caring jobs using multiple years of Current Population Survey (CPS) earnings files matched to O*NET job descriptors that provide continuous measures of 'assisting and caring' and 'concern' for others across all occupations. This approach differs from prior studies that assume occupations either do or do not require a high level of caring. Cross-section and longitudinal analyses are used to examine wage differences associated with the level of caring, conditioned on worker, location, and job attributes. Wage level estimates suggest substantive caring penalties, particularly among men. Longitudinal estimates based on wage changes among job switchers indicate smaller wage penalties, our preferred estimate being a 2 percent wage penalty resulting from a one standard deviation increase in our caring index. We find little difference in caring wage gaps across the earnings distribution. Measuring mean levels of caring across the U.S. labor market over nearly thirty years, we find a steady upward trend, but overall changes are small and there is no evidence of convergence between women and men.
Algorithms and Theory for Robust PCA and Phase Retrieval

In this dissertation, we investigate two problems, both of which require the recovery of unknowns from measurements that are potentially corrupted by outliers. The first part focuses on the problem of robust principal component analysis (PCA), which aims to recover an unknown low-rank matrix from a corrupted and partially-observed matrix. The robust PCA problem, originally nonconvex itself, has been solved via a convex relaxation based approach principal component pursuit (PCP) in the literature. However, previous works assume that the sparse errors uniformly spread over the entire matrix and characterize the condition under which PCP guarantees exact recovery. We generalize these results by allowing non-uniform error corruptions over the low-rank matrix and characterize the conditions on the error corruption probability of each individual entry based on the local coherence of the low-rank matrix, under which correct recovery can be guaranteed by PCP. Our results yield new insights on the graph clustering problem beyond the relevant literature. The second part of the thesis studies the phase retrieval problem, which requires recovering an unknown vector from only its magnitude measurements. Differently from the first part, we solve this problem directly via optimizing nonconvex objectives. As the nonconvex objective is often constructed in such a way that the true vector is its global optimizer, the difficulty here is to design algorithms to find the global optimizer efficiently and provably. In order to solve this problem, we propose a gradient-like algorithm named reshaped Wirtinger flow (RWF). For random Gaussian measurements, we show that RWF enjoys linear convergence to a global optimizer as long as the number of measurements is on the order of the dimension of the unknown vector. This achieves the best possible sample complexity as well as the state-of-the-art computational efficiency. Moreover, we study the phase retrieval problem when the measurements are corrupted by adversarial outliers, which models situations with missing data or sensor failures. In order to resist possible observation outliers in an oblivious manner, we propose a novel median truncation approach to modify the nonconvex approach in both the initialization and the gradient descent steps. We apply the median truncation approach to the Poisson loss and the reshaped quadratic loss respectively, and obtain two algorithms median-TWF and median-RWF. We show that both algorithms recover the signal from a near-optimal number of independent Gaussian measurements, even when a constant fraction of measurements is corrupted. We further show that both algorithms are stable when measurements are corrupted by both sparse arbitrary outliers and dense bounded noises. We establish our results on the performance guarantee via the development of non-trivial concentration measures of the median-related quantities, which can be of independent interest. ALGORITHMS AND THEORY FOR ROBUST PCA AND PHASE RETRIEVAL
Political economy of love: nurturance gap, disembedded economy and freedom constraints within neoliberal capitalism

This article critically evaluates the forms of love capital being accumulated by people in capitalist economies, through the lens of some of the core general principles of heterodox political economy (HPE). We start by situating love historically in the neoliberal culture and then examine the six main love styles as well as the five critical factors through the process of circular and cumulative causation. We then scrutinise the contradictions of neoliberal capitalism involving the nurturance gap, disembedded economy and freedom constraint which inhibit the generation of holistic love capital. The path dependent nature of love is then linked to relational phases and instabilities, especially involving serial monogamy in the United States. Some of the core principles of HPE provide a vantage point for scrutinising the problems involved in stimulating holistic love capital in the contemporary environment.
Performance Comparison of Experimental-based Kalman Filter and Complementary Filter for IMU Sensor Fusion by applying Quadrature Encoder

In this work, Kalman filter is designed with the help of C programming language and compared experimentally based on the actual Encoder values with Complementary estimation for Inertial Measurement Unit (IMU) sensor fusion. As IMU sensor, ADXL345 accelerometer and L3G4200D gyroscope are utilized in this analysis. For the actual rotation, the Quadrature encoder is applied with Arduino UNO by interrupt reading. Complementary filter is intended for human arm movement and Kalman filter is emphasized derived from the experimental covariance such as Qangle for accelerometer trust level, Qbias for gyroscope and Rmeasure for the measurement. The sable solution to this system is obtained via a chart display and root mean square error checking method. For the various changing to Kalman coefficient, the experimental test is performed and optimal estimation could be founded out at 0.001, 0.1 and 0.0001 for Qangle, Qbias and Rmeasure for Kalman filter.
Labor market institutions and economic performance

Barely a day goes by without some expert telling us how the continental European economies are about to disintegrate unless their labour markets become more flexible. Basically, we are told, Europe has the wrong sort of labour market institutions for the modern global economy. These outdated institutions both raise unemployment and lower growth rates. The truth of propositions such as these depends on which labour market institutions really are bad for unemployment and growth, and which are not. Our purpose in this paper is to set out what we know about this question. Our conclusions indicate that the labour market institutions on which policy should be focused are unions and social security systems. Encouraging product market competition is a key policy to eliminate the negative effects of unions. For social security the key policies are benefit reform linked to active labour market policies to move people from welfare to work. By comparison, time spent worrying about strict labour market regulations, employment protection and minimum wages is probably time largely wasted.
A GPU algorithm for tracking yeast cells in phase-contrast microscopy images

When information and measures obtained from sequences of microscopic images are subject to time constraints, suitable fast algorithms must be implemented to process the whole data set. In this work, we deal with sequences of images obtained from time-lapse microscopy in order to detect single yeast cells in a microfluidics chip over time. The underlying idea consists in determining a minimum cost configuration for each couple of frames, which can be expressed by setting up and solving a linear programming (LP) problem. Laboratories seldom have the opportunity to use HPC hardware for such intent. For this reason, we propose an efficient GPU-parallel software implemented in CUDA and based on the simplex method, a common tool for solving LP problems. Our parallel strategy minimizes the threads divergence and maximizes the device occupancy, in order to maximize the overall throughput. Also, memory transfers between host and device have been minimized to exploit data locality. Experimental results on real images sequences highlight a promising speedup with respect to the CPU version suitable for real-time applications.
The Asymmetric Impacts of Good and Bad News on Opinion Divergence: Evidence from Revisions to the S&P 500 Index

Motivated by the ambiguity theory of Epstein and Schneider (2003, 2008), we hypothesize that investors' beliefs on the prospects of firms converge upon the arrival of bad news, but do not converge or even further diverge on the arrival of good news. We expect firms with high divergence in opinions to experience lower stock returns around the announcements of bad news but not for good news. Using revisions to the S&P 500 index between 1962 and 2008 as information events, we find overwhelming support for the hypothesis. The results are robust to controlling for alternative hypotheses of price changes around revisions to the S&P 500 index, as well as common firm characteristics.
Probing a stationary non-Gaussian background of stochastic gravitational waves with pulsar timing arrays

We introduce the concept of stationary graviton non-Gaussianity (nG), an observable that can be probed in terms of 3-point correlation functions of a stochastic gravitational wave (GW) background. When evaluated in momentum space, stationary nG corresponds to folded bispectra of graviton nG. We determine 3-point overlap functions for testing stationary nG with pulsar timing array GW experiments, and we obtain the corresponding optimal signal-to-noise ratio. For the first time, we consider 3-point overlap functions including scalar graviton polarizations (which can be motivated in theories of modified gravity); moreover, we also calculate 3-point overlap functions for correlating pulsar timing array with ground based GW detectors. The value of the optimal signal-to-noise ratio depends on the number and position of monitored pulsars. We build geometrical quantities characterizing how such ratio depends on the pulsar system under consideration, and we evaluate these geometrical parameters using data from the IPTA collaboration. We quantitatively show how monitoring a large number of pulsars can increase the signal-to-noise ratio associated with measurements of stationary graviton nG.
A Mathematical Analysis of Memory Lifetime in a Simple Network Model of Memory

We study the learning of an external signal by a neural network and the time to forget it when this network is submitted to noise. The presentation of an external stimulus to the recurrent network of binary neurons may change the state of the synapses. Multiple presentations of a unique signal lead to its learning. Then, during the forgetting time, the presentation of other signals (noise) may also modify the synaptic weights. We construct an estimator of the initial signal using the synaptic currents and in this way define a probability of error. In our model, these synaptic currents evolve as Markov chains. We study the dynamics of these Markov chains and obtain a lower bound on the number of external stimuli that the network can receive before the initial signal is considered forgotten (probability of error above a given threshold). Our results are based on a finite-time analysis rather than large-time asymptotic. We finally present numerical illustrations of our results.
VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition

To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet. Variable group convolution is introduced by VarGNet to solve the conflict between small computational cost and the unbalance of computational intensity inside a block. We employ variable group convolution to design our network which can support large scale face identification while reduce computational cost and parameters. Specifically, we use a head setting to reserve essential information at the start of the network and propose a particular embedding setting to reduce parameters of fully-connected layer for embedding. To enhance interpretation ability, we employ an equivalence of angular distillation loss to guide our lightweight network and we apply recursive knowledge distillation to relieve the discrepancy between the teacher model and the student model. The champion of deepglint-light track of LFR (2019) challenge demonstrates the effectiveness of our model and approach. Implementation of VarGFaceNet will be released at https://github.com/zma-c-137/VarGFaceNet soon.
Many Faces of Symmetric Edge Polytopes

Symmetric edge polytopes are a class of lattice polytopes constructed from finite simple graphs. In the present paper we highlight their connections to the Kuramoto synchronization model in physics — where they are called adjacency polytopes — and to Kantorovich-Rubinstein polytopes from finite metric space theory. Each of these connections motivates the study of symmetric edge polytopes of particular classes of graphs. We focus on such classes and apply algebraic combinatorial methods to investigate invariants of the associated symmetric edge polytopes.
Making bright giants invisible at the Galactic Centre

Current observations of the Galactic Centre (GC) seem to display a core-like distribution of bright stars from ∼5 arcsec inwards. On the other hand, we observe young, massive stars at the GC, with roughly 20–50 per cent of them in a disc, mostly in the region where the bright giants appear to be lacking. In a previous publication we put the idea forward that the missing stars are deeply connected to the presence of this disc. The progenitor of the stellar disc is very likely to have been a gaseous disc that at some point fragmented and triggered star formation. This caused the appearance of overdensity regions in the disc that had high enough densities to ensure stripping large giants of their atmospheres and thus rendering them very faint. In this paper, we use a stellar evolution code to derive the properties that a red giant would display in a colour–magnitude diagram, as well as a non-linearity factor required for a correct estimate of the mass loss. We find that in a very short time-scale, the red giants leave their standard evolutionary track. The non-linearity factor has values that not only depend on the properties of the clumps, but also on the physical conditions of the giant stars, as we predicted analytically. According to our results, envelope stripping works, moving stars on a short time-scale from the giant branch to the white dwarf stage, thus rendering them invisible to observations.
Convergences of the rescaled Whittaker stochastic differential equations and independent sums

We study some SDEs derived from the $q\to 1$ limit of a 2D surface growth model called the $q$-Whittaker process. Through these SDEs, the fluctuations of the limiting model are proven to exhibit Gaussian characteristics that "come down from infinity": After rescaling and renormalization, convergence to the time-inverted stationary additive stochastic heat equation holds. The point of view in this paper is a probabilistic representation of the SDEs by independent sums. With this connection, normal and Poisson approximations fully explain the convergence. The proof extends these approximations to some particular integrated forms.
NLS: an accurate and yet easy-to-interpret regression method

An important feature of successful supervised machine learning applications is to be able to explain the predictions given by the regression or classification model being used. However, most state-of-the-art models that have good predictive power lead to predictions that are hard to interpret. Thus, several model-agnostic interpreters have been developed recently as a way of explaining black-box classifiers. In practice, using these methods is a slow process because a novel fitting is required for each new testing instance, and several non-trivial choices must be made. We develop NLS (neural local smoother), a method that is complex enough to give good predictions, and yet gives solutions that are easy to be interpreted without the need of using a separate interpreter. The key idea is to use a neural network that imposes a local linear shape to the output layer. We show that NLS leads to predictive power that is comparable to state-of-the-art machine learning models, and yet is easier to interpret.