Publications

In this study, we nowcast quarter-over-quarter US GDP growth rates between 2000Q2 and 2018Q4 using tree-based ensemble machine learning models, namely, bagged decision trees, random forests, and stochastic gradient tree boosting. For obtaining variables from our large-scale data set, we adopt a dynamic factor model. Our results show that tree-based ensemble models usually outperform linear dynamic factor models. Factors obtained from real variables appear to be more influential in machine learning models.

We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Then, dynamic factors are extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in sample and out of sample.

In this study, we propose a flexible two-stage least square regression to deal with both instability and endogeneity problems in the policy reaction function of the Central Bank of the Republic of Turkey (CBRT). By analyzing the period between 2006 and 2019, we clearly show that the monetary policy function of the CBRT changes over time, and using a time-invariant monetary policy rule model would yield misleading results.

The paper analyzes the point and density predictive performance of alternative nowcast combination schemes in the context of bridge equations for the Turkish unemployment rate. Furthermore, we also nowcast the unemployment rate by using dynamic factor models (DFMs). Our results indicate that most of the sophisticated forecast combination methods have better predictive accuracy than the simple forecast combinations, especially in higher forecast horizons, which constitutes a case for the nowcast combination puzzle.

Real gross domestic product (GDP) data in Turkey are released with a very long delay. This means that policy makers, the media, and market practitioners have to infer the current state of the economy by examining data that are more timely and are released at higher frequencies than the GDP. This paper proposes an econometric model that allows us to read through these more current and higher-frequency data automatically, and translate them into nowcasts for the Turkish real GDP. Our model outperforms the nowcasts produced by the Central Bank of Turkey, the International Monetary Fund, and the Organisation for Economic Co-operation and Development.