The Impact of Supply Chain Disruptions and Global Uncertainty on Inflation Rate in Saudi Arabia

Abstract

Inflation rate is considered undesirable in the modern globalized world due to its adverse and long-lasting impacts. The Kingdom of Saudi Arabia (KSA, hereafter) has also experienced inflationary pressure during the last few years, specifically post-COVID-19. However, the empirical literature on the determinants of inflation is indeed very scarce in the context of KSA. Amid this backdrop, this research paper aims to figure out the true determinants of inflation by focusing on the role of supply chain disruptions and global uncertainty by focusing on KSA. Quantitative data were collected from credible sources on a monthly basis for the period of 1998M01 to 2024M02 and were analyzed through the “Autoregressive Distributed Lag Model (ARDL)”. Our findings indicate that inflation in KSA is positively impacted by supply chain disruptions, global uncertainty, inflation spillovers from the United States, and money supply in the long run. Similarly, in the short run, only money supply, supply chain disruptions, and global uncertainty are responsible for the prevailing inflation rate in KSA. Moreover, the real effective exchange rate is positively and significantly linked with inflation only in the long run. Furthermore, positive shocks in oil prices cure inflation, while negative shocks in oil prices accelerate inflation in the short run. Our results are expected to shape policy formulation regarding the management of the inflation rate in KSA significantly.

1. Introduction

Inflation rates across the globe have reached unprecedented heights and are far beyond their levels prior to the COVID-19 pandemic. In some developed countries, inflation has reached double digits for the first time since the late 1970s. The problem is further exacerbated in developing countries, with inflation reaching three digits in some developing economies. The key challenge of the current inflation is that unlike past inflationary spikes that were often short-lived, the current inflation has been shown to be persistent over time. Attempts have been made by policymakers to slow down the escalations of the spike in current inflation; however, it remains well above pre-pandemic levels.

Several papers have discussed the nature of the current inflation and the key sources behind inflationary pressures. The primary factors driving the increase in inflation are closely tied to the economic recovery following the COVID-19 pandemic as described by De Grauwe (2021). Globalization has created significant competition among the economies in recent years as mentioned by Shahzad et al. (2023). Hence, due to increased globalization, economies are exposed to more external shocks. Both fiscal and monetary policies were implemented for recovering the economies after the full economic lockdowns. However, early warnings were issued cautioning that the generous fiscal subsidies, along with accommodative monetary policies, would likely fuel inflationary pressures (Bordo and Levy 2021; Blanchard 2021; Summers 2021). Gharehgozli and Lee (2022) asserted that the expansionary monetary policy to recover the GDP during the pandemic would lead to a persistent increase in core inflation. The inflation spikes after the pandemic were initially presumed to be only transitory, and the delay in implementing proper policy to curb inflation was one of the contributing factors to its persistence, according to Blanchard and Bernanke (2023).

A key source of the recent inflation is the ongoing impact of supply disruptions caused by COVID-19. Cavallo (2021) shows that the persistence of the inflation surge is dependent on the severity of demand and supply shocks and the extent they impact the different sectors across countries. Cavallo (2024) shows that the ongoing supply chain disruptions are a leading cause of the rise in product shortages, and inflated prices are more frequent where stockouts are persistent. Morana (2023) asserted that the inflationary pressures after COVID-19 were mainly derived from the supply side, and disruptions in the supply chain are causing the persistence of elevated price levels after the pandemic. Fornaro and Wolf (2023) show that a negative supply chain shock may lead to a persistent, or even a permanent drop in GDP below its levels prior to the shock. Their findings reveal that scars of supply shocks depress aggregate demand, which exerts inflationary pressures, contributing to more persistent inflation.

Global uncertainty also plays a pivotal role in shaping inflationary dynamics after the pandemic. The shortages in factors of production such as semiconductor chips (Dunn and Leibovici 2021; Ramani et al. 2022; Ferdous et al. 2023) and reductions in labor participation (Domash and Summers 2022; Baqaee and Farhi 2022) deteriorate confidence in economic conditions and exacerbate market volatility. Global uncertainty is also subject to the rise in geopolitical conflicts, which in turn could drive up production costs and lead to higher consumer prices. The Russian–Ukrainian war was shown to increase global uncertainty and inflate energy prices. Maurya et al. (2023) show that the Russian–Ukrainian conflict was a primary source of the dramatic surge in inflation, and the extent to which it affects trade interactions, along with geographic proximity, has a major influence on raising uncertainty. Their work also demonstrates that geographic proximity plays a direct role in escalating uncertainty, with neighboring economies being more significantly affected compared to more distant countries. Baumeister (2023) highlights the influence of the ongoing conflict on uncertainty levels, demonstrating that oil price uncertainty was a key driver of heightened inflation.

In the Kingdom of Saudi Arabia (KSA, hereafter), inflation is mostly impacted by global factors. Indeed, a growing body of research discussed the impact of spillovers from global economic events on domestic inflations including (Tiwari et al. 2015; Hałka and Szafranek 2016; Istiak et al. 2021; Hall et al. 2023; Pham and Sala 2022; Aharon et al. 2023; Al-Nassar and Albahouth 2023). This study demonstrated that inflation dynamics within a country are not solely influenced by domestic or idiosyncratic factors but also significantly shaped by spillovers from other economies. Moreover, the study by Ramady (2009) concluded that inflation in KSA is influenced both by internal factors such as the supply of money, stock market, and riyal interest rate, as well as external factors such as the exchange rate, oil prices, and US interest rates. Albahouth (2025) also investigated the determinants of inflation in Saudi Arabia, evaluating the impact of external shocks on local prices. The results show that inflation rate variations are influenced by oil price fluctuations and by global shocks that are transmitted through exchange rate volatilities. However, other than the mentioned external factors, global supply chain shocks and global uncertainty may also be responsible for the inflationary pressure in KSA. Exploring the external determinants of inflation is logical as it is a fact that inflation in an open economy like KSA could be explained by external factors as pointed out by Naseem (2018). Thus, investigations on the determinants of inflation should consider the impact of global shocks on shaping inflation volatilities in KSA.

This research paper significantly deviates from the previous literature in exploring the determinants of inflation in the context of the economy of KSA by primarily focusing on the role of external factors. It is a fact that supply chain disruptions and global uncertainty have severe adverse consequences for the global economy including rising inflation. However, the previous empirical literature is largely silent on the exact role of global uncertainty and supply chain disruptions while explaining the inflationary pressure in different economies. Therefore, the scope of this paper is to focus on the impact of supply chain disruptions and global uncertainty on inflation, an area that has not been thoroughly investigated in the case of KSA. The recent literature is indeed very scarce on the determinants of inflation particularly in the KSA. Additionally, this study provides a statistical evaluation on whether local inflation is affected by the observed inflation in major economies, specifically incorporating the impact of US inflation on local consumer prices in KSA. The influence of oil price fluctuations as a major source of global shocks and exchange rate volatility as a transmission channel of external shocks are also estimated in this work. Finally, the role of money supply in contributing to observed inflation in KSA is well documented in the existing literature, and this study reinvestigates this relationship to analyze the extent and magnitude of its influence after the pandemic.

Several insightful conclusions are drawn by this work. First, supply chain disruptions have a significant impact on inflation rate variations in KSA. This shows that disruption in supply chains due to the pandemic is one source of the current observed inflation in KSA. Second, global uncertainty has a significant influence on inflation rate variabilities where higher global uncertainty leads to higher inflation, and these results are consistent both in the short and the long run. Third, estimated models on the impact of global inflation trends on observed inflation show that US inflation movements could explain domestic inflation rate movements in the long run. Fourth, real exchange rate volatilities indicate that imported inflation has a significant impact on local prices in the short run, while oil prices do not have significant impacts on observed inflation. Finally, our results show that implemented monetary policy is the main driver of inflation in KSA, affecting local prices in both the short and the long run.

The rest of this paper is organized as follows: Section 2 provides a literature review on the growing attention to the role of supply chain disruptions and details the background on inflation determinants in KSA. Section 3 highlights the historical behavior of variables, while Section 4 discusses the methodology and the utilized model. In Section 5, we demonstrate and provide detailed discussions on the findings of this paper. We conclude this paper and provide a policy implication in Section 6.

2. Literature Review

Numerous studies have investigated the drivers of inflation surge after the COVID-19 pandemic. A key factor that emerges as a major source of the observed inflation is supply chain disruptions. LaBelle and Santacreu (2022) implemented a cross-industry evaluation and showed that shortages in factors of production due to the full economic lockdowns were the reason behind inflation in the US. Dunn and Leibovici (2021) and (Domash and Summers 2022; Baqaee and Farhi 2022) show that supply-side challenges stemmed from significant reductions in silicon chip inputs and reductions in labor participation rates, respectively, leading to supply chain disruptions. Ha et al. (2021) evaluated the underlying causes behind the unprecedented inflation surge and showed that disruptions in the supply chain were a dominant source of inflation after the pandemic. Ye et al. (2023) also investigated the same question on developed and emerging economies and showed that both supply chain disruption and oil price fluctuations have a significant impact on observed inflation after the pandemic. Diaz et al. (2024) show that since the Global Financial Crisis (GFC), supply shocks have emerged as the primary forces behind inflation, and these inflationary pressures were intensified after the COVID pandemic.

The impact of increasing global uncertainty on inflation has also been thoroughly documented, highlighting its significant influence on price stability across various economies. Wen et al. (2019) analyzed the impact of global uncertainty on inflation in China using proxies for economic policy uncertainty (EPU), financial markets (VIX), and energy markets (OVX). The study revealed that economic policy uncertainty exerts a significant and long-term influence on inflation, while financial market uncertainty appeared to be the most influential determinant of inflation. Additionally, their analysis showed that fluctuations in oil price uncertainty significantly affect the money supply, which in turn plays a key role in driving variations in inflation. Adeosun et al. (2023) also analyzed the dynamic relationship between EPU, geopolitical risks, and inflation in developed economies using continuous wavelet transformation. The findings reveal a strong and time-varying coherence between EPU and inflation, indicating that periods of heightened economic policy uncertainty are closely associated with increases in inflation. Anderl and Caporale (2023) also investigated the effect of EPU and oil price uncertainty (OPU) on inflation and showed that, while both have significant impacts on inflation, the influence of EPU outweighs the effect of OPU on inflation. Their findings also reveal that the influence of EPU is extended, influencing monetary policy uncertainty. Anderl and Caporale (2023) investigated the effects of EPU and OPU on inflation, finding that while both significantly impact inflation, EPU has a stronger influence compared to OPU. Further research reveals that EPU not only affects inflation directly but also extends its impact by contributing to monetary policy uncertainty.

Hemmati et al. (2018) utilized the data of the economy of Iran with the help OLS and VECM. Their empirical findings show that inflation in Iran has responded positively to increases in sanctions, the exchange rate, and the import price index during the study period. Similarly, Asghar et al. (2013) focused on Pakistan’s data and employed ARDL modeling for examining the factors affecting the inflation rate. The outcome of their study indicated that the inflation rate of the Pakistan economy could be explained by external inflation and financial crises. Similarly, the inflation rate, according to their results, is also dependent on money supply as well as the lagged value of inflation in Pakistan. On the other hand, Mohanty and John (2015) concluded that inflation in India is dependent on oil prices as well as the fiscal deficit in recent years.

This study aims to assess the extent to which supply chain disruptions and global uncertainty drive inflation rate fluctuations in KSA over varying time horizons. Specifically, it investigates whether these external factors have a temporary, short-term impact on inflation, or if their influence extends into more sustained, long-term effects. Additionally, this research examines the role of oil price fluctuations and exchange rate volatilities on inflationary trends in KSA. It also investigates the impact of US inflation as a main transmitter of global inflation to evaluate how external price pressures affect domestic inflation. Given the substantial emphasis from prior research on the impact of money supply on inflation, this factor is re-evaluated in this work to provide a more recent analysis of its significance to the economy. By incorporating these factors into the investigation of the primary drivers of inflation in KSA, this study aims to offer valuable policy insights for suggesting effective strategies to control inflation.

3. Trends in Inflation, Supply Chain Disruptions, and Global Uncertainty

In this section, this study aims to highlight the trends in the main variables selected for the study. For this purpose, we show the inflation rate, supply chain disruptions, and global uncertainty in Figure 1, Figure 2 and Figure 3, respectively. Figure 1 shows that the inflation rate remained stable till 2006. From 2007 and onward, KSA witnessed a significant rise in inflation rate. Similarly, during COVID-19, the inflation rate declined. However, since COVID-19, the inflation rate has increased faster in KSA.

Figure 2 shows that supply chain disruptions remained stable over the years till the emergence of COVID-19. The supply chain disruptions reached the highest levels after COVID-19. Recent data show that supply chain disruptions decreased steadily. The reduction in supply chain disruptions observed in recent years is evident as the global economy has made a significant recovery post-COVID-19. Finally, several ups and downs have been seen for the global uncertainty post-2008. Global uncertainty reached its highest level in 2020. However, recent data show that global uncertainty has declined steadily.

4. Modeling and Methodology

4.1. Model Specification

This section is devoted to the specification of the model for assessing the determinants of inflation in KSA. Inflation responds to several factors as evident from prior studies. The independent variables include supply chain disruption, global uncertainty, oil prices, real effective exchange rate, money supply, and the inflation rate of the United States. We propose the following model for the purpose of analysis. The study by Li et al. (2024) is utilized in model building. The logarithmic transformation is used to address the potential non-linearity issue and further simplify the interpretation of results.

$${\mathbf{L}\mathbf{N}\mathbf{C}\mathbf{P}\mathbf{I}}_{\mathbf{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathbf{L}\mathbf{N}\mathbf{O}\mathbf{I}\mathbf{L}}_{\mathbf{t}}+{\mathsf{\beta }}_2{\mathbf{L}\mathbf{N}\mathbf{M}\mathbf{S}}_{\mathbf{t}}+{\mathsf{\beta }}_3{\mathbf{L}\mathbf{N}\mathbf{R}\mathbf{E}\mathbf{E}\mathbf{R}}_{\mathbf{t}}+{\mathsf{\beta }}_4{\mathbf{L}\mathbf{N}\mathbf{U}\mathbf{N}}_{\mathbf{t}}+{\mathsf{\beta }}_5{\mathbf{L}\mathbf{N}\mathbf{C}\mathbf{P}\mathbf{I}\mathbf{U}\mathbf{S}}_{\mathbf{t}}+{\mathsf{\beta }}_6{\mathbf{S}\mathbf{C}}_{\mathbf{t}}+{\mathbf{U}}_{\mathbf{t}}$$

(1)

Model 1 shows that the inflation rate is approximated by the consumer price index. Oil prices are measured in USD/barrel, while money supply is measured in broad money as a percentage of GDP. The “real effective exchange” rate is approximated using “the value of a currency against a weighted average of several foreign currencies”. The uncertainty index is measured by an index, where higher values indicate increased uncertainty and vice versa. The US inflation rate is measured by taking the CPI index. Finally, supply chain disruptions are also measured by an index, where higher values show more disruptions and vice versa. Data on the selected variables were taken monthly for the period 1998M1–2024M2. Detailed information on the variables is shown in the following

Table 1. Variables and sources.

Variables	Construction	Sources
${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}}$	“Consumer price index”	“International Monetary Fund”
${\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}}$	“Global oil prices, USD/Barrel”	“U.S. Energy Information Administration (EIA)”
${\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}}$	“Money supply, broad money as % GDP”	“General Authority of Statistics in Saudi Arabia”
${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}}$	“Real effective exchange rate”	“International Monetary Fund”
${\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}}$	“Global uncertainty index, where higher values indicate higher uncertainty”	World Uncertainty Index (WUI): Global
${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}}$	“US inflation rate, CPI index”	“International Monetary Fund”
${\mathrm{S}\mathrm{C}}_{\mathrm{t}}$	“An index where higher values indicate more disruptions”	Global Supply Chain Pressure Index (GSCPI)

.

4.2. Estimation Techniques

The monthly data gathered for the purpose of analysis are basically time series data. The “Ordinary Least Squares (OLS)” estimator is not an appropriate estimator for the estimation of time series data due to the potential non-stationarity of estimated variables (Tahir 2020; Shah et al. 2022). The presence of the unit root problem, which is commonly associated with time series data, violates one of the assumptions of OLS. To address this problem, several cointegration approaches were developed in the literature. The Engle and Granger (1987) cointegration test available for handling two non-stationary variables received significant attention in the early days. Similarly, the Johansen (1988) multivariate cointegration test is also very effective in handling non-stationary variables. Pesaran et al. (2001) proposed a comprehensive cointegration test “Autoregressive Distributed Lag Model (ARDL)”, which has multiple benefits over other proposed cointegration tests. ARDL modeling simultaneously produces long-run and short-run relationships among the variables under consideration. Islam (2021) discussed the benefits of using the ARDL approach and highlighted that it handles variables of the same as well as diverse integration orders and is useful in the case of small sample sizes. Due to the benefits mentioned, the present study also adopted ARDL modeling for the estimation.

4.3. Preliminary Testing

Unit root testing is carried out using the “Augmented Dickey–Fuller (ADF)” approach. Results presented in

Table 2. Unit root testing (ADF).

“Variables”	“Level”	“F. D”	“Conclusion”
${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}}$	−2.127	−15.419 ***	I(1)
${\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}}$	−2.895	−13.607 ***	I(1)
${\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}}$	−0.232	−18.839 ***	I(1)
${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}}$	−1.756	−13.452 ***	I(1)
${\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}}$	−5.576 ***	−13.788 ***	I(0)
${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}}$	−0.501	−10.729 ***	I(1)
${\mathrm{S}\mathrm{C}}_{\mathrm{t}}$	−3.782 **	−16.169 ***	I(0)

Note: “The asterisks (***) and (**) indicate 1-percent and 5-percent levels of significance”.

show that some of the variables selected for this study are non-stationary at the level. For instance, the inflation rate, oil prices, money supply, real effective exchange rate, and inflation rate of the United States are stationary at first difference but non-stationary at the level. Likewise, the uncertainty index and supply chain disruption index are stationary not only at the level but also at first difference. The integration order of variables is different and hence this different order permits us to use the ARDL modeling approach instead of other testing procedures.

4.4. ARDL Modeling Approach

In ARDL modeling, the first step is to convert Model 1 into the ARDL framework. Following the previous literature, we have converted Equation (1) into the ARDL framework, as shown by Models 2–8.

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_2\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-1}+ \\ {{\mathrm{\varnothing }}_3\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-1}{+{\mathrm{\varnothing }}_4\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_5\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_6\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_7\mathrm{S}\mathrm{C}}_{\mathrm{t}-1}+{\mathrm{U}}_{\mathrm{t}} \end{gathered}$$

(2)

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_2\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-1}+ \\ {{\mathrm{\varnothing }}_3\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-1}{+{\mathrm{\varnothing }}_4\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_5\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_6\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_7\mathrm{S}\mathrm{C}}_{\mathrm{t}-1}+{\mathrm{U}}_{\mathrm{t}} \end{gathered}$$

(3)

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_2\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-1}+ \\ {{\mathrm{\varnothing }}_3\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-1}{+{\mathrm{\varnothing }}_4\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_5\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_6\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_7\mathrm{S}\mathrm{C}}_{\mathrm{t}-1}+{\mathrm{U}}_{\mathrm{t}} \end{gathered}$$

(4)

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_2\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-1}+ \\ {{\mathrm{\varnothing }}_3\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-1}{+{\mathrm{\varnothing }}_4\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_5\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_6\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_7\mathrm{S}\mathrm{C}}_{\mathrm{t}-1}+{\mathrm{U}}_{\mathrm{t}} \end{gathered}$$

(5)

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_2\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-1}+ \\ {{\mathrm{\varnothing }}_3\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-1}{+{\mathrm{\varnothing }}_4\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_5\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_6\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_7\mathrm{S}\mathrm{C}}_{\mathrm{t}-1}+{\mathrm{U}}_{\mathrm{t}} \end{gathered}$$

(6)

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_2\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-1}+ \\ {{\mathrm{\varnothing }}_3\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-1}{+{\mathrm{\varnothing }}_4\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_5\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_6\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_7\mathrm{S}\mathrm{C}}_{\mathrm{t}-1}+{\mathrm{U}}_{\mathrm{t}} \end{gathered}$$

(7)

$$\begin{gathered} {∆\mathrm{S}\mathrm{C}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_2\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-1}+ \\ {{\mathrm{\varnothing }}_3\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-1}{+{\mathrm{\varnothing }}_4\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_5\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_6\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-1}+{{\mathrm{\varnothing }}_7\mathrm{S}\mathrm{C}}_{\mathrm{t}-1}+{\mathrm{U}}_{\mathrm{t}} \end{gathered}$$

(8)

Equations (2)–(8) represent the ARDL framework of Equation (1). The parameters (${\mathsf{\beta }}_0-{\mathsf{\beta }}_7)$, which are adjacent to the operator ($∆$), measure the short-run relationships. Likewise, the other parameters (${\mathrm{\varnothing }}_1-{\mathrm{\varnothing }}_7$) indicate the long-run relationships. In the ARDL framework, the absence or presence of cointegration is straightforward. The presence or absence of cointegration can be tested with the help of null and alternative hypotheses. The null hypothesis generally assumes that variables are not cointegrated in the long run “(${\mathrm{\varnothing }}_1={\mathrm{\varnothing }}_2={\mathrm{\varnothing }}_3={\mathrm{\varnothing }}_4={\mathrm{\varnothing }}_5={\mathrm{\varnothing }}_6={\mathrm{\varnothing }}_7=0$)”, while the alternative hypothesis “(${\mathrm{\varnothing }}_1\ne {\mathrm{\varnothing }}_2\ne {\mathrm{\varnothing }}_3\ne {\mathrm{\varnothing }}_4\ne {\mathrm{\varnothing }}_5\ne {\mathrm{\varnothing }}_6\ne {\mathrm{\varnothing }}_7\ne 0$)” supports the presence of cointegration. Hypothesis testing is based on the F-test. The F-test value will be matched with the values proposed by Narayan (2004). A higher F-test value as compared to the upper bound will be the reflection of a cointegrating relationship, while a lower F-test value as compared to the lower bound will be an indication of no cointegration relationship. An F-test value lower than the upper bound and greater than the lower bound would be an indication of an inconclusive cointegrating relationship.

After the confirmation of cointegration analysis, the second step is to develop a “restricted error correction model (ECM)”. ECM modeling is important in the ARDL framework as it helps the researchers to figure out the speed of convergence. Similarly, ECM modeling is also useful in figuring out the short-run impacts that independent variables have on the dependent variable. We have developed our ECM based on Models 2–8 as shown below.

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{E}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{\mathrm{t}} \end{gathered}$$

(9)

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{E}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{\mathrm{t}} \end{gathered}$$

(10)

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{E}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{\mathrm{t}} \end{gathered}$$

(11)

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{E}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{\mathrm{t}} \end{gathered}$$

(12)

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{E}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{\mathrm{t}} \end{gathered}$$

(13)

$$\begin{gathered} {∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{E}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{\mathrm{t}} \end{gathered}$$

(14)

$$\begin{gathered} {∆\mathrm{S}\mathrm{C}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}1}{\mathsf{\beta }}_{1\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}2}{\mathsf{\beta }}_{2\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}3}{\mathsf{\beta }}_{3\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}4}{\mathsf{\beta }}_{4\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}5}{\mathsf{\beta }}_{5\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}6}{\mathsf{\beta }}_{6\mathrm{i}}{∆\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}7}{\mathsf{\beta }}_{7\mathrm{i}}{∆\mathrm{S}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{{\mathrm{\varnothing }}_1\mathrm{E}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{\mathrm{t}} \end{gathered}$$

(15)

5. Results and Discussion

5.1. Descriptive Analysis

A descriptive analysis of the variables selected for the current study is shown in

Table 3. Descriptive statistics.

	$\mathbf{C}\mathbf{P}\mathbf{I}$	${\mathbf{O}\mathbf{I}\mathbf{L}}_{\mathbf{t}}$	${\mathbf{M}\mathbf{S}}_{\mathbf{t}}$	${\mathbf{R}\mathbf{E}\mathbf{E}\mathbf{R}}_{\mathbf{t}}$	${\mathbf{U}\mathbf{N}}_{\mathbf{t}}$	${\mathbf{C}\mathbf{P}\mathbf{I}\mathbf{U}\mathbf{S}}_{\mathbf{t}}$	${\mathbf{S}\mathbf{C}}_{\mathbf{t}}$
Mean	100.515	59.660	1,209,410	110.730	143.159	222.251	0.014
Maximum	133.798	133.880	2,756,193	133.642	431.642	311.054	4.374
Minimum	75.624	11.350	271,487.0	90.396	49.228	162.000	−1.558
Std. Dev.	20.260	27.569	740,677.4	10.500	75.341	37.818	1.009
Obs.	314	314	314	314	314	314	314

Note: “Authors’ own calculations using data from several sources”.

. According to reported statistics, the average value of CPI, which is used as a measure of inflation, is 100.515, with a standard deviation of 10.260. The highest (133.798) and lowest (75.624) values of CPI were observed in February 2024 and November 2001, respectively. Similarly, the mean value of oil is 59.660, while its standard deviation is 27.569. The maximum value of oil prices (133.880) was recorded in July 2008, while the minimum value (11.350) was witnessed in December 1998. The real effective exchange rate, on the other hand, has an average value of 110.730. The effective exchange rate reached its highest point (133.642) in January 1998, while its lowest value (90.396) was observed in March 2008.

Further statistics show that the average value of money supply is 1,209,410 for the study period. The money supply reached its highest value (2,756,193) in February 2024 and its lowest value (271,487) in August 1998. The standard deviation of money supply was recorded as 740,677.40. Furthermore, the global uncertainty index takes an average value of 143.159, with a maximum value of 431.642 and minimum value of 49.228. The maximum and minimum values of the global uncertainty index were recorded in May 2020 and July 2007, respectively. Moreover, the United States inflation rate has an average value of 222.251, with a standard deviation of 37.818. The US inflation rate, in terms of CPI, reached 311.054 in February 2024, which is the highest. Similarly, the lowest inflation rate (162) in terms of CPI was achieved in January 1998. Finally, the average value of the supply chain disruption index is 0.014, with a standard deviation of 1.009. The highest value of the supply chain disruption index (4.373) was observed in December 2021, while its lowest value was recorded in May 2023.

5.2. Cointegration Results

Results of ARDL testing are shown in the following

Table 4. ARDL findings.

“Dependent Variables”	“Bound Test”	“Cointegration?”
${LNCPI}_t$/${LNOIL}_t$, ${LNMS}_t$, ${LNREER}_t$, ${LNUN}_t,{LNCPIUS}_t$, ${SC}_t$	9.316	“H1 Accepted”
${LNOIL}_t$/${LNCPI}_t$, ${LNMS}_t$, ${LNREER}_t$, ${LNUN}_t,{LNCPIUS}_t$, ${SC}_t$	6.555	“H1 Accepted”
${LNMS}_t$/${LNOIL}_t$, ${LNCPI}_t$, ${LNREER}_t$, ${LNUN}_t,{LNCPIUS}_t$, ${SC}_t$	14.345	“H1 Accepted”
${LNREER}_t$/${LNOIL}_t$, ${LNMS}_{t}LNCPI$, ${LNUN}_t,{LNCPIUS}_t$, ${SC}_t$	4.581	“H1 Accepted”
${LNUN}_t$/${LNOIL}_t$, ${LNMS}_{t}LNCPI$, ${LNREER}_t,{LNCPIUS}_t$, ${SC}_t$	5.189	“H1 Accepted”
${LNCPIUS}_t$/${LNOIL}_t$, ${LNMS}_{t}LNCPI$, ${LNREER}_t,{LNUN}_t$, ${SC}_t$	16.315	“H1 Accepted”
${SC}_t$/${LNOIL}_t$, ${LNMS}_{t}LNCPI$, ${LNREER}_t,{LNUN}_t$, ${LNCPIUS}_t$	4.272	“H1 Accepted”
Critical Level	“I (0)”	“I (1)”
1%	350	4.63
5%	2.81	3.76
10%	2.49	3.38

. For lag selection, we used the automatic Schwartz criteria. The SC criteria suggest three lags for estimation. The findings show that all our proposed models are cointegrated. The F-test values of the estimated models are higher as compared to the critical values shown in the lower portion of Table 4. Hence, we conclude that the variables chosen for our study are cointegrated, hence having a long-run relationship.

5.3. Long-Run Results

After the confirmation of the cointegrating relationship, the next step is to present the long-term results. We show the long-term findings in

Table 5. Long-run findings.

“Regressors”	“Coefficient Value”	“Standard Errors”
${\mathrm{S}\mathrm{C}}_{\mathrm{t}}$	0.038 ***	0.011
${\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}}$	0.562 ***	0.068
${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}}$	0.046	0.184
${\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}}$	0.075 ***	0.024
${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}}$	0.004 ***	0.001
${\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}}$	−0.031	0.037
@TREND	−0.004	0.0009

Note: “The asterisks (***) measure significance at the 1% level”.

. Supply chain disruptions have a significant impact on the inflation rate in KSA. Previous studies have also shown that global supply shocks are primarily responsible for increased inflation rates (Diaz et al. 2023; Laumer 2023). Global supply chain disruptions in the modern globalized world create uncertainty and reduce supply in the market, leading to exponential price increases. Our results regarding supply chain disruptions could also be considered by other oil-exporting economies for policy formulation. Amid increased supply chain disruptions, KSA, in particular, and other oil-exporting economies, such as other members states of GCC and Russia, in general, could increase their safety-level stocks and further diversify their import base in order to minimize the impacts of supply disruptions. These steps would help the authorities to minimize the adverse effects of supply chain disruptions on the inflation rate.

Besides supply chain disruptions, we found evidence about the positive impact of money supply on inflation. This result is consistent with the monetarists’ theory. Doan Van (2020) empirically demonstrated that money supply is responsible for increased inflation rates. The estimate indicates that the influence of money supply is higher as compared to other factors considered in the study. Therefore, a rational policy would be to have strict control over the money supply as it creates inflation, and the inflation rate is responsible for so many problems including socioeconomic issues. It is a fact that global uncertainty cannot be controlled by individual economies including KSA. However, the adverse consequences of global uncertainty could be minimized by effective policy-making. Amid uncertainty, policymakers must aggressively take action by diversifying the import base with effective forecasting using advanced technologies to minimize inflationary pressures.

The results further indicated that global uncertainty has also cast an upward pressure on the rate of inflation in KSA. Uncertainty in the global economy creates panic among the producer and ultimately consumers, leading to price increases in domestic markets. As the global economy has become more globalized in recent times, positive policy shock impacts all economies through multiple channels. Moreover, the results also uncovered the potential influence of United States inflation rate on the inflation of KSA. It is found that the inflation rate of the United States has also accelerated the inflation rate in KSA. The United States, being the world’s largest economy, has a direct as well as indirect impact on all economies including KSA.

Moreover, the inflation rate in KSA is independent of the variation in oil prices. According to the results, the coefficient of oil prices is negative but statistically insignificant. A possible reason, among others, could be that KSA is basically an oil-exporting economy, and hence, the variation in oil prices has a limited or no impact on the general price level. Furthermore, KSA has launched several subsidized programs, including low oil prices for the residents. Similarly, the income level in KSA is relatively higher. Hence, higher income of the people coupled with reduced oil prices could explain why the inflation rate in KSA is independent of the oil prices. Lastly, we found evidence that the “real effective exchange rate” is unable to explain the variation in inflation. It possesses a positive coefficient in the estimated model; however, statistically, this relationship is insignificant.

In summary, the inflation rate in KSA could be explained largely by external factors, as KSA is closely connected with the global economy. Global uncertainty, supply chain disruptions, and inflation spillovers from the United States are the major factors behind the rising inflation level in KSA. Among the internal factors, only money supply was found to cause inflation. Finally, the real effective exchange rate and oil prices are unable to explain the inflation rate in KSA.

5.4. Short-Run Results and Speed of Adjustment

The short-run relationships among the variables are presented in

Table 6. Short-run results.

Variables	Coefficients	Standard Errors
$∆{\mathrm{S}\mathrm{C}}_{\mathrm{t}}$	0.001 **	0.0007
${∆\mathrm{L}\mathrm{N}\mathrm{M}\mathrm{S}}_{\mathrm{t}}$	0.23 ***	0.005
${∆\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{E}\mathrm{R}}_{\mathrm{t}}$	0.100 ***	0.023
$∆{\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}}_{\mathrm{t}}$	0.003 ***	0.001
$∆{\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{P}\mathrm{I}\mathrm{U}\mathrm{S}}_{\mathrm{t}}$	−1.75 × 105-	0.0005
$∆{\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}}$(Positive)	−0.002	0.005
$∆{\mathrm{L}\mathrm{N}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{t}}$(Negative)	0.006	0.005
ECT (-1)	−0.038 ***	0.004

Note: “The asterisks (***) and (**) indicate 1-percent and 5-percent levels of significance”.

. According to the results, money supply, supply chain disruptions, and global uncertainty are the major driving forces behind the inflation rate. These results are supported by the long-run findings. Similarly, in the short run, we found that “effective exchange rate” also explains the variation in the inflation rate. Moreover, the inflation rate in the United States is unable to explain inflation rate. It means that the spillover effects take time to impact other economies.

Furthermore, positive shocks in oil prices reduce the inflation rate in KSA primarily due to the inward revenues. Likewise, negative shocks in oil prices increase inflationary pressures in KSA. However, these impacts are statistically significant, as KSA is an oil-exporting economy. It is a fact the oil price shocks affect importing and exporting economies differently.

5.5. Testing Diagnostics

In this section, this study conducts several tests to assess validity. All the tests provided in

Table 7. Validity testing.

“Test”	“Statistic and Probability”	“Decision”
“LM Test”	0.032 (0.968)	“No serial correlation”
“White Test”	0.001 (0.991)	“No heteroscedasticity”
“Ramsey Test”	2.246 (0.107)	“Correct functional form”

indicate that the estimated models are valid as they are free from econometric issues. The estimated model possesses the correct functional form and is further free from the problem of heteroscedasticity and serial correlation, as shown in Table 6.

5.6. Stability Testing

Furthermore, the residuals are stable, as confirmed by the results of the CUSUM test illustrated by Figure 4. The CUSUM test is extensively employed by researchers in the literature to assess the stability of the disturbance term. According to the outcome of the CUSUM test, the residuals are stable during the study period, as indicated by the blue line being well within the critical limits.

6. Concluding Remarks, Implications, and Limitations

6.1. Concluding Remarks

This research paper has focused on KSA to assess the determinants of the inflation rate using monthly data spanning from 1998M1 to 2024M2. This paper utilized ARDL modeling for exploring the long- and short-run determinants of the inflation rate.

Our estimated model shows that the inflation rate in KSA could be explained largely by external factors instead of internal factors. Supply chain disruptions, global uncertainty, and inflation spillovers from the United States are the major contributors to inflation in KSA. Among the internal factors, only money supply has accelerated the inflation rate in KSA. The short-run results have also proved that the inflation rate in KSA responds positively to global uncertainty, supply chain disruptions, and money supply. Moreover, oil prices and the effective exchange rate appeared to be irrelevant in explaining the long-run inflation in KSA. Finally, the short-run analysis further showed that the exchange rate impacts inflation, while positive oil price shock cures inflation and negative oil price shock increases inflation.

6.2. Implications

This research article has several implications for KSA. Firstly, this study suggests that supply chain disruptions, global uncertainty, and spillovers from the US worsen the inflationary pressure in KSA. Amid these factors, the policymakers of KSA need to take significant measures. For instance, KSA authorities are advised to diversify the supply base and further ensure the safety of stock levels to curb inflationary pressure amid shortages. In addition, efficient forecasting with advanced technologies and modeling of global supply shocks and uncertainly would help the KSA economy to avoid inflationary pressures. Finally, the proper development of contingency plans for dealing with uncertainty and disruptions would help policymakers a great deal in ensuring price stability.

6.3. Limitations of the Study

Several limitations are associated with this study. Firstly, this study is only focused on KSA, while it is a fact that inflation is a global phenomenon. Hence, focusing on a single economy may not provide universal results. Therefore, future studies are suggested to focus on more resource-rich economies to figure out the true impact of global factors on inflation. Secondly, the results of the current study could not be generalized to a greater extent due to the specific structure of KSA. Thirdly, the current study is only limited to a few factors of inflation; however in the real world, inflation can be impacted by several other factors. Therefore, future studies should consider all potential factors that influence inflation.