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    Wyświetlanie 1-3 z 3
    Tytuł:
    An analysis of the Pollution Haven Hypothesis in the context of Turkey: A nonlinear approach
    Autorzy:
    Temurlenk, M. Sinan
    Lögün, Anıl
    Powiązania:
    https://bibliotekanauki.pl/articles/2034007.pdf
    Data publikacji:
    2022-04-15
    Wydawca:
    Uniwersytet Ekonomiczny w Poznaniu
    Tematy:
    Pollution Haven Hypothesis
    foreign direct investments (FDI)
    emissions
    nonlinear autoregressive distributed lag model
    Turkey
    Opis:
    Foreign direct investment (FDI) is an important driver of countries' economic development. Factors such as looser environmental regulations may cause dirty FDI to flow mainly to developing countries. This is explained by the Pollution Haven Hypothesis. eTh paper aims to investigate whether the Pollution Haven Hypothesis is valid in Turkey using the nonlinear autoregressive distributed lag (NARDL) approach for the period 1974-2017. eTh results show that FDI inflows and carbon emissions have asymmetric eefcts in both the short and long term for Turkey, supporting the Pollution Haven Hypothesis. Furthermore, there is a link between carbon emissions and trade openness, manufacturing and economic growth. Policymakers should develop the policies necessary to transfer clean technologies to Turkey by providing improvements and technical advances for a more eficient energy use.
    Źródło:
    Economics and Business Review; 2022, 8, 1; 5-23
    2392-1641
    Pojawia się w:
    Economics and Business Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis and comparison of long short-term memory networks short-term traffic prediction performance
    Autorzy:
    Dogan, Erdem
    Powiązania:
    https://bibliotekanauki.pl/articles/2091136.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    deep learning
    traffic flow
    short-term
    prediction
    LSTM
    nonlinear autoregressive
    training set size
    uczenie głębokie
    ruch uliczny
    krótki termin
    prognoza
    autoregresja nieliniowa
    Opis:
    Long short-term memory networks (LSTM) produces promising results in the prediction of traffic flows. However, LSTM needs large numbers of data to produce satisfactory results. Therefore, the effect of LSTM training set size on performance and optimum training set size for short-term traffic flow prediction problems were investigated in this study. To achieve this, the numbers of data in the training set was set between 480 and 2800, and the prediction performance of the LSTMs trained using these adjusted training sets was measured. In addition, LSTM prediction results were compared with nonlinear autoregressive neural networks (NAR) trained using the same training sets. Consequently, it was seen that the increase in LSTM's training cluster size increased performance to a certain point. However, after this point, the performance decreased. Three main results emerged in this study: First, the optimum training set size for LSTM significantly improves the prediction performance of the model. Second, LSTM makes short-term traffic forecasting better than NAR. Third, LSTM predictions fluctuate less than the NAR model following instant traffic flow changes.
    Źródło:
    Zeszyty Naukowe. Transport / Politechnika Śląska; 2020, 107; 19--32
    0209-3324
    2450-1549
    Pojawia się w:
    Zeszyty Naukowe. Transport / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    State-dependent Autoregressive Models with p Lags: Properties, Estimation and Forecasting
    Autorzy:
    Gobbi, Fabio
    Mulinacci, Sabrina
    Powiązania:
    https://bibliotekanauki.pl/articles/2119921.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    convolution-based autoregressive models
    level-increment dependence
    nonlinear time series
    maximum likelihood
    forecasting accuracy
    Opis:
    In this paper we consider a class of nonlinear autoregressive models in which a specific type of dependence structure between the error term and the lagged values of the state variable is assumed. We show that there exists an equivalent representation given by a p-th order state-dependent autoregressive (SDAR(p)) model where the error term is independent of the last p lagged values of the state variable (yt−1, . . . , yt−p) and the autoregressive coefficients are specific functions of them. We discuss a quasi-maximum likelihood estimator of the model parameters and we prove its consistency and asymptotic normality. To test the forecasting ability of the SDAR(p) model, we propose an empirical application to the quarterly Japan GDP growth rate which is a time series characterized by a level-increment dependence. A comparative analyses is conducted taking into consideration some alternative and competitive models for nonlinear time series such as SETAR and AR-GARCH models.
    Źródło:
    Central European Journal of Economic Modelling and Econometrics; 2022, 1; 81-108
    2080-0886
    2080-119X
    Pojawia się w:
    Central European Journal of Economic Modelling and Econometrics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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