# **B Model calibration and estimation**

This section reports the estimation and calibration of the model. In order for certain model variables to match their empirical counterparts at the steady state, some parameters have been restricted. All structural parameters have been estimated using Bayesian techniques. Here, we report first calibrated parameters and then estimated parameters with their 90% credibility intervals.

## **B.1 Data**

We use time series data for 31 observed variables over 2000Q2-2022Q3, which are reported in Table B1.<sup>22</sup> Twenty-six observables are available at the quarterly frequency.<sup>23</sup> However, energy balances from the International Energy Agency (IEA) and CO2 emissions from the Climate Watch (CAIT) database maintained by the World Resources Institute are annual. We use mixed frequency data in the estimations. The missing quarterly observations of annual (and quarterly) data are obtained by the Kalman (1960) filter. This feature of our estimation technique is highly relevant for emerging market economies where data are available but generally with gaps or at lower frequencies than what the models are set at. We discuss the details of how we deal with missing observations below.

We detrend policy rate, 3-month interest rate, inflation rate, low-skilled wage inflation rate, high-skilled wage inflation rate, real exchange rate, employment rate, RoW interest rate and RoW inflation rate by the double-sided Hodrick and Prescott (1980) filter. For the capacity utilization rate, we use demeaned data. For all other observables, we use demeaned growth rates. We subtract the growth rate of the labor force from volume-related variables such as macro aggregates, emissions, sectoral inputs/outputs and hours worked. We take prices relative to the GDP deflator to match the definition of prices in the model.

Some approximations are needed due to data limitations. The rate of capacity utilization is for manufacturing which, empirically, is the biggest chunk in our core goods sector. We use hours worked in manufacturing only, which covers all of our estimation period.<sup>24</sup> For low-skilled wage inflation, we use money wages of construction workers, 80% of whom have less than high school education (Cengiz and Tekg ̈u ̧c 2022). For high-skilled wage inflation, we take wages of information and communication sector, which attracts mainly university degree holders. For global fossil fuel prices, we use OECD producer prices from the IEA.

> 22Data sources are as follows: Components of GDP, employment, interest rates, capacity utilization rate and hours worked (OECD Main Economic Indicators), public-private investment, compensation of employees, unemployment, high-skilled and low-skilled wages (TurkStat), price indexes, EU output, inflation and prices (Eurostat), energy production and prices (IEA), emissions (CAIT) and real effective exchange rate (BIS).

> 23Public and private investment expenditure series are not available after 2016Q2. Low-skilled wage inflation is available over 2005Q2-2017Q4 and high-skilled wage inflation is available after 2009Q2.

> 24Total hours worked data from TurkStat is available after 2010, and there is a close match between quarterly growth rates of manufacturing hours worked and total hours worked over 2010-2022.

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