Modelling Inflation Uncertainty with Structural Breaks Case of Turkey (1994–2013)

Abstract

In recent years, the importance attached to the concept of volatility has increased and become a phenomenon frequently encountered in every field ranging from financial markets to macroeconomic indicators. In this study, inflation data obtained from CPI index for the period of 1994:01–2013:12 in Turkey was used to determine the best representative of the inflation uncertainty. To realize this, both symmetric and asymmetric GARCH-type models were employed. Since there are many factors that may lead to structural change within the economic course of Turkey, a structural break in the series has first been investigated. By administering Bai-Perron structural break test, two different break points both in mean and variance have been detected to be in February 2002 and in June 2001, respectively. The inclusion of those break points to the related equations, appropriate forecasting models were projected. Moreover it was found that, while in the periods prior to the break in both variance and mean the inflation itself was the reason for inflation uncertainty, following the dates of the break, the relationship changed bidirectionally. In the meantime, when the series was taken as a whole without considering the break, bidirectional causality relationship was also detected in the series.

Modelling UK House Prices with Structural Breaks and Conditional Variance Analysis

“…Similarly, Göktaş and Dişbudak (2014) Bai andPerron (1998, 2003) extended the Quandt-Andrews test by allowing for multiple unknown breakpoints, which is the structural break test we use in our analysis. However, we also need to test for potential breakpoints in the variance.…”

“…However, we also need to test for potential breakpoints in the variance. Consequently, similar to Göktaş and Dişbudak (2014), the squared residuals of the AR models are regressed on a constant, and Bai-Perron tests are then reperformed.…”

“…However, as breaks in the variance could look as ARCH effects (Diebold 1986), we also tested for breakpoints in the variance of each region. Following Göktaş and Dişbudak (2014), the squared residuals of the least squares estimation of the above AR models, taking the identified breakpoints into account as appropriate, were regressed on a constant, and Bai-Perron tests were then reperformed. Again for any breakpoint identified by the Bai-Perron tests, we also performed Chow's breakpoint tests in order to verify whether a suggested breakpoint date is indeed a structural break.…”

“…Hence we tested for breakpoints in the variance of each property type as well. The squared residuals of the least squares estimation of the above AR mean equations were regressed on a constant, taking the identified breakpoints into account, similar to Göktaş and Dişbudak (2014), and Bai-Perron tests were reperformed. These results are also reported in Table 5.…”

Transportation Dynamic Behaviour of Structural Break and Consumer Price Index

“…These researchers also indicated that the break point was in 1985; the beginning of the Great Moderation period and other macro variables stabilised inflation and its aftermath on relative prices across US cities. On the other hand, [10] studied the concept of volatility and uncertainty in inflation using CPI index for the period of 1994 until 2013 in Turkey. Using Bai-Perron test, two different break points in terms of mean and variance were identified, namely February 2002 and June 2001.…”