Researcher:
Çakmaklı, Cem

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Cem

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Çakmaklı

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Çakmaklı, Cem

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2014 - 201942020 - 20225

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Researcher Of Publications: search.filters.isAuthorOfPublication.44284bf7-f694-4479-8206-5ddf3cb24033

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    Forecasting inflation using survey expectations and target inflation: evidence for Brazil and Turkey
    (Elsevier, 2016) Department of Economics; Department of Economics; Altuğ, Sumru; Çakmaklı, Cem; Faculty Member; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; College of Administrative Sciences and Economics; N/A; 107818
    In this paper, we formulate a statistical model of inflation that combines data on survey expectations with the inflation target set by central banks. Our model produces inflation forecasts that are aligned with survey expectations, thus integrating the predictive power of the survey expectations into the baseline model. Furthermore, we incorporate the inflation target set by the monetary authority in order to examine the effectiveness of monetary policy in forming inflation expectations, and therefore, in predicting inflation accurately. The results indicate that the predictive power of the proposed framework is superior to that of the model without survey expectations, as well as to the performances of several popular benchmarks, such as the backward- and forward-looking Phillips curves and a naive forecasting rule. (C) 2015 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
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    The future of universities from an institutional perspective
    (Peter Lang AG, 2021) Department of Business Administration; Department of Economics; Divarcı Çakmaklı, Anıl; Çakmaklı, Cem; Faculty Member; Faculty Member; Department of Business Administration; Department of Economics; College of Administrative Sciences and Economics; College of Administrative Sciences and Economics; 198542; 107818
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    Modeling and estimation of synchronization in size-sorted portfolio
    (Central Bank Republic Turkey, 2022) Paap, Richard; van Dijk, Dick; Department of Economics; Çakmaklı, Cem; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; 107818
    This paper examines the lead/lag relations between size-sorted portfolio returns through the lens of financial cycles governing these returns using a novel econometric methodology. Specifically, we develop a Markov-switching vector autoregressive model that allows for imperfect synchronization of cyclical regimes such as bull and bear market regimes in US large-, mid- and small-cap portfolio returns. This is achieved by characterizing the cycles of the mid- and small-cap portfolio returns in concordance with the cycle of large-cap portfolio returns together with potential phase shifts. We find that a three-regime model with distinct phase shifts across regimes characterizes the joint distribution of returns most adequately. These regimes are closely linked to the business cycle and small-cap portfolio returns are more sensitive to the cyclical phases than the large-cap portfolios. While all portfolios switch contemporaneously into boom and crash regimes, the large-cap portfolio leads the small-cap portfolio for switches to a moderate regime from a boom regime by a month. This suggests that small-cap portfolio adjusts with a delay to the relatively negative news compared to portfolios with larger market capitalization. We document that information diffusion accelerates in response to surprises related to the monetary policy. This reflects a link between financial returns and real economic activity from the viewpoint of 'financial accelerator theory' where portfolios with distinct size serve as a proxy for firm characteristics. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of Central Bank of The Republic of Turkey. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).
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    Using survey information for improving the density nowcasting of U.S. GDP
    (Taylor & Francis Inc) Demircan, Hamza; Department of Economics; Çakmaklı, Cem; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; 107818
    We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants' predictions, often used as a measure of "ambiguity," conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.
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    Modelling of economic and financial conditions for real-time prediction of recessions
    (Wiley, 2021) Demircan, Hamza; Department of Economics; Department of Economics; Çakmaklı, Cem; Altuğ, Sumru; Faculty Member; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; College of Administrative Sciences and Economics; 107818; N/A
    In this paper, we propose a method for real-time prediction of recessions using large sets of economic and financial variables with mixed frequencies. This method combines a dynamic factor model for the extraction of economic and financial conditions together with a tailored Markov regime switching specification for capturing their cyclical behaviour. Unlike conventional methods that estimate a single common cycle governing economic and financial conditions or extract economic and financial cycles in isolation of each other, the model allows for a common cycle which is reflected with potential phase shifts in the financial conditions estimated alongside with other parameters. This, in turn, provides timely recession predictions by enabling efficient modelling of the financial cycle systematically leading the business cycle. We examine the performance of the model using a mixed frequency ragged-edge data set for Turkey in real time. The results show evidence for the superior predictive power of our specification by signalling oncoming recessions (expansions) as early as 3.6 (3.0) months ahead of the actual realization.
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    Posterior-predictive evidence on us inflation using extended new keynesian phillips curve models with non-filtered data
    (Wiley, 2014) Basturk, Nalan; Ceyhan, S. Pinar; Van Dijk, Herman K.; Department of Economics; Çakmaklı, Cem; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; 107818
    Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended New Keynesian Phillips curve (NKPC) models. It is shown that mechanical removal or modeling of simple low-frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic NKPC models are extended to include structural time series models that describe typical time-varying patterns in levels and volatilities. Forward- and backward-looking expectation components for inflation are incorporated and their relative importance is evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation-based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long-run stability between inflation and marginal costs. Backward-looking inflation appears stronger than forward-looking inflation. Levels and volatilities of inflation are estimated more precisely using rich NKPC models. The extended NKPC structures compare favorably with existing basic Bayesian vector autoregressive and stochastic volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of deflation in recent years.
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    Getting the most out of macroeconomic information for predicting excess stock returns
    (Elsevier, 2016) van Dijk, Dick; Department of Economics; Çakmaklı, Cem; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; 107818
    This paper documents the fact that the factors extracted from a large set of macroeconomic variables contain information that can be useful for predicting monthly US excess stock returns over the period 1975-2014. Factor-augmented predictive regression models improve upon benchmark models that include only valuation ratios and interest rate related variables, and possibly individual macro variables, as well as the historical average excess return. The improvements in out-of-sample forecast accuracy are significant, both statistically and economically. The factor-augmented predictive regressions have superior market timing abilities, such that a mean variance investor would be willing to pay an annual performance fee of several hundreds of basis points to switch from the predictions offered by the benchmark models to those of the factor-augmented models. One important reason for the superior performance of the factor-augmented predictive regressions is the stability of their forecast accuracy, whereas the benchmark models suffer from a forecast breakdown during the 1990s. (C) 2016 Published by Elsevier B.V. on behalf of International Institute of Forecasters.
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    PublicationOpen Access
    Role of institutional, cultural, and economic factors on the effectiveness of the lockdown measures
    (Elsevier, 2022) Department of Economics; Çakmaklı, Cem; Demiralp, Selva; Ergönül, Önder; Yeşiltaş, Sevcan; Yıldırım, Muhammed Ali; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Economics; School of Medicine; College of Administrative Sciences and Economics; 107818; 42533; 110398; N/A; 219280
    Objective: this study considered the role of institutional, cultural and economic factors in the effectivemess of lockdown measures during the coronavirus pandemic. Earlier studies focusing on cross-sectional data found an association between low case numbers and a higher level of cultural tightness. Meanwhile, institutional strength and income levels revealed a puzzling negative relationship with the number of cases and deaths. Methods: data available at the end of September 2021 were used to analyse the dynamic impact of these factors on the effectiveness of lockdowns. The cross-sectional dimension of country-level data was combined with the time-series dimension of pandemic-related measures, using econometric techniques dealing with panel data. Findings: greater stringency of lockdown measures was associated with fewer cases. Institutional strength enhanced this negative relationship. Countries with well-defined and established laws performed better for a given set of lockdown measures compared with countries with weaker institutional structures. Cultural tightness reduced the effectiveness of lockdowns, in contrast to previous findings at cross-sectional level. Conclusion: institutional strength plays a greater role than cultural and economic factors in enhancing the performance of lockdowns. These results underline the importance of strengthening institutions for pandemic control.
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    Modeling the density of US yield curve using Bayesian semiparametric dynamic Nelson-Siegel model
    (Taylor _ Francis, 2019) Department of Economics; Çakmaklı, Cem; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; 107818
    This paper proposes the Bayesian semiparametric dynamic Nelson-Siegel model for estimating the density of bond yields. Specifically, we model the distribution of the yield curve factors according to an infinite Markov mixture (iMM). The model allows for time variation in the mean and covariance matrix of factors in a discrete manner, as opposed to continuous changes in these parameters such as the Time Varying Parameter (TVP) models. Estimating the number of regimes using the iMM structure endogenously leads to an adaptive process that can generate newly emerging regimes over time in response to changing economic conditions in addition to existing regimes. The potential of the proposed framework is examined using US bond yields data. The semiparametric structure of the factors can handle various forms of non-normalities including fat tails and nonlinear dependence between factors using a unified approach by generating new clusters capturing these specific characteristics. We document that modeling parameter changes in a discrete manner increases the model fit as well as forecasting performance at both short and long horizons relative to models with fixed parameters as well as the TVP model with continuous parameter changes. This is mainly due to fact that the discrete changes in parameters suit the typical low frequency monthly bond yields data characteristics better.
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    PublicationOpen Access
    Ambiguous business cycles: a quantitative assessment
    (Elsevier, 2020) Altuğ, Sumru; Collard, Fabrice; Mukerji, Sujoy; Department of Economics; Çakmaklı, Cem; Özsöylev, Han Nazmi; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; 107818; N/A
    In this paper, we examine the cyclical dynamics of a Real Business Cycle model with ambiguity averse consumers and investment irreversibility using the smooth ambiguity model of Klibanoff et al. (2005, 2009). Ambiguity of belief about the productivity process arises as agents do not know the process driving variation in aggregate TFP, and they must make inferences regarding the true process at the same time as they infer the behavior of the unobserved temporary component using a Kalman filtering algorithm. Our findings may be summarized as follows. First, the standard business cycle facts hold in our framework, which are not altered significantly by changes in the degree of ambiguity aversion. Second, we demonstrate a role for information and learning effects, and show that lower initial ambiguity or greater confidence coupled with learning dynamics lowers the volatility and increases the persistence in all of the key macroeconomic variables. Third, comparing the performance of our model to the New Keynesian business cycle model of Ilut and Schneider (2014) with maxmin expected utility, we find that the version of their model without nominal and real frictions turns out to have limited success at matching the moments for the quantity variables. In the maxmin expected utility framework, the worst case scenario instills too much caution on the part of agents who, in the absence of a key set of nominal and real frictions, end up excessively reducing their responses to TFP shocks.