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    Wyświetlanie 1-3 z 3
    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
    Tytuł:
    Bayesian Inference for a Deterministic Cycle with Time-Varying Amplitude: The Case of the Growth Cycle in European Countries
    Autorzy:
    Lenart, Łukasz
    Powiązania:
    https://bibliotekanauki.pl/articles/2076240.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deterministic cycle with time-varying amplitude
    Bayesian inference
    almost periodic function
    growth cycle
    industrial production
    Opis:
    The main goal of this paper is to propose the probabilistic description of cyclical (business) fluctuations. We generalize a fixed deterministic cycle model by incorporating the time-varying amplitude. More specifically, we assume that the mean function of cyclical fluctuations depends on unknown frequencies (related to the lengths of the cyclical fluctuations) in a similar way to the almost periodic mean function in a fixed deterministic cycle, while the assumption concerning constant amplitude is relaxed. We assume that the amplitude associated with a given frequency is time-varying and is a spline function. Finally, using a Bayesian approach and under standard prior assumptions, we obtain the explicit marginal posterior distribution for the vector of frequency parameters. In our empirical analysis, we consider the monthly industrial production in most European countries. Based on the highest marginal data density value, we choose the best model to describe the considered growth cycle. In most cases, data support the model with a time-varying amplitude. In addition, the expectation of the posterior distribution of the deterministic cycle for the considered growth cycles has similar dynamics to cycles extracted by standard bandpass filtration methods.
    Źródło:
    Central European Journal of Economic Modelling and Econometrics; 2018, 3; 233-262
    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
      Wyświetlanie 1-3 z 3

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