Empirical analysis of economic performance and metal prices in Peru using svar and okun's law

Abstract

Observing the growth and improvements in the Peruvian labor market over the past 27 years, this study aims to test the validity of Okun's Law hypothesis for Peru during the period 1992–2019. To achieve this, a novel methodology is proposed, which involves calculating the production gap and unemployment gap. Firstly, a proxy variable for the time-varying natural rate of unemployment and a measure of potential output are calculated, both following the methodological approach suggested by the literature. Subsequently, a structural vector autoregressive model (S-VAR) is estimated to capture the lagged effects of Okun's theoretical relationship. The main findings of the study suggest that shocks in the precious metal price index impact both the production gap and the unemployment gap, confirming that the international dynamics of precious metal prices play a crucial role in economic performance when analyzed from the perspective of Okun's Law. This article demonstrates, in the context of a Latin American country, that Okun's law can be fulfilled by considering a fundamental analysis of the determinants of economic growth.

Appendices

Appendix A

Table 3

Table 3 Lag inclusion test

Appendix B

Structural VAR Estimates
Sample (adjusted): 1997 2019
Included observations: 23 after adjustments
Estimation method: Maximum likelihood via Newton–Raphson (analytic derivatives)
Convergence achieved after 14 iterations
Structural VAR is just-identified
Model: e = Phi*Fu where E[uu'] = I
F =
C(1)	0	0
C(2)	C(4)	0
C(3)	C(5)	C(6)
	Coefficient	Std. Error	z-Statistic	Prob
C(1)	0.14848	0.02189	6.78232	0.000
C(2)	0.01065	0.00345	3.08244	0.002
C(3)	-0.21066	0.03837	-5.48953	0.000
C(4)	0.01476	0.00217	6.78232	0.000
C(5)	-0.07639	0.01952	-3.91361	0.000
C(6)	0.07645	0.01127	6.78232	0.000
Log likelihood	92.94310
Estimated S matrix:
0.08885	0.07269	-0.02966
-0.00618	0.01644	-0.00051
0.06036	-0.01321	0.11768
Estimated F matrix:
0.14848	0.00000	0.00000
0.01065	0.01476	0.00000
-0.21066	-0.07639	0.07645

1. Source: Own elaboration based on data from CEPAL and Penn World Table version 9.1 and price index retrieved from IMF Primary Commodity Price Index data base for the Precious Metals Price Index, 2016 = 100, which includes price indexes for gold, silver, palladium, and platinum

Appendix C

Table 4

Table 4 Serial autocorrelation test of VAR residuals, LM-Test

Appendix D

Table 5

Table 5 Variance decomposition

Appendix E

Fig. 4

Fig. 4

Source: Own elaboration based on SVAR estimation – Cholesky ordering Metal price index growth- GDP gap – Unemployment gap

Cumulative FIR to one S.D structural innovations.

Appendix G

Table 6

Table 6 Test de Causalidad de Granger

Appendix H

Table 7

Table 7 Cointegration test

Appendix I

Fig. 5

Source: Own elaboration based on SVAR estimation

Historical Decomposition of Variance (HDV).