Inflation refers to an ongoing and overall comprehensive increase in the overall level of goods and services price in the economy. Today, inflation, which is attempted to be kept under control by central banks or, in the same way, whose price stability is attempted, consists of continuous price changes that occur in all the goods and services used by the consumers. Undoubtedly, in terms of economy, in addition to the realized inflation, inflation expectations are also gaining importance. This situation requires forecasting the future rates of inflation. Therefore, reliable forecasting of the future rates of inflation in a country will determine the policies to be applied by the decision-makers in the economy. The aim of this study is to predict inflation in the next period based on the consumer price index (CPI) data with two alternative techniques and to examine the predictive performance of these two techniques comparatively. Thus, the first of the two main objectives of the study are to forecast the future rates of inflation with two alternative techniques, while the second is to compare the two techniques with respect to statistical and econometric criteria and determine which technique performs better in comparison. In this context, the 9-month inflation in April-December 2019 was forecast by Box-Jenkins (ARIMA) models and Artificial Neural Networks (ANN), using the CPI data which consist of 207 data from January 2002 to March 2019 and the predictive performance of both techniques was examined comparatively. It was observed that the results obtained from both techniques were close to each other.
The prediction of an economic crisis is the most critical area of study for all actors related to the economy. Crises, a sign of uncertainty, do not have a specific timeline, but they can be predicted by analyzing particular indications. Studies on predicting the crisis are commonly related to macroeconomic variables. This study addresses an alternative approach to predicting crisis periods, which involves analyzing changes in the trading volumes of companies listed on Borsa Istanbul (BIST) instead of relying solely on macroeconomic variables. The study aims to examine the transaction volume data from 169 firms that regularly traded in BIST between 2000 and 2018. The predictability of economic crises in Türkiye has been investigated by applying binary logistic regression analysis, a methodology commonly employed in the literature as a signal approach for detecting economic crises. Some statistically significant parameters were discovered positive, and some were found negative in estimated logistic regression models, and the companies to which the statistically insignificant parameters belonged were evaluated as companies that did not give a signal for the economic crisis model. The findings suggest that changes in the trading volume of many companies, not just a few ones, can be a valuable predictor of crises.
Elektrik talep tahmini konusunda yapılan çalışmalar gerek çok komplike yapılar gerekse çok daha kolay yöntemler olsun, kısa dönemde (saat, gün ve hafta) benzer çıktıları üretecektir. Uzun dönemli (yıllık) tahminler ise gerçeğe yakın sonuçlar üretmeyebilir. Bunun nedeni, uzun dönemli tahminlerde kullanılan değişkenlerin parametrelerinde yıldan yıla farklılıklar olabilir. Bu duruma en güncel örnek ise 2020 yılında meydana gelen COVID-19 pandemisi gösterilebilir. Orta dönemli (aylık, çeyrek yıllık) çalışmalar ise, yukarıda sayılan durumlara nazaran daha sağlıklı sonuçlar verebilir. Bu çalışmada orta vadede, elektrik talebine etki edebilecek tüketici fiyat endeksi, ülkeye gelen turist sayısı, işsizlik ve sanayi üretim endeksi değişkenleri ile VAR modeli ile incelenmiştir. Bulgular ise Toda Yamamoto nedensellik sonuçları ile varyans ayrıştırması analizi sonuçları arasında paralellik bulunmaktadır. Ayrıca ülkeye gelen turist sayısından elektrik tüketimine tek yönlü, işsizlik ve sanayi üretim endeksi arasında çift yönlü nedensellik ilişki bulunmuştur.
Inflation refers to an ongoing and overall comprehensive increase in the overall level of goods and services price in the economy. Today, inflation, which is attempted to be kept under control by central banks or, in the same way, whose price stability is attempted, consists of continuous price changes that occur in all the goods and services used by the consumers. Undoubtedly, in terms of economy, in addition to the realized inflation, inflation expectations are also gaining importance. This situation requires forecasting the future rates of inflation. Therefore, reliable forecasting of the future rates of inflation in a country will determine the policies to be applied by the decision-makers in the economy. The aim of this study is to predict inflation in the next period based on the consumer price index (CPI) data with two alternative techniques and to examine the predictive performance of these two techniques comparatively. Thus, the first of the two main objectives of the study are to forecast the future rates of inflation with two alternative techniques, while the second is to compare the two techniques with respect to statistical and econometric criteria and determine which technique performs better in comparison. In this context, the 9-month inflation in April-December 2019 was forecast by Box-Jenkins (ARIMA) models and Artificial Neural Networks (ANN), using the CPI data which consist of 207 data from January 2002 to March 2019 and the predictive performance of both techniques was examined comparatively. It was observed that the results obtained from both techniques were close to each other.
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