Search Results (9)

After 2010, the consumer price index fell to a low level in the EU. In the euro area, it remained low between 2010 and 2020. The European Central Bank has even had to take action against the emergence of deflation. The situation changed significantly in 2021. Inflation jumped to levels not seen for 40 years in the EU. Our study aims to use artificial intelligence to forecast inflation. We also use artificial intelligence to forecast stock index changes. Based on the forecasts, we propose portfolio reallocation decisions to protect against inflation. The forecasting literature does not address the importance of structural breaks in the time series, which, among other things, can affect both the pattern recognition and prediction capabilities of various machine learning models. The novelty of our study is that we used the Zivot–Andrews unit root test to determine the breakpoints and partitioned the time series into training and testing datasets along these points. We then examined which database partition gives the most accurate prediction. This information can be used to re-balance the portfolio. Two different AI-based prediction algorithms were used (GRU and LSTM), and a hybrid model (LSTM–GRU) was also included to investigate the predictability of inflation. Our results suggest that the average error of the inflation forecast is a quarter of that of the stock market index forecast. Inflation developments have a fundamental impact on equity and government bond returns. If we obtain a reliable estimate of the inflation forecast, we have time to rebalance the portfolio until the inflation shock is incorporated into government bond returns. Our results not only support investment decisions at the national economy level but are also useful in the process of rebalancing international portfolios. Full article

This paper augments the sparse literature on the inflationary impact of foreign debt in Brazil while addressing methodological caveats in previous studies. We depart from the linearity assumption and employ two nonlinear techniques: the nonlinear autoregressive distributed lag (NARDL) model and a Markov Switching Regression (MSR) to investigate the connection between foreign debt and inflation within a multivariate framework. The analyses consider the presence of structural breaks via assessing variable stationarity using the Zivot and Andrew unit root test and incorporating a residual-based cointegration test proposed by Gregory and Hansen. Additionally, we apply a multiple structural breakpoints test by Bai and Perron to determine the presence of structural breaks in the impact of foreign debt on inflation. Our findings robustly indicate that the domestic money supply has a statistically significant positive effect, while the nominal effective exchange rate has a negative effect on inflation in both the short and long run. The NARDL model reveals that only positive changes in foreign debt have a statistically significant negative effect on inflation in the short run, whereas both positive and negative foreign debt changes significantly affect inflation in the long run. The results from the MSR model are generally consistent with those of the NARDL model. Full article

Xinjiang is home to one of the most serious resource-based water shortages, and at the same time, it is an important main production area of grain, cotton, and high-quality fruits and vegetables in China, placing a heavy burden on water resources. Based on this, this paper determines the basic condition of water resources in regions of Xinjiang using the water footprint method. It then identifies the drivers of water footprint changes using the population scale effect, policy support effect, investment–output effect, economic structure effect, water use efficiency effect, and water use structure effect via the LMDI decomposition model. Finally, this paper illustrates the trajectory of the regional water footprint through individual stochastic convergence. This study found the following: (1) The water footprint of Xinjiang showed a fluctuating upward trend, and the total water footprint varied significantly between regions. From a compositional standpoint, most regions were dominated by the agricultural water footprint, while spatially, the regional water footprint had a high distribution trend in the south and a low distribution in the north. (2) The driving effects of the water footprint, policy support, population scale, and water use structure were incremental, while the effects of water use efficiency, economic structure, and investment output were decremental. (3) Most regions in Xinjiang showed individual stochastic convergence trends, indicating that regions converged to their respective compensating difference equilibrium levels. In this regard, it is necessary to strengthen R&D and the promotion of water use technology, further optimize the industrial structure, and leverage the positive effect of government investment to alleviate the regional water constraint dilemma and promote high-quality regional economic development. Full article

The present study examines the effects of the steep surge in crude oil prices which has also been considered as an oil price shock on the stock price returns and currency exchange rates of G7 countries, namely Canada, France, Germany, Italy, Japan, the United Kingdom (UK) and the United States (US), in the context of the Russia–Ukraine conflict. Due to the outbreak of the war, the steep surge in Brent crude oil price returns is seen as an exogenous shock to stock price returns and exchange rates during the period from 2 January 2017 to 29 June 2022. The paper applies the Fractionally Integrated GARCH (FIGARCH) model to capture the effect of the crude oil price shock and the Breakpoint unit root test to examine the structural breaks in the dataset. Structural breakpoints in the dataset for the entire stock price returns and exchange rates are observed during the period commencing from the last week of February, 2022, to the last week of March, 2022. Except for TSX, NASDAQ and USD, noteworthy long memory effects running from Brent crude oil price to all the stock price returns along with the currency exchange rates for all G7 countries were also found. Full article

We examined volatility spillover effects from five prominent global stock markets to India’s stock market during the pre-and-post COVID-19 outbreak using daily adjusted closing prices between January 2019 and September 2021 from six capital markets. The structural breakpoint was identified as 23 March 2020, as per the breakpoint unit root test, to examine and compare the results pre-and-post COVID-19. Results show that previous period news and volatility feeds the next period’s volatility significantly and the volatility is found to be persistent. The analysis also shows that during the pre-COVID period there is a negative significant volatility spillover from four of the five selected stock markets (Australia, China, Japan, and Germany) to the Indian stock market, and that spillover continues in the post-COVID period. There is a positive significant return and volatility spillover from the US market to the Indian stock market in the post-COVID-19 period. The results of our study will be useful for retail investors and portfolio managers in understanding the portfolio allocation methods in case of volatility spillover arising due to the crisis caused by the COVID-19 outbreak. Full article

This is a simulation-based warning note for practitioners who use the $M^{GLS}$ unit root tests in the context of structural change using different selection lag length criteria. With $T=100$ , we find severe oversize problems when using some criteria, while other criteria produce an undersizing behavior. In view of this dilemma, we do not recommend using these tests. While such behavior tends to disappear when $T=250$ , it is important to note that most empirical applications use smaller sample sizes such as $T=100$ or $T=150$ . The $AD{F}^{GLS}$ test does not present an oversizing or undersizing problem. The only disadvantage of the $AD{F}^{GLS}$ test arises in the presence of $MA(1)$ negative correlation, in which case the $M^{GLS}$ tests are preferable, but in all other cases they are very undersized. When there is a break in the series, selecting the breakpoint using the Supremum method greatly improves the results relative to the Infimum method. Full article

This paper investigates the long-term and causal relationship between energy intensity, real GDP per capita, urbanization and industrialization in Saudi Arabia over the period 1971–2012 using the breakpoint unit root tests developed by Perron (1989) and the autoregressive distributed lag (ARDL) model bounds testing to cointegration proposed by Pesaran et al. (2001) and employing a modified version of the Granger causality test proposed by Toda and Yamamoto (1995). Additionally, to test the robustness of the results, the fully modified ordinary least squares (OLS) regression, the dynamic OLS regression, and the Hansen test are used. Our results show that the variables are cointegrated when energy intensity is the dependent variable. It is also found that urbanization positively affects energy intensity in both the short term and the long term. Causality tests indicate that urbanization causes economic output that causes energy intensity in the long term. Our results do not support the urban compaction hypothesis where urban cities benefit from basic public services and economies of scale for public infrastructure. Therefore, measures that slow down the rapid urbanization process should be taken to reduce energy intensity in Saudi Arabia. In addition, reducing energy inefficiency in energy consumption should be a strategy to attain sustainable development in the near future in Saudi Arabia. Full article

The classical Chow test for structural instability requires strictly exogenous regressors and a break-point specified in advance. In this paper, we consider two generalisations, the one-step recursive Chow test (based on the sequence of studentised recursive residuals) and its supremum counterpart, which relaxes these requirements. We use results on the strong consistency of regression estimators to show that the one-step test is appropriate for stationary, unit root or explosive processes modelled in the autoregressive distributed lags (ADL) framework. We then use the results in extreme value theory to develop a new supremum version of the test, suitable for formal testing of structural instability with an unknown break-point. The test assumes the normality of errors and is intended to be used in situations where this can be either assumed nor established empirically. Simulations show that the supremum test has desirable power properties, in particular against level shifts late in the sample and against outliers. An application to U.K. GDP data is given. Full article

The main purpose of this paper is to evaluate the effect of crude oil price on global fertilizer prices in both the mean and volatility. The endogenous structural breakpoint unit root test, ARDL model, and alternative volatility models, including GARCH, EGARCH, and GJR models, are used to investigate the relationship between crude oil price and six global fertilizer prices. The empirical results from ARDL show that most fertilizer prices are significantly affected by the crude oil price while the volatility of global fertilizer prices and crude oil price from March to December 2008 are higher than in other periods. Full article