Wyniki wyszukiwania - Biblioteka Nauki

Ten serwis zostanie wyłączony 2025-02-11.

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

1 Equilibrium. Quarterly Journal of Economics and Economic Policy

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Application of interpolated discrete fourier transform method in the diagnostics of cage induction motors

100%

Krzyk P. , Pragłowska-Ryłko N. , Sułowicz M.

This paper compares the accuracy of the multi-frequency stator phase current spectrum estimation. This is as a development of considerations made in [1]. In this paper, the authors added to previous results for the classic Discrete Fourier Transform method and the two-point interpolated DFT method (IpDFT ), new results obtained by three-point IpDFT method’s algorithm. The analysis was made for slip spectrum component which is used for cage damage detection. To minimize the spectrum leakage effect, the selected Rife-Vincent Class I windows (RVCI ) were used. The article focuses on the effectiveness of spectral component estimation in function of the used method, type of time window and measurement time. Values of errors of frequency and amplitude estimations are presented on graphs. The authors compared theoretical considerations with the case of real stator current of an asynchronous motor with a broken cage. This article can be used as a base for further studies on the diagnosis of induction motors.

Firm's default - new methodological approach and preliminary evidence from Poland

88%

Berent T. , Bławat B. , Dietl M. , Krzyk P. , Rejman R.

Equilibrium. Quarterly Journal of Economics and Economic Policy

2017

tom 12

nr 4

753-773

Research background: Bankruptcy literature is populated with scores of (econometric) models ranging from Altman's Z-score, Ohlson's O-score, Zmijewski's probit model to k-nearest neighbors, classification trees, support vector machines, mathematical programming, evolutionary algorithms or neural networks, all designed to predict financial distress with highest precision. We believe corporate default is also an important research topic to be identified with the prediction accuracy only. Despite the wealth of modelling effort, a unified theory of default is yet to be proposed. Purpose of the article: Due to the disagreement both on the definition and hence the timing of default, as well as on the measurement of prediction accuracy, the comparison (of predictive power) of various models can be seriously misleading. The purpose of the article is to argue for the shift in research focus from maximizing accuracy to the analysis of the information capacity of predictors. By doing this, we may yet come closer to understanding default itself. Methods: We critically appraise the bankruptcy research literature for its methodological variety and empirical findings. Default definitions, sampling procedures, in and out-of-sample testing and accuracy measurement are all scrutinized. In an empirical part, we use a double stochastic Poisson process with multi-period prediction horizon and a comprehensive database of some 15,000 Polish non-listed companies to illustrate the merits of our new approach to default modelling. Findings & Value added: In the theoretical part, we call for the construction of a single unified default forecasting platform estimated for the largest dataset of firms possible to allow testing the utility of various sources of micro, mezzo, and macro information. Our preliminary empirical evidence is encouraging. The accuracy ratio amounts to 0.92 for t = 0 and drops to 0.81 two years ahead of default. We point to the pivotal role played by the information on firm's liquidity (alternatively in profitability) and - in contrast to Altman's tradition - hardly any contribution to predictive power of other financial ratios. Macro data is shown to be critical. It adds, on average, more than 10 p.p. to accuracy ratio. In the future, we hope to integrate listed and non-listed firms data into one model, ideally at higher frequency than annual, and include the information on firm's competitiveness position.