The Forecasting Performance of Setar Models: An Empirical Application

Boero, Gianna; Lampis, Federico

doi:10.1111/boer.12068

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Thus, the politicians need the scientific reliable information of the related indicators in the country. Time Series Analysis is a suitable method that provides us to reach this information [16]. In this article, we forecast the monthly data of the IPI series that is one of the most important economic indicators in Turkey.…”

Section: Resultsmentioning

confidence: 99%

Examination of industry production index in Turkey with time series method

Çekim¹

2018

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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In this paper, the time series analysis is conducted to the monthly industrial production index data calculated between 2005 and 2017 by TURKSTAT. The aim of the study is to define the industrial production index with the time series chart, to find the suitable time series model for the index and to forecast the future values of the index. For this purpose, we make the series stationary by taking both the first difference and the second seasonal difference of the series to perform the Box-Jenkins models. As a result of the analysis, SARIMA(1,1,1)(3,2,0) 12 model is determined as the most suitable model for the series. Using this model, the forecast values for the months of 2018 of the index series are calculated.

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Section: Resultsmentioning

confidence: 99%

Examination of industry production index in Turkey with time series method

Çekim¹

2018

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…Additionally, the models are re-specified at the end of each year, such that the first specification is based on data up to fourth quarter of 2005 and the last specification on data up to fourth quarter of 2013. This approach is inspired to the recommendations of Teräsvirta et al (2005) and Boero and Lampis (2016), who suggest that frequent model re-specification increases the forecast accuracy of nonlinear models. Moreover, this procedure allows us to take into account the structural breaks occurred between 2008 and 2009 when the Great Recession hit Spanish economy severely.…”

Section: Empirical Analysis and Forecastmentioning

confidence: 99%

“…The forecasting exercise is performed on 1-step-ahead and 4-steps-ahead point forecasts using a recursive scheme with an expanding window to approximate a genuine forecasting environment. This approach is based on the recommendations of Teräsvirta et al (2005), Ferrara et al (2012) and Boero and Lampis (2016), who found it very convenient when the forecasting accuracy of nonlinear models is considered. In the next section, we present the statistical information available for the Basque Country, and in the third section, we briefly describe the models estimated and compare the forecasting accuracy of our models.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the sectoral GVA of a small Spanish region

Lampis

2016

Econom. bus. letters

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Our main goal in this paper is to evaluate the point forecasting accuracy of several time series econometric models when applied to a small Spanish region. The variable of interest is the sectoral GVA of the Basque Country. The results support the use of causal models, which outperform univariate models, such as ARMA and SETAR, in forecasting accuracy. The use of a causal model, such as a simple Dynamic Regression model, offers a systematic advantage in the case of a small regional economy for which abundant regional statistical information is available.

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“…As a result of the evaluations, they reported that the best modeling for the time series in question was the AAR-SETAR modeling. Boero and Lampis (2016) in [3] conducted a study for 4 major European countries and applied SETAR modeling by using Industrial Production Index (IPI) values, and investigated the belief that claimed that the dynamic determination process increased the prediction performance (Terӓsvirta et al (2005)). However, the literature is not rich in terms of the properties of the sampling on this model group and in terms of test statistics (Hansen, 1997).…”

Section: Introduction and The Literaturementioning

confidence: 99%

SETAR (Self-exciting Threshold Autoregressive) Non-linear Currency Modelling in EUR/USD, EUR/TRY and USD/TRY Parities

Firat¹

2017

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In economies that are open to foreign markets the numerical value of the currencies as a macroeconomic variable is of great importance especially when the mutual dependency among the economies is concerned. When it is considered in terms of political economy, the targeted level of the currencies have vital importance especially in economies that have the characteristics of export-driven growth and in economies that struggle not to disrupt the picture in macroeconomic design. When it is considered that each time series has a structure that is sensitive to its own internal dynamics (sometimes these dynamics are expressed as the time series components), these dynamics provide us with coordinates for estimations and may eliminate the compulsory dependency on the outsourced variables at a serious level. This is exactly what has been done in this study. First of all, the non-linear time series analyses are examined in terms of linearity tests, and the linearity tests are applied for all parties and for different time periods. Then, the SETAR Modelling, which is the title of the study, has been applied in order to explain the non-linear pattern in detail. The SETAR Modelling process and other definitions statistical analyses of this model have been applied in relevant parities for separate time periods. The SETAR model, which is one of the TAR Group modeling, shows a better performance than many other linear and non-linear modeling. In this study, the secondary purpose is to express that the SETAR model performance is superior to the other models by considering the observation values of the parities.

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The Forecasting Performance of Setar Models: An Empirical Application

Cited by 10 publications

References 24 publications

Examination of industry production index in Turkey with time series method

Examination of industry production index in Turkey with time series method

Forecasting the sectoral GVA of a small Spanish region

SETAR (Self-exciting Threshold Autoregressive) Non-linear Currency Modelling in EUR/USD, EUR/TRY and USD/TRY Parities

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