This appendix has four main sections providing further details regarding some of our main results. First, we present a detailed description of the structural dynamic stochastic general equilibrium (DSGE) model that underpins our quantitative results. The next two sections discuss model estimation and sensitivity analysis, while the fourth section sheds further light on model dynamics, and the final section presents the counterfactual simulations using the time series of year-over-year growth rates.
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)| false Kaminsky, Graciela L., Carmen M. Reinhart, and Carlos A. Vegh, 2005, “ When It Rains, It Pours: Procyclical Capital Flows and Macroeconomic Policies,” in NBER Macroeconomics Annual 2004, Vol. 19, ed. By pp. Mark Gertlerand Kenneth Rogoff, 11– 82( Cambridge, Massachusetts: National Bureau of Economic Research).
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The views expressed here are those of the authors and do not necessarily represent those of any institution the authors are or have been affiliated with. For insightful comments we are grateful to Hoe Ee Khor, Jack Joo Ree, Markus Rodlauer, Thomas Rumbaugh, and Phurichai Rungcharoenkitkul
For further details, see: http://www.bot.or.th/English/MonetaryPolicy/Target/Pages/index.aspx
In terms of theory, our model brings together elements from papers including Adolfson and others (2007), Bernanke and others (1999), Elekdag and Tchakarov (2007), and Gertler and others (2007) among many others, while, in order to facilitate estimation, we build on the work of Smets and Wouters (2003, 2007) and Elekdag and others (2006). For a recent example, see Alp and Elekdag (2011).
Note that this shock actually consists of two components: the first is the exogenous component discussed above. The second component is actually endogenous and depends on the levels of debt outstanding thereby accounting for sovereign risk (in line with other recent open-economy DSGE models, see Appendix for further details).
We follow Gertler and others (2007) and also use domestically-denominated debt when modeling the financial accelerator. Given the risks associated with foreign currency-denominated debt, adding this feature as in Elekdag and Tchakarov (2007) is a refinement worth pursuing in future research.
The model also includes shocks to the Philips curves, but these are typically used to capture terms of trade-related shocks (including commodity price fluctuations).
For further details: http://www.bot.or.th/English/MonetaryPolicy/Target/Pages/Target.aspx
The model is intentionally estimated to include the tumultuous period of 2008-2011 during which Thailand had to endure three major shocks to evaluate the effectiveness of countercyclical monetary policy. However, it should be recognized that the sample ends only one quarter after the Thai floods intensified, and therefore, parameter estimates may be less stable than in the case when a more tranquil sample was used for estimation. Nonetheless, the results are generally plausible, and yield informative policy implications.
Nonetheless, after estimating the model over the 1993–2012 sample, three results noteworthy. First, the main structural shocks accounting for year-over-year output growth are those to foreign demand (capturing, in large part, shocks transmitted via the trade channel), trend productivity (associated with shocks primarily associated with the natural disasters), and those to government spending (capturing, in a cursory way, fiscal policy). As noted above, the trend productivity shocks can permanently affect the growth trend of the economy, and as argued by Aguilar and Gopinath (2007), is a key determinant of business cycle fluctuations across emerging economies. Second, in tandem with lower output volatility in the second half of the sample, the standard deviations of all major structural shocks are also lower in the 2001–2012 period. Third, the correlation between the government spending and monetary policy shocks decreases in the second half of the sample, indicating that policies have become more countercyclical. In the case of government spending shocks, a sizeable positive correlation (typical in many emerging economies, see Kaminsky and others, 2005) switches to a negative correlation.
Just as the model-based framework assumes that the inflation targeting regimes are fully credible, it also assumes that the exchange rate regimes are fully credible. While the latter assumption is harder to justify, the credibility of both regimes is needed for comparability. For a lack of a better term, credibility was used, but perhaps sustainability is a more related or even more appropriate characterization.
All results are available from the authors’ upon request; Table 4 (below) presents results in further detail.
It should be underscored that this paper focuses on the role of interest rate and exchange rate fluctuations, and therefore does not capture the role of other policies.
China, Japan, the United States, and Hong Kong SAR are Thailand’s largest export destinations accounting for 12, 11, 10, and 7 percent, respectively, of total exports.
Specifically, IFS codes 57860…ZF… and 57860P…ZF…, respectively for the BOT policy and lending rates, where 578 is Thailand’s IFS country code; using the discount rate yields on even lower external finance premium proxy.
Additional information on our estimation results including, for example, kernel density estimates for the posteriors, together with the priors are available from the authors upon request.