Search results

This paper aims to examine the relationship between trade and economic growth using data of UAE economy for the period of 1974-2011.

The bounds testing is applied for testing the cointegration relationship between the variables. The rolling window approach has been used to analyze the stability of long run coefficients.

The empirical analysis shows the presence of cointegration between trade and economic growth. Furthermore, exports have positive, but imports have negative effect on economic growth. The rolling window approach confirms the stability of long-run estimates.

This paper provides new insights for policymakers to use trade as economic tool for sustainable economic development.

This paper makes a unique contribution to the literature with reference to UAE, being a pioneering attempt to investigate the relationship between trade and economic growth by using long time series data and applying more rigorous techniques like time varying rolling window analysis.

This study investigates the existence of the environmental Kuznets curve (EKC) for deforestation for Ghana over the 1962–2018 the time period.

The study employs a time-varying approach, the bootstrap rolling window Granger causality test to achieve its set objectives.

The results from our study reveals an inverted “N” shape EKC for deforestation, implying that deforestation will initially decrease with increases in economic growth up to a certain income threshold and increases with further increases in economic growth beyond this income threshold up to a higher income threshold and then decrease with further increases in economic beyond the higher income threshold.

The results from the study project show that over time economic growth can serve as a natural panacea to cure and mitigate the ills of deforestation that have plagued Ghana's forests over the years.

The results further highlight the important role of strong institutions in fighting the deforestation menace.

The originality of this study lies in its methodology which allows for feedback from deforestation to the economy. This is in contrast to earlier studies on the EKC for deforestation which allowed causality only from deforestation to the economy.

The study is intended to find out the relationship between housing prices and the inflow of foreign capital in Pakistan. There is a shortage of housing units due to rising population and rural–urban migration since its inception; on the other hand, there is also a lack of housing finances. The urban sprawl has created the demand for housing units, but the supply of housing has not been increased up to the required level, the major reason is a deficiency of housing finances.

The analysis was carried out from 1973 to 2018, on an annual, quarterly and monthly basis; the structural changes are captured by the Zivot–Andrews unit root test. Gregory–Hansen test is used for cointegration, the combined cointegration also validates the results. In addition, the rolling window is used to capture timely changes between data sets. Finally, wavelet analysis is used to prove volatility.

The rising prices of housing in the country is alarming; Pakistan is a developing country, and it is facing many problems along with a housing shortage. The domestic sources of housing finances are inadequate, so foreign funds are welcomed. The rolling window regression proves that domestic factors along with the foreign capital inflow affect housing prices positively, and the wavelet analysis finds out that foreign direct investment is more volatile than workers’ remittances in financing the housing market.

This is a pioneering study to find out the impact of foreign capital inflows on the housing prices in the economy of Pakistan. The inadequacy of housing finances from domestic sources attracted foreign funds financing this sector. This study has used new techniques like rolling window and wavelet transformation, such techniques have not been used before.

The purpose of this paper is to report on a sequential three-stage analysis of inflation persistence using monthly data from 11 inflation targeting (IT) countries and, for comparison, the USA, a non-IT country with a history of credible monetary policy.

First, the authors estimate inflation persistence in a rolling-window fractional-integration setting using the semiparametric estimator suggested by Phillips (2007). Second, the authors use tests for unknown structural breaks as a means to identify effects of the regime switch and the global financial crisis on inflation persistence. The authors use the sequences of estimated persistence measures from the first stage as dependent variables in the Bai and Perron (2003) structural break tests. Finally, the authors reapply the Phillips (2007) estimator to the subsamples defined by the breaks.

Four countries (Canada, Iceland, Mexico, and South Korea) experience a structural break in inflation persistence that coincide with the implementation of the IT regime, and three IT countries (Sweden, Switzerland, and the UK), as well as the USA experience a structural break in inflation persistence that coincides with the global financial crisis.

The authors find that in most cases the estimates of inflation persistence switch from mean-reversion nonstationarity to mean-reversion stationarity.

Monetary policy implications differ between pre- and post-global financial crisis.

Global financial crisis affected the persistence of inflation rates.

First paper to consider the effect of the global financial crisis on inflation persistence.

This research aims to examine the time-varying behavior of the Weekend, Turn-of-the-Month, January, and Halloween effects in eight foreign exchange rates against the U.S. dollar from the Adaptive Market Hypothesis (AMH) perspective. It also explores whether these anomalies can generate excess returns compared to a buy-and-hold strategy.

Using daily return data from January 2004 to December 2023 in a rolling-window framework, the study employs the Concordance Coefficient test and AR-GARCH models to assess the time-varying behavior of four calendar anomalies. It also assesses the statistical significance of the trading strategies implied by these anomalies using t-tests and applies F-tests for subperiod analysis.

The results reveal a generalized time-varying presence of calendar anomalies in emerging currencies and, to a lesser extent, developed currencies. However, the trading strategies implied by these anomalies generally did not show statistical significance, except for the Turn-of-the-Month effect, which exhibited statistically significant unprofitability.

The study pioneers an analysis of five calendar anomalies across various currencies from the standpoint of the AMH and proposes case-specific explanations for their occurrence. It also examines the potential for the anomalies’ implied trading strategies to generate excess returns compared to a straightforward buy-and-hold strategy. Additionally, the study introduces the recently developed Concordance Coefficient test as a valuable alternative to other non-parametric methods.

The purpose of this paper is to explore whether the out-of-sample model bias plays an important role in predicting volatility.

Under the heterogeneous autoregressive realized volatility (HAR-RV) framework, we analyze the predictive power of out-of-sample model bias for the realized volatility (RV) of the Dow Jones Industrial Average (DJI) and the S&P 500 (SPX) indices from in-sample and out-of-sample perspectives respectively.

The in-sample results reveal that the prediction model including the model bias can obtain bigger R², and the out-of-sample empirical results based on several evaluation methods suggest that the prediction model incorporating model bias can improve forecast accuracy for the RV of the DJI and the SPX indices. That is, model bias can enhance the predictability of original HAR family models.

The author introduce out-of-sample model bias into HAR family models to enhance model capability in predicting realized volatility.

This paper aims to examine the weak form of efficiency for price series of four precious metals, i.e. gold, silver, platinum and palladium, using a generalized spectral method.

The method has the advantage of detecting both linear and non-linear serial dependence in the conditional mean, and it is robust to various forms of conditional heteroscedasticity. The authors use three different rolling windows for the purpose of robustness.

The authors report weak form of efficiency across metals series for almost all rolling windows. The optimum efficiency for Gold and Palladium is achieved through 250 days rolling window estimates whereas it is 500 days rolling window for silver. Platinum has similar efficiency levels across rolling windows. The degree of efficiency for metal prices is observed to be varying over time with silver market possessing highest levels of efficiency. The efficiency synchronization also varies across rolling windows and metals.

The results reveal that metal markets are efficient for most times implying the low predictability and the low likelihood of earning abnormal returns by speculating in these markets.

The study uses a relatively new statistical technique, the generalized spectral test, to capture linear and non-linear serial dependence. Therefore, the results possess adequate power against departure from market efficiency.

The purpose of this paper is to construct a financial development index for China and to analyze the relationship between the financial sector development index and economic growth.

This study uses Johansen‐Juselius cointegration approach to determine long run relationship between variables. To determine the strength of causal relationship variance decomposition is used. The stability of coefficient is evaluated through rolling window regression method.

The results of Johansen‐Juselius cointegration approach confirm long run relationship between financial development index and economic growth. Normalized cointegrating vector indicates that financial development index, real interest rate, capital and labor force positively determine economic growth in China. The yearly coefficient is provided by the rolling regression and indicates that financial development index negatively link to economic growth in 1991, 1992, 1994, 1995, 1999, 2000, 2003‐2005. Interest rate is negatively linked to economic growth in 1991‐1996, 2007 and 2008. The variance decomposition method validates that shocks in financial development index and real interest rate are explained by economic growth.

A financial development index for China is constructed and the relationship between economic growth and financial development is indicated.

The purpose of this study is to construct a financial conditions index (FCI) for the South African economy to enable the gauging of financial conditions and to better understand the macro-financial linkages in the country. The global financial crisis that began in 2007-2008 demonstrated how severe the impact of financial markets’ stress on real economic activity can be. In the wake of the financial crisis, policy-makers and decision-makers across the world identified the critical need for a better understanding of financial conditions, and more importantly, their impact on the real economy.

The FCI is constructed using monthly data over the period 1966 to 2011, and is based on a set of 16 financial variables, which include variables that define the state of international financial markets, asset prices, interest rate spreads, stock market yields and volatility, bond market volatility and monetary aggregates. The authors explore different methodologies for constructing the FCI, including full sample and rolling-window principal components analysis. Furthermore, the authors investigate whether it is beneficial to purge the FCI of the real effects of inflation, economic growth and interest rates, and evaluate the performance of our constructed FCIs by comparing their ability to pick up turning points in the South African business cycle, and by running in-sample causality (forecast) tests.

The authors find that the estimated FCIs are good predictors of economic activity; with the rolling-window FCI being the “best” performing index. Causality tests indicate that this FCI is a good in-sample predictor of industrial production growth and the Treasury Bill rate, but a weak predictor of inflation.

The authors find that the resulting FCI can act as an “early warning system”. This, in turn, may serve to indicate that monetary policy should take broader financial conditions into account.

This study offers three main contributions to the existing literature on financial conditions in South Africa: the authors construct an FCI over a sample period that is three decades longer than existing indices, the FCI of this paper comprises a wider coverage of financial variables than others and the authors make use of rolling-window estimation techniques that allow them to account for parameter instability and to capture the real-time constraints faced by a policymaker.

The purpose of this paper is to investigate the causal link between foreign direct investment (FDI) and exports in China based on the knowledge capital model (KK model, Markusen, 2002).

The bootstrap Granger full-sample and sub-sample rolling window causality test is used to determine whether FDI can promote exports.

The full-sample causality test indicates no causal relationship from FDI to exports. However, considering structural changes of exports and FDI, the authors’ find that the full-sample test is not reliable. Instead, the authors use the rolling window causality test to revisit the dynamic causal relationship, and the results present significant effects from FDI on exports, mostly around periods in which the proportion of FDI from Hong Kong, Macao and Taiwan is increasing. Specifically, positive impacts of FDI on exports are stronger than the negative impacts in China.

The findings in this study suggest a significant time-varying nature of the correlation between FDI and exports. The promotion effect of FDI to exports is proved by the rolling window approach; it thus supports the KK model that divides FDI into lateral FDI and vertical FDI and proves that the constitution of FDI is critical to the relationship between FDI and exports.

China has been facing adjustment of its economic structure in recent years, and in this situation, increasing the proportion of FDI that can bring advanced production function is critical for the industrial structural adjustment.

This paper uses the bootstrap rolling window causality test to investigate the time-varying nature of the causality between FDI and exports, considering structural changes for the first time. The authors further deepen the previous research and draw a more realistic conclusion.