Possible effects of domestic and foreign factors on monetary policy implementation in Turkey: a DSGE-VAR approach

Abstract

In this paper, we attempt to explore the possible effects of technology, foreign output, price and terms of trade shocks on short-term interest rates in Turkey within a dynamic stochastic general equilibrium-vector autoregressive (DSGE-VAR) framework. In a sense, the primary aim of our paper is to analyse whether the Central Bank of the Republic of Turkey (CBRT) should consider the role of technology, foreign output, price and terms of trade shocks in its monetary policy implementation. Empirical results reveal that the above-mentioned factors have importance for the CBRT, which intends to control economy-wide interest rates in order to maintain price stability.

Keywords:

• foreign shocks
• domestic shocks
• short-term interest rates
• monetary policy
• DSGE-VAR model

JEL classification:

• E17
• E30
• F41

Notes

1. When the relative PPP condition is assumed to hold, π_t is equal to , where Δq_t is the change in terms of trade with respect to the previous period; is a world inflation shock. It is assumed that is an exogenous AR(1) process, , where is autoregressive coefficient and denotes the innovations of the process.

2. See Galí and Monacelli (2005) and Mihailov, Rumler, & Scharler, (2011).

3. The specification of the IS Curve by Lubik and Schorfheide (2007) depends on the small open economy model with two sectors (household and firms) as summarised by Galí and Monacelli (2005).

4. For more details, see Lubik and Schorfheide (2007).

5. We calculate q_t as the relative price of exports in terms of imports. The terms of trade is placed in the IS curve equation as in first difference form (Δ) to reflect the changes in (relative) prices that affect CPI inflation.

6. Technological progress, foreign output and terms of trade are assumed to evolve according to univariate AR(1) processes: z_t = ρ_zz_t−1 + ɛ_z,t, and Δq_t = ρ_qΔq_t−1 + ɛ_q,t, where, ρ_z, and ρ_q are autoregressive coefficient and ɛ_z,t, and ɛ_q,t denote the innovations of the AR(1) processes.

7. x_t satisfies the relation t = 1, ..., T, where d_t is q dimensional and deterministic. The residuals ɛ_t are assumed to be i.i.d. Gaussian with zero mean and positive definite covariance matrix. Π_l is a matrix (p × p), while Ψ is (p × q), measuring the expected value of x_t. The elements of the vector x_t (d_t) are all elements of the vector y_t (x_t) in the measurement equation of the DSGE model (Warne, 2012).

8. For the identification of SVAR models, see Breitung, Brüggemann, and Lütkepohl (2007).

9. For the details of the conditional variance decompositions of the DSGE model function, see Warne (2012).

10. The real GDP, consumer price inflation, overnight interbank rate and nominal exchange rate series are obtained from the OECD database; export and import price indices are extracted from the database of the CBRT.

11. For the details of the UR test, see Saikkonen and Lütkepohl (2003) and Lütkepohl (2007).

12. Prior distribution of model parameters identical and priors for the parameters of the model were determined to consider the fact that Turkey is a natural resource importing country. Therefore, domestic business cycle fluctuations are likely to have a major international relative price component in Turkey. Initial values, prior distribution of model parameters and parameter estimates can be provided upon request.

13. Estimation procedure requires the vector B_t, containing the variables used in the model to be modified as: B_t = (4r_t, 4π_t, y_t, Δe_t)′. For more information on the technical details of the estimation procedure, see Warne (2012).