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The purpose of this paper is to test the overshooting effects of monetary expansion on prices of agricultural products at farm production, processing and circulation stages in China, and to investigate the heterogeneity of the overshooting mechanisms in these three links.

Empirical results are obtained through the vector error correction model and the overshooting framework proposed by Saghaian et al. (2002b). Specifically, we first apply the Dickey–Fuller generalized least squares (DF-GLS) method to test the stationarity of the key variables, and then use the Johansen’s (1991) method to conduct the cointegration test. Finally, the vector error correction model is employed to examine the overshooting hypotheses in the three stages of China’s agricultural sector.

Empirical results indicate that overshooting of prices relative to monetary expansion in China’s agricultural sector is a common phenomenon, but with significant heterogeneity. Firstly, at the stage of agricultural production, the overshooting degree and restoration rate of material price are greater than those of agricultural products price. Secondly, at the processing stage of agricultural products, both the purchase price of agricultural products and industrial producer price have an overshooting effect, but the overshooting effect of the former is more significant than the latter. Thirdly, at the circulation stage of agricultural products, the overshooting coefficient of the wholesale price index of agricultural products is the most significant, while that of the retail and purchase price of agricultural products is not significant.

The paper contributes to proposing a comprehensive framework on testing the overshooting effects for three main stages of agricultural sector in China and empirically investigating the heterogeneity of the overshooting mechanisms in different stages with time series methods.

The purpose of this study is to examine two issues, namely the degree of current account deficit (CAD) sustainability and the degree of capital mobility.

The sample for this study comprises 24 Latin American and Caribbean countries, including three regional agreements: Andean Community, MERCOSUR (Mercado Común del Sur), and SICA (Central American Integration System). This study employs the dynamic common correlated effects mean group (DCCEMG) estimator in a panel data set to investigate the long-run relationship between savings and investment along with short-run dynamics.

The findings indicate that CAD is weakly sustainable in the Latin American and Caribbean region, MERCOSUR, and SICA, while CAD is strongly unsustainable in the Andean Community. The sub-period analysis reveals that CAD has been adversely affected by the 2008 crisis. However, in the post-crisis period, CAD has been slowly decreasing in the Latin American and Caribbean region and Andean Community, whereas CAD has continued increasing in MERCOSUR and SICA. Further, the estimates of error-correction terms and short-run coefficients indicate that the Andean Community and MERCOSUR observe a higher degree of long-run and short-run capital mobility than SICA.

The results carry fundamental implications for policy-making processes aimed at maintaining sustainable CADs.

This study gives an alternative interpretation of the “Feldstein-Horioka” coefficient in terms of CAD sustainability and analyses the saving–investment relationship in light of Chudik and Pesaran (2015).

This study investigates the long-run relationship between workers' remittances and human capital formation in Sri Lanka by using the macro-level time series data during the period of 1975–2020.

In this study, the augmented Dickey–Fuller (ADF) and Philips–Perron (PP) unit root tests, the autoregressive distributed lag (ARDL) bounds cointegration technique, the Granger causality test, the forecast error variance decomposition technique and impulse response function analysis were employed as the analytical techniques.

In accordance with the results of unit root tests, the variables used in this study are mixed order. Results of cointegration confirm that workers' remittances in Sri Lanka have both long-run and short-run beneficial relationship with human capital formation. The Granger causality test results indicate that there is a two-way causal relationship between workers' remittances and human capital formation. The results of forecast error variance decomposition expose that innovation of workers' remittances contributes to the forecast error variance in human capital in bell shape. Further, the empirical evidence of impulse response function analysis reveals that a positive standard deviation shock to workers' remittances has an immediate significant positive impact on human capital formation in Sri Lanka for a period of up to ten years.

This research provides insights into the workers' remittances in human capital formation in Sri Lanka. The findings of this study provides evidence that workers' remittances help to produce human capital formation.

By using the ARDL Bounds cointegration and other techniques in Sri Lanka, this study fills an important gap in academic literature.

This paper aims to understand the effect of a change in Economic Policy Uncertainty (EPU) of India and the USA on investors' sentiment in the Indian context, consisting of Sensex returns and volatility index (Vix).

The authors employ bounds testing approach to cointegration to capture the short-and long-run effects of EPU on investors' sentiment, along with impulse response functions and variance decompositions to check the effect of a shock on Sensex and Vix.

The study concludes the existence of a cointegrating relationship for both models, that is, Vix and Sensex. In the long-run, changes in EPU_India affect Vix and Sensex positively and negatively, respectively. On the other hand, EPU_USA affects Vix and Sensex positively. Furthermore, Gregory and Hansen (1996) cointegration with endogenous structural break reveals a long-run cointegrating relationship for both models.

The effect of EPUs on investors' sentiment reveals that when there is an uncertain event that adversely affects the stock prices, investors should not make haste to take a decision as the impact on stock prices perturbation might be temporary. Therefore, one should persevere for the dip in prices to hit the desired target.

Various studies look at the effect of cross-country EPU on the home country, However, there is no such study in the Indian context. The present study examines the impact of India's EPU on investors' sentiments after controlling the USA's EPU, one of India's largest trading partners and a key determinant of global economic policy.

The primary objective of the present study is to figure out the relative effectiveness of alternate public expenditure with regard to agricultural development particularly in the context of input subsidies vis-a-vis investment. Besides, the authors also endeavour to test the applicability of crowding-out hypothesis in the present context.

Initially, unit root tests are applied for checking stationarity of the underlying data using Augmented Dickey-Fuller (ADF) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests. Further, the highly celebrated autoregressive distributive lag (ARDL) model is applied on annual time series data for the period 1991–2020 to investigate the long-run and short-run impact of the said relationship.

The authors observe that public investment is more productive than input subsidies for overall agricultural development. Besides, the findings document the existence of crowding-in hypothesis, i.e. complementarity between public investment and private investment in case of the agricultural sector in India.

The outcome of the research recommends to reprioritize state expenditure and reformulate agricultural policy regarding the public financing of agriculture. More to invest and less to subsidize seems a better policy intervention to achieve desirable outcomes from the Indian agriculture in the long run.

This study is novel in the sense that the subsidies vs investment debate is revisited in the current scenario of agricultural development so that resource allocation be optimized. To ensure robustness of the study, the authors specifically took four proxies of agricultural development, namely, productivity growth, private investment, food security and farmers’ income.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

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 study examined the roles of public spending and population moderating characteristic structure of selected African economies on bank-based financial development through credit to private sector.

The study sampled 37 selected African economies for the years 1991–2018, and it applied a pooled mean group (PMG) estimator to account for short-run and long-run causal effects, and confirmed short-run adjustments towards the long-run convergences between the variables. Specific suitable tests were also applied.

Evidence confirms positive impacts of both capital formation and final consumption expenditures on financial development in the short run and long run. The moderation of population structures on expenditure structures help to speed up convergences.

This work attests its innovation by accounting for the separate effects of the expenditure types, the moderation effects of young and mature populations for capital and final consumption expenditure on financial development among selected economies in Africa.

This purpose of this study is to explore the impact of global trend of economic integration and interconnectedness which has drawn the attention of world economies and their implications on trade inflow. This trajectory has its impact, either positive/negative, on key macroeconomic indicators, to say the least on environmental sustainability, especially emerging economies. To this end, the need to explore the connection between foreign direct investment (FDI) inflow and energy consumption amidst the wave of economic globalisation is timely and pertinent for the case of Turkey.

This study seeks to explore the interaction between the outlined variables in a carbon-income framework for annual time series data from 1970 to 2016. A series of econometrics strategies was used consisting of unit root tests to examine the stationarity properties of the highlighted series. Subsequently, Pesaran’s Bounds testing technique is used to explore the long-run equilibrium relationship between the highlighted variables in conjunction with the Johansen cointegration test. For long-run regression coefficients, Pesaran’s autoregressive distributed lag and dynamic ordinary least squares methodology are used, and innovative accounting approaches are used to explore the responsiveness of each variable on another.

Empirical results validate the pollution haven hypothesis (PHH) in the long run for the case of Turkey. Thus suggesting that FDI inflow induced environmental degradation in Turkey. Additionally, this study observed that renewable energy, on the contrary, improves the quality of the environment. This study also affirms the presence of the environmental Kuznets curve phenomenon, indicating that Turkey, at its early stage of economic trajectory, emphasis is on economic growth rather than environmental quality. This suggests a need for more deliberate action(s) by the government administrators to pursue cleaner FDI inflow and energy technologies and strategies to foster a clean environment in Turkey and a cleaner ecosystem at large.

This study is unique in its choice of variables which is in line with the United Nations Sustainable Development Goals (SDGs) agenda to be achieved by 2030 and is very limited in the extant literature. From the economic perspective, the effect of the PHH is of interest especially to ascertain the extent the interplay among the variables has on the economy of Turkey. The empirical insights on PHH hypothesis have received less documentation in the extant literature especially for emerging economy like Turkey. Thus, this study seeks to revisit this theme for Turkey with aim to presents environmentally sustainable strategies without compromise for economic growth. Thus, this study seeks to revisit this theme.

The purpose of this study was to investigate the dynamic interactive link between housing prices, stock market price and effective exchange rate in the Iranian economy for a monthly period from April, 2004, to March, 2019. In addition, for a more accurate analysis, three control and determinates variables including real interest rate, real GDP and FDI have been added to the base model.

For this purpose, we will consider this issue by developing the study of Lean & Smyth (2014), Ali & Zaman (2017) and Coskun et al (2017) in the framework of ADRL and NARDL models. Also, this study analyzed the asymmetric/non-linear impact of stock market indexes and effective exchange rate on Iran’s housing inflation. Asymmetries imply to both positive and negative changes in the variables.

The results obtained from the ADRL and NARDL models suggest that the existence of cointegration relationship between housing market price and its determinants. From linear model, we found that the exchange rate and stock market price have a positive effect on the real estate inflation in the short run; this relationship is also confirmed in the long run. Other empirical results indicate that the GDP stimulates housing price in both long and short run cases, while FDI and real interest rate have an opposite effect. In addition, the results provided by the asymmetric model lead to the rejection of the null hypothesis of no co-integration between the variables. In addition, we found that the effect of stock price in the short and long term are asymmetric and there also is an asymmetric long-run effect of real exchange rate on the real estate price.

Finally, to analyze the sensitivity, we entered two explanatory variables of inflation and money supply to the baseline equation. The finding represented that in both linear and nonlinear framework, a positive correlation between these two variables with housing prices have been proved.