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The paper aims to shed light on the effects of inflation on gold price and exchange rate in Vietnam by using time-varying cointegration.

Using cointegration techniques with fixed coefficient and time-varying coefficient, the study exams the impacts of inflation in models and compares the results through coefficient estimates.

A significant inflation impacts are found with the time-varying cointegration but not with the fixed coefficient cointegration models. Moreover, monetary policy affects exchange rate not only directly via its instruments as money supply and interest rate but indirectly via inflation. Also, interest rate is one of the determinants of gold price.

To the best of our knowledge, this paper is the first to use time-varying cointegration to analyze the impact of inflation on the gold price and exchange rate in Vietnam. Gold price and exchange rate fluctuations are always the essential and striking issues, which have been emphasized by economists and policymakers. In macroeconometric researches, cointegration models are often used to analyze the long-term relations between variables. Attentionally, applied models show a limitation when estimating coefficients are fixed. This characteristic might not really match with the data properties and the variation of the economy. Currently, time-varying cointegration models are emerging method to solve the above issue.

This study reexamines the sustainability of fiscal policy in Sweden.

To test the sustainability of fiscal policy, two approaches are used; the methodology of Kejriwal and Perron (2010), testing for multiple structural changes in a cointegrated regression model and time-varying cointegration test of Bierens and Martins (2010), and Martins (2015).

Using the first approach of testing for multiple structural changes in a cointegrated regression model, the results indicate that government spending and revenue are cointegrated with two breaks. An estimation of a two-break long-run model shows that the slope coefficient increases from 0.678 to 0.892 from the first to the second regime, implying that fiscal deficits were weakly sustainable in the first two regimes, from 1800 to 1943, and from 1944 to 1974. Further, results from time-varying cointegration test indicate that cointegration between spending and revenue in Sweden is time-varying. Fiscal deficits were found to be unsustainable for the periods 1801–1811, 1831–1838, 1853–1860 , 1872–1882, 1897–1902, 1929–1940 and 1976–1982 and weakly sustainable over the rest of the study period.

A number of implications arise from this study: (1) Accounting for breaks in cointegration analysis and in the estimation of the level relationship between spending and revenue is very important because ignoring breaks may lead to an overestimated slope coefficient and hence a bias on the magnitude of fiscal deficit sustainability. (2) In testing for cointegration between spending and revenue, assuming a constant cointegrating slope when it is actually time-varying can also be misleading because deficits can be sustainable for a period of time and unsustainable over another period.

The contribution of this study is three-fold; first, the study uses a long series of annual data spanning over a period of two centuries, from 1800 to 2011. Second, because of the importance of structural change in economics, to examine the existence of a level relationship between spending and revenue, the study uses the methodology of Kejriwal and Perron (2010) to test for multiple structural changes in a cointegrated regression model, as well as time-varying cointegration of Bierens and Martins (2010) and Martins (2015).

This study aims to measure the relationship between demand and exchange rate shocks in the tourism industry.

A panel data set is constructed covering the period between 1995 and 2017, and the data set includes the top 26 countries that host 10 million tourists and above in the world as of 2017. The standard errors of the series are used as an indicator of shocks. First, the cross-sectional dependency, stationarity and the homogeneity of the series are examined; second, a panel cointegration analysis is implemented; third, long-term panel cointegration coefficients are analyzed with Dynamic Common Correlated Effects (DCCE) approach; and, finally, Dumitrescu and Hurlin’s (2012) Granger non-causality test is used to detect the causality.

The preliminary analyses show that the variables are cross-sectional dependent and heterogeneous and are stationary in their first difference; hence, the effects of the shocks are temporary. On the other hand, as a result of the panel cointegration analysis, it is found that both series are cointegrated over the long-term. However, the long-term coefficients estimated with the DCCE approach are found not to be statistically significant. Finally, as a result of the Dumitrescu and Hurlin’s (2012) Granger non-causality test, it is concluded that there is a causality running from exchange rate shocks to demand shocks.

To the best of the authors’ knowledge, the cointegration between the tourism demand shocks and exchange rates shocks has not been investigated before, and therefore, this study is considered to be a pioneering study that will contribute to the literature.

Brazil uses the dollar as a vehicle currency to invoice its exports. This fact produces a tendency toward equalizing the prices of products in dollars in the international market and reducing the ability of firms to practice pricing-to-market (PTM). This study aims to evaluate the hypothesis by estimating error correction models in panel data, obtaining estimates of PTM for 25 manufacturing products exported by Brazil between 2010 and 2020.

This study uses the correlated common effect estimator proposed by Pesaran (2006) and Chudik and Pesaran (2015b) to estimate the PTM coefficients.

Results of this study indicate that exporters practice local-currency pricing stability for dollar prices. This study obtains that Brazilian exporters tend to stabilize their dollar price for exports, reducing heterogeneity between destination markets. The results are in agreement with the hypothesis of the prevalence of the coalescing effect of Goldberg and Tille (2008) and lower sensitivity of the markup adjustment to the specific market, as pointed out by Corsetti et al. (2018). The pricing of Brazilian exports in dollars reflects a profit maximization strategy that considers an international price system based on global demand for products.

In addition to analyzing the dollar role in the pricing of Brazilian exports through the triangular decomposition, this study also shows the importance of examining the cross-section dependence of errors, considering the heterogeneous cointegration in export pricing models and producing PTM estimates for short-term and long-term.

In this paper we examine the validity of the J-curve hypothesis in four Southeast Asian economies (Indonesia, Malaysia, the Philippines and Thailand) over the 1980–2017 period.

We employ the linear autoregressive distributed lags (ARDL) model that captures the dynamic relationships between the variables and additionally use the nonlinear ARDL model that considers the asymmetric effects of the real exchange rate changes.

The estimated models were diagnostically sound, and the variables were found to be cointegrated. However, with the exception of Malaysia, the short- and long-run relationships did not attest to the presence of the J-curve effect. The trade flows were affected asymmetrically in Malaysia and the Philippines, suggesting the appropriateness of nonlinear ARDL in these countries.

The previous research tended to examine the effects of the real exchange rate changes on the agricultural trade balance and specifically the J-curve effect (deterioration of the trade balance followed by its improvement) in the developed economies and rarely in the developing ones. In this paper, we address this omission.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

This paper aims to assess the impact of digital financial innovation on financial system development in Common Market for eastern and Southern Africa (COMESA). This paper evaluates the dynamic relationship between digital financial innovation measures and financial system development using time series data from COMESA countries for the period 1997–2019.

A dynamic autoregressive distributed lag model (ARDL) was adopted and the mean group (MG), pooled mean group (PMG) and dynamic fixed effect (DFE) of the model were estimated to evaluate the short- and long-run impact. In addition, the dynamic generalized method of moments (DGMM) was adopted for a robustness check. The Hausman test results show PMG to be the most consistent and efficient estimator, while the coefficient of lagged dependent variable of different GMM is less than the fixed effect coefficient, and, as such, suggests system GMM is the most suitable estimator. Data for the study were sourced from World Bank Development Indicator (WDI, 2020), World Governance Indicator (WGI, 2020) and World Bank Global Financial Development Database (GFD, 2020).

The result shows that digital financial innovation significantly impacts financial system development in the long run. As such, the evidence revealed that automated teller machines (ATMs), point of sale (POS), mobile payments (MP) and mobile banking are significant and contribute positively to financial system development in the long run, while mobile money (MM) and Internet banking (INB) are insignificant but exhibit positive and inverse relationship with financial development respectively. Further investigation revealed that institutional quality and a stable macroeconomic environment including their interactive term are significantly imperative in predicting financial system development in the COMESA region.

Researchers recommend a cohesive and conscious policy that would checkmate the divergence in the short run and suggest a common regional innovative financial strategy that could be pursued to incentivize technology transfer needed to promote financial system development in the long run. More so, plausible product and process innovations may be adapted to complement innovative institutions in the different components of the COMESA financial system.

Digital financial innovation services if well managed increase the inherent benefits in financial system development.

To the best of the authors’ knowledge, this paper presents new background information on digital financial innovation that may stimulate the development of the financial system, particularly in the COMESA region. It also exposes the relevance of digital financial innovation, institutional quality and stable macroeconomic environment as well as their interactive effect on COMESA financial system development.

Malaysia is one of the fastest-growing Asian economies with a properly designed and developed Islamic financial system. This unique feature of the Malaysian economy made it an important case study, and the purpose of this study is to assess for the dynamic contribution of Islamic finance to the growth of the real economy.

The study uses a quarterly data set of 20 years analysed via the autoregressive distributive lag bounds test approach to cointegration.

The results in the short-run show a non-significant relationship between Islamic banking indices and the real economy. However, in the long-run, financing and deposits of Islamic banks are favourable and contribute significantly to the growth of the Malaysian economy. There was an accumulation of meaningful and wide-ranging investment over the period of the study and productivity of capital was also extra-efficient. The direction of causality is found to be bidirectional between Islamic banking deposits and Malaysian gross domestic product (GDP), but there is a weak causal effect from Islamic banking financing to GDP.

Malaysia has a dual financial system (conventional and Islamic) and both can affect its real economy. This research is limited to Islamic banking’s effects on Malaysian economic growth. The research also limits the scope and coverage for 20 years, from 1998 to 2017 to cover the years for which data is available for all the variables used in the study.

The results confirm that the Islamic banking sector in Malaysia is performing well in carrying out its major function of financial intermediation, which is the pooling and channelling of funds to productive investment activities. Consequently, the fact that Malaysia excels in Islamic finance is not a fluke. It is because of the effective performance of Islamic financial institutions in the country. Furthermore, Malaysian authorities are doing their level best in promoting Islamic financial activities.

The study fulfills the need to uncover the relationship between the Islamic financial system and the real economy in Malaysia. It differs from other studies as it uses the most recent available data, introduces new variables and identifies the channel by which Islamic banking development transmits growth.

This paper aims to analyze the effectiveness of aid in stimulating investment using different measures of aid and up-to-date panel time-series techniques. This study controls for endogeneity by using dynamic ordinary least squares (DOLS) and minimizes the risk of running a spurious long-run relationship by using series that are cointegrated. This paper finds evidence that aid promotes investment in countries with good institutional quality and gain interesting insights on the influence of country characteristics and the amount of aid received. Aid is ineffective in countries with unfavorable country characteristics such as a colonial past, being landlocked and with large distances to markets. Aid can boost investment in regions that receive high (above-median) amounts of aid such as Africa and the Middle East but not in regions that receive low amounts of aid. Investment-targeted aid is effective but non-investment-related aid can also enhance investment.

Regressions on the aid-investment nexus are based on either a rather simple (115 countries) or an extended/augmented investment model (91 countries). The data covers the period of 1973–2011 and 1985–2011 if the institutional quality is included. This study estimates the relationship between aid and investment by applying the DOLS/dynamic feasible generalized least squares technique which is based on a long-run relationship of the regression variables (cointegration). In this framework, this paper incorporates country-fixed effects, control for endogeneity, autocorrelation and take heteroscedasticity and cross-country correlation of the residuals into account.

This study finds empirical evidence that aid promotes investment in countries with good institutional quality and gain interesting insights on the role played by country characteristics and the amount of aid received. Aid is ineffective in countries with unfavorable country characteristics such as the colonial past, being landlocked, distant from markets. Aid can boost investment in regions that receive high (above-median) amounts of aid such as Africa and the Middle East. Investment-targeted aid is effective but non-investment-related aid also able to enhance investment.

The study looks at the investment to gross domestic product (GDP) ratio (including domestic investment and foreign direct investment (FDI)) and, hence does not disentangle these factors. It looks at the net effect (positive and negative impact together) and, therefore does not allow to identify the direct crowding out the impact of aid. Of course, if this paper finds that aid has a negative impact on investment, it is clear that aid must have crowded out either domestic investment or FDI or both.

The authors think that it is relevant to have identified the circumstances and settings in which foreign aid can be particularly effective and in which foreign aid needs accompanying measures that improve the effectiveness of aid. Also, it is relevant that the relative amount of aid received (aid-to-GDP ratio) must be quite high so that aid can increase investment.

This study sees that the least developed, low-income countries and (in terms of regions) the sub-Saharan Africa countries benefit from aid. This is very desirable. This paper further sees that higher relative amounts of aid do help more and that it is helpful to care about a better institutional quality in developing countries. Hence, this study provides some support for the desirability of aid.

The paper was done very diligently, and this study is very confident that the results are robust. This paper is also confident that this study has studied the long-run (which is of special importance) nexus between aid and investment. The estimation technique used is original, as it combines regular DOLS with corrections for autocorrelation and cross-section dependence.

The purpose of the paper is to test and analyze the equilibrium economic relationships of the East Africa Community (EAC).

To attain the study's purpose the authors applied the Johansen cointegration test, including long-run structural modeling (LRSM), vector-error-correlation-model (VECM) and variance-decomposition (VDC).

At I(1), both Philips‐Peron (PP) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests show that the East Africa member states' economies are cointegrated. The result was further substantiated by the tests based on Johansen cointegration and VECM procedures, showing significant long-run and short-run economic relations. The result further reveals that despite some uncommon issues among member states such as Tanzania and Kenya, however, their economic relationships remain significant though it is negative. Moreover, the finding revealed positive and significant short-run economic relationships between Kenya, Burundi and Rwanda.

The paper applies the cointegration techniques in the context of EAC. The result is likely to be adding value to the policymaker and also to the existing literature on the subject. This may trigger policy implications and open new research direction within the region and out.