Financial development–economic growth nexus in Pakistan: new evidence from the Markov switching model

Figures & data

Table 1. Descriptive statistics and correlation analysis

Panel A	$\mathbf{\Delta }LRGDP$	$\mathbf{\Delta }FD$	$\mathbf{\Delta }LK$	$\mathbf{\Delta }LLF$	$\mathbf{\Delta }LTRADE$	$\mathbf{\Delta }LEXP$	$INF$
Mean	0.0474	0.0029	−0.0053	0.0243	−0.0095	0.0031	0.0769
Maximum	0.0858	0.0737	0.1534	0.0917	0.1514	0.2855	0.1847
Minimum	0.0036	−0.0748	−0.1262	−0.0376	−0.1389	−0.1812	0.0250
Std. Dev	0.0184	0.0290	0.0610	0.0214	0.0700	0.0855	0.0355
Skewness	0.0125	0.1929	0.3517	0.3061	0.0852	0.7416	0.6683
Kurtosis	2.8445	4.6616	3.4021	5.6775	2.2470	4.7078	3.4714
Jarque–Bera	0.0382	4.4857	1.0119	11.6299^	0.9189	7.8884*	3.0964
Panel B:
$\mathrm{\Delta }LRGDP$	1.0000
$\mathrm{\Delta }FD$	0.1974	1.0000
ΔL$K$	0.4338	0.2393	1.0000
$\mathrm{\Delta }LLF$	−0.0667	−0.0113	−0.1157	1.0000
$\mathrm{\Delta }LTRADE$	0.2426	−0.1222	0.2247	0.1123	1.0000
$\mathrm{\Delta }LEXP$	0.0829	−0.0303	−0.0125	0.4102	0.1720	1.0000
INF	−0.1221	−0.3812	−0.2636	0.0422	0.1305	−0.0580	1.0000

Note: ^ and * indicate significance at 1% and 5% levels.

Table 2. Results of BDS test

Dimension	BDS statistic	Z-statistic	p-value
2	0.0482	4.8787	0.000***
4	0.0562	2.8909	0.004***
6	0.0659	3.244	0.001***

Note: ***indicates significance at the 1% level of significance. P-values obtained based on bootstrap with 100,000 replications. The null hypothesis is that the series are linearly dependent.

Table 3. Results of unit root tests

Variable	Specification	ADF test	PP-test
$LRGDP$	$C$	−1.7622	−2.4532
$\mathrm{\Delta }LRGDP$	$C$	−4.0763***	−4.0354**
$FD$	$C$	−1.7381	−1.5273
$\mathrm{\Delta }FD$	$C$	−4.0344***	−4.0344***
$LK$	$C$	−1.7431	−1.8532
$\mathrm{\Delta }LK$	$C$	−5.4106***	−5.3917***
$LF$	$C$	−0.0819	−0.0809
$\mathrm{\Delta }LF$	$C$	−5.8593***	−5.8593***
$LTRADE$	$C$	−1.5058	−1.5700
$\mathrm{\Delta }LTRADE$	$C$	−7.5198***	−7.5111***
$LEXP$	$C$	−1.4732	−1.6303
$\mathrm{\Delta }LEXP$	$C$	−5.2374***	−5.2089***
$LCPI$	$C$	−0.0291	−0.1404
$INF$	$C$	−4.0449***	−2.8703*

Note: $\mathrm{\Delta }$ is the first difference in the variable. ***, ** and *, respectively, stand for significance at 1%, 5% and 10% level of significance.

Table 4. Residual analysis of linear and $\mathrm{M}\mathrm{S}$ models

	Linear model	MS (2) ^^
Panel A: Descriptive analysis
Mean	0.0000	−1.2164e-009
Std. Dev	1.0000	0.98889
Skewness	1.4055	0.37376
Excess Kurtosis	−0.37951	−0.70492
Panel B: Diagnostic statistics
Log-Likelihood	100.7597	111.7054
LR Linearity test	–	21.891 [0.0005]**
Approximate upper bound	–	0.0004**
ARCH 1–1 test	0.59776 [0.4462]	0.39225 [0.5376]
Q(12)	4.1876 [0.6513]	6.9775[0.3229]
Q^2(12)	9.1334 [0.1662]	2.3258 [0.8874]
Jarque–Bera	0.34387 [0.8420]	1.6275 [0.4432]
RCM	–	1.7887
Panel C: Information Criterion
SC	−4.6657	−4.7694
HQ	−4.8912	−5.1359
AIC	−5.0140	−5.3354

Note: **show significance at 5% level . The values in [.] are the p-values. ^^ Model selected on the basis of information criterion and $RCM$

Table 5. Estimates of linear and $MS$ model (1980–2017)

Variable	$\mathrm{\Delta }\mathit{L}RGDP$
	Linear model	$\mathit{M}S$ (2)
Mean $({\mu }_0)$	0.049***	0.086***
	(6.64)	(42.00)
Mean $({\mu }_1$)		0.049***
		(17.8)
Variance $({\sigma }_0$)	0.0159***	0.0014***
Variance $({\sigma }_1$)		0.0113***
$\mathrm{\Delta }F{D}_0$	0.0885	0.6489***
	(0.781)	(12.2)
$\mathrm{\Delta }F{D}_1$	–	0.1727***
		(2.77)
$\mathrm{\Delta }LK$ (${\theta }_1$)	0.1078**	0.0057
	(2.55)	(0.416)
Δ$LLF$(${\theta }_2$)	−0.0716	−0.4564***
	(−0.619)	(−12.3)
Δ$LTRADE$ (${\theta }_3$)	0.0449	0.0676***
	(1.04)	(5.85)
$\mathrm{\Delta }LEXP$ (${\theta }_4$)	0.0209	0.0410***
	(0.606)	(4.15)
$LCPI$ (${\theta }_5$)	0.0062	0.0315
	(0.082)	(1.15)
Probabilities Matrix	–	$\left(\begin{array}{cc}0.2507& 0.1975\\ 0.7493& 0.8025\end{array}\right)$

Note: *** and ** respectively shows 1% and 5% level of significance. The values in (.) are robust t-statistics. 0 and 1 in subscript shows regime 0 (high growth regime) and regime 1 (low growth regime), respectively.

Table 6. Duration of regime classification of MS(2)

	High economic growth	Low economic growth
Years	8	29
In % age terms	21.62%	78.38%
Average duration	1.33 years	4.14 years
Steady-state probabilities	20.90%	79.10%

Figure 1. Smoothed probabilities of $\mathit{M}S\left(2\right)$

Table A: A Literature review on finance-growth nexus in Pakistan

Researcher	Study Period	Major Findings
Khan et al. (2005)	1971-2004	The study concluded a positive long-run impact of financial depth and the real interest rate on economic growth in Pakistan. The share of investment exerted positive and insignificant impact on real income in the long-run. However, in the short run, changes in share of investment have significant positive impact on economic growth in Pakistan.
Tahir (2008)	1973-2006	The study found no causality between financial development and economic growth when interest rate was considered as a measure of financial development. However, evidence of causality relationship was found when the price level was included in the specification.
Khan (2008)	1961-2005	The financial development and share of investment has significant positive association in the short-run and in the long-run.
Jalil and Ma (2008)	1960-2005	Financial development spurs economic growth.
Lal et al. (2009)	1975-2008	The financial structure spurs economic growth.
Jalil and Feridun (2011)	1975-2008	Positive relationship between financial development and economic growth was found. They concluded that finance spurs growth in Pakistan
Rehman et al. (2011)	1973-2008	Financial reforms agumented economic growth.
Farooq et al. (2013)	1987-2009	The results confirmed the role of supply leading hypothesis in finance-growth nexus.
Mahmood (2013)	1979-2008	Significant positive association between financial development and economic growth was found in the short and long-run.
Naveed and Mahmood (2019)	1972-2010	A positive link was found between financial sector reforms and economic growth. In the short-run, the study found a negative nexus between reforms and income growth.