Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Mazur, Błażej" wg kryterium: Autor

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-4 z 4
    Tytuł:
    Probabilistic predictive analysis of business cycle fluctuations in Polish economy
    Autorzy:
    Mazur, Błażej
    Powiązania:
    https://bibliotekanauki.pl/articles/1833823.pdf
    Data publikacji:
    2017-09-30
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    density forecasts
    Opis:
    Research background: The probabilistic setup and focus on evaluation of uncertainties and risks has become more widespread in modern empirical macroeconomics, including the analysis of business cycle fluctuations. Therefore, forecast-based indicators of future economic conditions should be constructed using density forecasts rather than point forecasts, as the former provide description of forecast uncertainty. Purpose of the article: We discuss model-based probabilistic inference on business cycle fluctuations in Poland. In particular, we consider model comparison for probabilistic prediction of growth rates of the Polish industrial production. We also develop a class of indicators of future economic conditions constructed using probabilistic information on the rates (that make use of joint predictive distribution over several forecast horizons). Methods: We use Bayesian methods (in order to capture the estimation uncertainty) and consider two groups of models. The first group consists of Dynamic Conditional Score models with the generalized t conditional distribution (with conditional heteroskedasticity and heavy tails, being important for modelling of extreme observations). Another group of models relies on deterministic cycle modelling using Flexible Fourier Form. Ex-post density forecasting performance of the models is compared using the criteria for probabilistic pre-diction: Log-Predictive Score (LPS) and Continuous Ranked Probability Score (CRPS). Findings & value added: The pre-2013 data support the deterministic cycle models whereas more recent observations can be explained by a simple mean-reverting Gaussian AR(4) process. The results indicate a structural change affecting Polish business cycle fluctuations after 2013. Hence, forecast pooling strategies are recommended as a tool for further research. We find rather limited support in favor of the first group of models. The probabilistic indicator of future economic conditions considered here leads actual phases of the growth cycle quite well, though the effect is less obvious after 2013.
    Źródło:
    Equilibrium. Quarterly Journal of Economics and Economic Policy; 2017, 12, 3
    1689-765X
    2353-3293
    Pojawia się w:
    Equilibrium. Quarterly Journal of Economics and Economic Policy
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On the Empirical Importance of Periodicity in the Volatility of Financial Returns - Time Varying GARCH as a Second Order APC(2) Process
    Autorzy:
    Mazur, Błażej
    Pipień, Mateusz
    Powiązania:
    https://bibliotekanauki.pl/articles/483329.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    GARCH models
    Bayesian inference
    periodically correlated stochastic processes
    volatility
    unconditional variance
    Opis:
    We discuss the empirical importance of long term cyclical effects in the volatility of financial returns. Following Amado and Terasvirta (2009), Cizek and Spokoiny (2009) and others, we consider a general conditionally heteroscedastic process with stationarity property distorted by a deterministic function that governs the possible time variability of the unconditional variance. The function proposed in this paper can be interpreted as a finite Fourier approximation of an Almost Periodic (AP) function as defined by Corduneanu (1989). The resulting model has a particular form of a GARCH process with time varying parameters, intensively discussed in the recent literature. In the empirical analyses we apply a generalisation of the Bayesian AR(1)-GARCH model for daily returns of S&P500, covering the period of sixty years of US postwar economy, including the recently observed global financial crisis. The results of a formal Bayesian model comparison clearly indicate the existence of significant long term cyclical patterns in volatility with a strongly supported periodic component corresponding to a 14 year cycle. Our main results are invariant with respect to the changes of the conditional distribution from Normal to Student-t and to the changes of the volatility equation from regular GARCH to the Asymmetric GARCH.
    Źródło:
    Central European Journal of Economic Modelling and Econometrics; 2012, 4, 2; 95-116
    2080-0886
    2080-119X
    Pojawia się w:
    Central European Journal of Economic Modelling and Econometrics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On Bayesian Inference for Almost Periodic in Mean Autoregressive Models
    Wnioskowanie bayesowskie dla zmiennej w czasie prawie okresowej funkcji wartości oczekiwanej w modelu autoregresji
    Autorzy:
    Lenart, Łukasz
    Mazur, Błażej
    Powiązania:
    https://bibliotekanauki.pl/articles/1050550.pdf
    Data publikacji:
    2016-09-30
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Bayesian inference
    almost periodic mean function
    autoregressive model
    MCMC sampler
    wnioskowanie bayesowskie
    funkcja prawie okresowa wartości oczekiwanej
    model autoregresji
    próbnik MCMC
    Opis:
    The goal of the paper is to discuss Bayesian estimation of a class of univariate time-series models being able to represent complicated patterns of “cyclical” fluctuations in mean function. We highlight problems that arise in Bayesian estimation of parametric time-series model using the Flexible Fourier Form of Gallant (1981). We demonstrate that the resulting posterior is likely to be highly multimodal, therefore standard Markov Chain Monte Carlo (MCMC in short) methods might fail to explore the whole posterior, especially when the modes are separated. We show that the multimodality is actually an issue using the exact solution (i.e. an analytical marginal posterior) in an approximate model. We address that problem using two essential steps. Firstly, we integrate the posterior with respect to amplitude parameters, which can be carried out analytically. Secondly, we propose a non-parametrically motivated proposal for the frequency parameters. This allows for construction of an improved MCMC sampler that effectively explores the space of all the model parameters, with the amplitudes sampled by the direct approach outside the MCMC chain. We illustrate the problem using simulations and demonstrate our solution using two real-data examples.
    Źródło:
    Przegląd Statystyczny; 2016, 63, 3; 255-272
    0033-2372
    Pojawia się w:
    Przegląd Statystyczny
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    STATISTICAL ANALYSIS OF BUSINESS CYCLE FLUCTUATIONS IN POLAND BEFORE AND AFTER THE CRISIS
    Autorzy:
    Lenart, Łukasz
    Mazur, Błażej
    Pipień, Mateusz
    Powiązania:
    https://bibliotekanauki.pl/articles/517281.pdf
    Data publikacji:
    2016
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    APC processes
    subsampling
    Bayesian inference
    global economic crisis
    business cycle fluctuations
    Opis:
    The main objective of the paper is to investigate properties of business cycles in the Polish economy before and after the recent crisis. The essential issue addressed here is whether there is statistical evidence that the recent crisis has affected the properties of the business cycle fluctuations. In order to improve robustness of the results, we do not confine ourselves to any single inference method, but instead use different groups of statistical tools, including non-parametric methods based on subsampling and parametric Bayesian methods. We examine monthly series of industrial production (from January 1995 till December 2014), considering the properties of cycles in growth rates and in deviations from long-run trend. Empirical analysis is based on the sequence of expanding-window samples, with the shortest sample ending in December 2006. The main finding is that the two frequencies driving business cycle fluctuations in Poland correspond to cycles with periods of 2 and 3.5 years, and (perhaps surprisingly) the result holds both before and after the crisis. We, therefore, find no support for the claim that features (in particular frequencies) that characterize Polish business cycle fluctuations have changed after the recent crisis. The conclusion is unanimously supported by various statistical methods that are used in the paper, however, it is based on relatively short series of the data currently available.
    Źródło:
    Equilibrium. Quarterly Journal of Economics and Economic Policy; 2016, 11, 4; 769-783
    1689-765X
    2353-3293
    Pojawia się w:
    Equilibrium. Quarterly Journal of Economics and Economic Policy
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies