The Vietnamese lending rate, policy-related rate, and monetary policy post-1997 Asian financial crisis

Figures & data

Figure 1. Vietnamese lending rate, policy-related rate, and their spread since 1997.

Table 1. Estimation results, Vietnamese monthly data, 1997:01–2014:06

$\begin{array}{c}S{P}_t=3.673451+0.010189Tren{d}_t-2.507404Dumm{y}_t+{\mathit{\epsilon }}_t\hfill \\ (13.{6911}^{\ast })(3.{2184}^{\ast })(-6.{4358}^{\ast })\hfill \end{array}$
ln L = −347.5726 R^2 = 0.83934	DW statistic^(a) = 0.3510	F(2, 207) = 30.2036^*

^* Significances at 1% level.

Notes: As articulated by Enders and Siklos (2001, p. 166), in this type of model specification, ε_t may be contemporaneously correlated.

Table 2. Perron’s endogenous unit root test, Vietnamese data, 1997:01–2014:06

$\begin{array}{c}S{P}_t=1.2306+1.3333DU+0.0090t-0.0161DT+2.9775D(T_b)+0.6057S{P}_{t-1}+{\mathit{\upsilon }}_t\hfill \\ (4.{6964}^{\ast })(1.{7023}^{\ast \ast \ast })(3.{0487}^{\ast })(-2.{7621}^{\ast })(3.{7574}^{\ast })(8.{1231}^{\ast })\hfill \end{array}$
Number of augmented lags: k = 12	Break Date: March 2007	t(α = 1) = −5.28907^*

Note: Critical values for t-statistics in parentheses. Critical values based on n = 100 sample for the break date (Perron, 1997).

^* Significances at 1% level.

^***Significances at 10% level.

Table 3. Unit root and tests of asymmetry, Vietnamese data, 1997:01–2014:06

ρ1	ρ2	τ	H0 : ρ1 = ρ2 = 0	H0 : ρ1 = ρ2	aic	sic
−0.7754^*	−0.1317^*	1.38076	Φμ = 13.8472^*	F =15.8852^*	−0.3910 −0.3889
QLB (12) = 15.2350[0.2288]			ln L = −241.3522	F(4, 203) = 11.7963^*	D.W. = 1.8414

Notes: The null hypothesis of a unit root, H₀ : ρ₁ = ρ₂ = 0, uses the critical values from Enders and Siklos (2001). The null hypothesis of symmetry, H₀ : ρ₁ = ρ₂, uses the standard F distribution. τ is the threshold value determined via the Chan’s (1993) method. Q_{LB (12)} denotes the Ljung–Box Q-statistic with twelves lags.

^* Significance at 1% level.

Table 4. Vietnamese lending and central bank’s policy-related rates, data, 1997:01–2014:06

$\begin{array}{c}\mathrm{\Delta }L{R}_t=-0.0301+0.0836I_t{\widehat{\mathit{\epsilon }}}_{t-1}-0.0716(1-I_t){\widehat{\mathit{\epsilon }}}_{t-1}+A_{11}(L)\mathrm{\Delta }L{R}_{t-i}+A_{12}(L)\mathrm{\Delta }P{R}_{t-i}+u_{1t}\hfill \\ (-0.3494)(0.9164)(-0.9369)F_{11}=0.7808[0.44594]F_{12}=6.4677[0.0003]\hfill \\ Q_{(12)}=6.3060[0.8999]lnL=-312.9573F_{(7,201)}\text{-}\text{statistic}=3.{3189}^{\ast \ast }\hfill \end{array}$

$\begin{array}{c}\mathrm{\Delta }P{R}_t=0.1052+0.{3804}_t{\widehat{\mathit{\epsilon }}}_{t-1}-0.0467(1-I_t){\widehat{\mathit{\epsilon }}}_{t-1}+A_{21}(L)\mathrm{\Delta }L{R}_{t-i}+A_{22}(L)\mathrm{\Delta }P{R}_{t-i}+u_{2t}\hfill \\ (-1.4845){(4.1277)}^{\ast }(0.6032)F_{21}=30.4878[0.0000]F_{22}=0.2233[0.8001]\hfill \\ Q_{(12)}=3.8950[0.9853]lnL=-320.2305F_{(5,203)}\text{-}\text{statistic}=10.{5865}^{\ast }\hfill \end{array}$

^*Significances at 1% level.

^**Significances at 10% level.

Table

$\begin{array}{c}S{P}_t=4.605419-0.101972{\mathit{\omega }}_t^2+{\mathit{\epsilon }}_t\\ (196.{0087}^{\ast })(-7.{8247}^{\ast })\end{array}$

$$\begin{array}{c}{\mathit{\omega }}_t^2=0.247836+0.859074{\mathit{\epsilon }}_{t-1}^2+827174{\mathit{\epsilon }}_{t-2}^2-0.971880{\mathit{\omega }}_{t-1}^2+0.167535{\mathit{\omega }}_{t-2}^2+0.234490{\mathit{\omega }}_{t-3}^2\hfill \\ (10.{9836}^{\ast })(14.{6937}^{\ast })(15.{8759}^{\ast })(-30.{6617}^{\ast })(6.{6117}^{\ast })(7.{0224}^{\ast })\hfill \end{array}$$

Enders, W., & Siklos, P. (2001). Cointegration and threshold adjustment. Journal of Business & Economic Statistics, 19, 304–311.

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Perron, P. (1997). Further evidence on breaking trend functions in macroeconomic variables. Journal of Econometrics, 80, 355–385.https://doi.org/10.1016/S0304-4076(97)00049-3

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Chan, K. (1993). Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. The Annals of Statistics, 21, 520–533.https://doi.org/10.1214/aos/1176349040

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