Identifying leading indicators of real activity and inflation for Turkey, 1988-2010

Altuğ, Sumru; Uluceviz, Erhan

doi:10.1787/jbcma-2013-5k4221j86n8v

Cited by 2 publications

(2 citation statements)

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“…However, it is seen that there are few studies on the subject in developing countries especially for Turkey. For instance, it is seen that some studies including of Murutoglu (1999), Alper (2000), Altug and Uluceviz (2011) and Atabek et al (2014) focus on creating of composite leading indicators, while Hacihasanoglu and Soytas (2011), Gulhan et al (2012), Topcu and Unlu (2013), Topcu (2014) and Eyuboglu and Eyuboglu (2019) try to analyze the relationship between composite leading indicators and stock market indices. In this section, we give information about the studies which examine the relationship between macroeconomic factors and stock markets at first and then deal with the studies the impacts of composite leading indicators on stock market indices in Turkey.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Stock Market Indices with the Composite Leading Indicators: Evidence from Turkey

Kılcı

2020

Sosyoekonomi

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The objective of this study is to evaluate the empirical performance of composite leading indicators (CLIs) in forecasting stock market indices for Turkey in the period from 2007:03 through 2019:07. After examining the stationarity of the series by using Narayan and Popp (2010) and Enders and Lee (2012) Fourier ADF unit root tests, the causality relationship from the composite leading indicators to stock market indices are tested by employing Enders and Jones (2016) Fourier Granger causality test. The results support the evidence of a causality relationship from composite leading indicators to BIST100, BIST Financial and BIST Industrial Indexes under structural breaks.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting Stock Market Indices with the Composite Leading Indicators: Evidence from Turkey

Kılcı

2020

Sosyoekonomi

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“…They find that only 10 percent of the indicators beat the benchmark in both periods, while around 20 percent of the indicators beat the benchmark in only one of the evaluation periods. Altug and Uluceviz (2013) analyze the forecasting performance of selected indicators for Turkish industrial production. Their results show that the forecast performance relative to an AR model changes depending on the evaluation sample.…”

Section: Forecast Evaluationmentioning

confidence: 99%

Forecasting Turkish Industrial Production Growth With Static Factor Models

Gunay¹

2015

International Econometric Review

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In this paper, we forecast industrial production growth for the Turkish economy using static factor models. We evaluate how the performance of the models change based on the number of factors we extract from our data as well as the level of aggregation for the series in the data set. We consider two evaluation samples for the out-of-sample forecasting exercise to assess the stability of the forecasting performance. We find that the effect of the data set size on the forecasting performance is not independent from the number of factors extracted from this data set. Rankings of the models change in different evaluation samples. We conclude that using a dynamic approach to evaluate models from different dimensions is important in the forecasting process.

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Identifying leading indicators of real activity and inflation for Turkey, 1988-2010

Cited by 2 publications

References 0 publications

Forecasting Stock Market Indices with the Composite Leading Indicators: Evidence from Turkey

Forecasting Stock Market Indices with the Composite Leading Indicators: Evidence from Turkey

Forecasting Turkish Industrial Production Growth With Static Factor Models

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