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Today, developing economies are of major importance for global macroeconomic development. However, the empirical analysis and especially the forecasting of macroeconomic time series remain difficult due to a lack of sufficient data, data frequency, high volatility, and non-linear developments. These difficulties require more sophisticated approaches to obtain reliable forecasts. Therefore, we propose an improved forecasting method especially for growth data based on a data-driven local linear trend estimation with an extended iterative plug-in algorithm for determining the bandwidth endogenously. This approach allows a smooth trend estimation that takes care of temporary changes in trend processes. Further, the naïve random walk model is extended for forecasting by including a local linear, time-varying drift. We apply this method to GDP development for six developing and two advanced economies and compare different forecast combinations. The combinations that include the local linear approach and the random walk with a local linear trend improve forecasting accuracy and reduce variance. [ABSTRACT FROM AUTHOR]

The University of the Philippines (UP) uses high school grades and standardized test scores for predictive admissions, balancing academic performance with affirmative action for underprivileged applicants. Using a novel UP panel dataset, I find that high school grades better predict academic performance, while entrance exam scores reflect background characteristics. Simulations suggest that reducing the weight of standardized exams boosts the qualification rates for public and private school students, but lowers it for science high schools and Metro Manila students. The findings indicate that relying more on grades could enhance both excellence and equity in college admissions. [ABSTRACT FROM AUTHOR]

We extend the Fama–MacBeth regression framework for cross-sectional return prediction to incorporate big data and machine learning. Our extension involves a three-step procedure for generating return forecasts based on Fama–MacBeth regressions with regularization and predictor selection as well as forecast combination and encompassing. As a by-product, it provides estimates of characteristic payoffs. We also develop three performance measures for assessing cross-sectional return forecasts, including a generalization of the popular time-series out-of-sample R 2 statistic to the cross section. Applying our extension to over 200 firm characteristics, our cross-sectional return forecasts significantly improve out-of-sample predictive accuracy and provide substantial economic value to investors. Overall, our results suggest that a relatively large number of characteristics matter for determining cross-sectional expected returns. Our new method is straightforward to implement and interpret, and it performs well in our application. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to improving forecasts of stock market indexes by considering common stock prices as hierarchical time series, combining clustering with forecast reconciliation. We propose grouping the individual stock price series in various ways including via metadata and using unsupervised learning techniques. The proposed approach is applied to the Dow Jones Industrial Average Index and the Standard & Poor 500 Index and their component stocks, and the results obtained with different grouping approaches are compared. The results empirically demonstrate that the combined use of clustering and reconciliation improves the forecast accuracy of the stock market indexes and their constituents. [ABSTRACT FROM AUTHOR]

In this study, we develop the first daily news-based Economic Policy Uncertainty (EPU) index for Nigeria, which was previously not covered in recent EPU indices. The need to track economic uncertainties in Nigeria becomes crucial for investment and policy, especially with the renewed interest in the country as an important investment destination. To construct the EPU index, we use relevant keywords from articles in prominent newspapers in the country, covering the aftermath of the global financial crisis and the COVID pandemic, with a data scope of January 2010 to November 2022. We evaluate the predictability of the index by examining its connection with economic and financial variables like exchange rates, stock prices, and inflation in Nigeria. The results are robust to alternative model specifications, data frequencies, and multiple forecast horizons. We hope to extend this exercise to other useful indices, including Geopolitical Risk, Financial Stress Indicators, and Monetary Policy Uncertainty, which are not readily available for Africa, including Nigeria. [ABSTRACT FROM AUTHOR]

This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of Merton's conditional Intertemporal Capital Asset Pricing Model (ICAPM) (1973), which predicts an average maximum of 1.83 percent of annual housing risk premium in REIT equity return. Moreover, the housing risk index explains a significant portion of the cross-sectional variation of sectoral REIT returns. In cross-section, the positive risk-return relationship remains significant after controlling for VIX, Fama–French four factors, and a broad set of macroeconomic and financial variables. I also find that the proposed housing beta accurately forecasts U.S. macroeconomic and financial conditions. [ABSTRACT FROM AUTHOR]

Stock index trend prediction is a very important topic in the finance. The purpose of this paper is to compare six ensemble learning related techniques for stock index direction prediction, including four boosting methods (Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost) and Gradient Boosting Decision Tree (GBDT)), one bagging method (Random Forest (RF)) and one tree-structured machine learning method (Decision Tree (DT)). The Shanghai Composite Index is chosen for experimental evaluation. A factor library of seventy-two technical factors, thirty-five macro factors and seven micro factors are our inputs. Our predictions are one month ahead, and each prediction model is evaluated by the Area Under Curve (AUC). The results indicate that ensemble learning techniques perform well in stock index prediction, with all AUC values above 0.5. RF is considered as the top algorithm with an AUC value of 0.7355 before feature selection and 0.6736 after feature selection. Also, we predict the stock index trend using a comprehensive factor library and three single factor libraries, respectively. The results show that forecasting stock index directions with a complete factor library is of great importance, which could achieve more stable forecasting results. This study contributes to literature in that it is, to the best of our knowledge, the first to make an extensive evaluation of ensemble learning related methods by constructing a comprehensive factor library and three single factor libraries. [ABSTRACT FROM AUTHOR]

Purpose: This study forecasts the government expenditure components in Nigeria, including recurrent and capital expenditures for 2021 and 2022, based on data from 1981 to 2020. Design/methodology/approach: The study employs statistical/econometric problems using the Feasible Quasi Generalized Least Squares approach. Expenditure forecasts involve three simulation scenarios: (1) do nothing where the economy follows its natural path; (2) an optimistic scenario, where the economy grows by specific percentages and (3) a pessimistic scenario that defines specific economic contractions. Findings: The estimation model is informed by Wagner's law specifying a positive link between economic activities and public spending. Model estimation affirms the expected positive relationship and is relevant for generating forecasts. The out-of-sample results show that a higher proportion of the total government expenditure (7.6% in 2021 and 15.6% in 2022) is required to achieve a predefined growth target (5%). Originality/value: This study offers empirical evidence that specifically requires Nigeria to invest a ratio of 3 to 1 or more in capital expenditure to recurrent expenditure for the economy to be guided on growth. [ABSTRACT FROM AUTHOR]

This paper applied the Cobb-Douglas production function to look into the impact of global crude oil prices on agricultural output in Ghana. Annual time-series secondary data spanning from 1980 to 2020 were utilized, sourced from the World Development Indicators and the US Energy Information Administration. The variables employed included agriculture value added per gross domestic product (GDP), international Brent crude oil prices, agricultural employment as a percentage of total employment, Tractors and agricultural equipment, as well as the proportion of land used for agriculture. To investigate the short- and long-term correlations between these variables, the Autoregressive Distributed Lag (ARDL) cointegration technique was employed. A unit root test was performed to evaluate the stationarity of the variables before ARDL analysis. The EViews Statistical/Econometric package facilitated the analysis. The findings from the ARDL cointegration estimates revealed a negative correlation between international crude oil prices and agriculture value added per GDP. According to the model, all variables exhibited cointegration over both the short and long term. Control variables, including agricultural employment as a percentage of total employment and agriculture machinery and tractors, showed a positive correlation with agriculture value added per GDP. However, agricultural land was found to be insignificant in influencing agricultural output.

Mobile money technologies in Malawi have revolutionised banking and monetary transactions across geographical barriers. Prospects of profit have drawn mobile money agents to invest in the business but find it is more profitable when substantial customers subscribe to the cash-in and cash-out facilities of mobile money. Despite the initial success, several challenges have emerged, including regulatory hurdles, network reliability issues, and the need for increased financial literacy among users. The volumes of transactions in the rural areas are observably lower compared to urban areas. This study uses Bvumbwe township in Malawi to model and forecast the discrepancy of mobile money transactions in rural and semi-urban Malawi. The study uses ARIMA modelling to understand the temporal manifestation of mobile money subscription in these localities. Using ARMA (1,1) models decomposed for the semi-urban and rural area, the study finds that the semi-urban area has a disproportionately higher and lasting volume of mobile money transactions compared to the rural area. The study also finds that mobile money transactions are more susceptible to long-lasting effects of external shocks in the rural area compared to the urban area. Intuitively, the day-to-day relationship in the transactions is also stronger in the rural area. These findings highlight the need for tailored policy interventions to enhance mobile money adoption and utilization in different geographical contexts.

Transmission channels from monetary shocks might be identified by studying the features of the production network. The main aim of this paper is to provide insights about the role of production network into the propagation of monetary policy shocks in G7 economies. Time-varying Bayesian vector-autoregressions were built to compute impulse response functions of output to monetary policy shocks in these countries. Panel Auto-Regressive Distributed Lag Bound Approach based on Mean-Group estimator was used to assess the long and short-run connections between production network structure and various shocks associated to monetary policy in the period 2000–2018 and during the Great Recession (2007–2009). The results show that upstreamness is more significant than downstremness in the period 2000–2018, while the financial sector significantly contributed to the spread of various monetary shocks during the Great Recession.

AbstractThe objective of this study is to predict unemployment in Indonesia in the wake of the demographic dividend. The sample used in this study is the unemployment data from 1990 to 2022 from the Indonesian Central Bureau of Statistics database. Using non-seasonal ARIMA (Autoregressive Integrated Moving Average) modeling, this study projected unemployment. It was predicted using six alternative models. With a mean absolute percent error (MAPE) of 9.56% (MAPE ≤10%), the predictions were quite accurate. It indicates that the ARIMA model has a good forecasting capability. According to the dynamic method’s unemployment projection, there will be less unemployment between 2023 and 2050. For Indonesia, maximizing the demographic dividend is both a challenge and an opportunity presented by the decline and stable number in unemployment. The demographic dividend will cause a substantial increase in employment and the creation of various new jobs. Several factors will support the demographic dividend. Thus, it could help governments to make decisions on labor issues. It also highlights a policymaker’s direction to pursue labor development, including employment trends.

AbstractTime series data from various sources usually results to variables measured at varying frequencies as this is often dependent on the source. Modeling from these data can be facilitated by aggregating high frequency data to match the relatively lower frequencies of the rest of the variables. This can easily lose vital information that characterizes the system ought to be modeled. Two semiparametric volatility models are postulated to account for covariates of varying frequencies without aggregation of the data to lower frequencies. First is an extension of the autoregressive integrated moving average with explanatory variable, integrating high frequency data into the mean equation. Second is an extension of the Glosten, Jagannathan, and Rankle model that incorporates the high frequency data into the variance equation. High frequency data were introduced as a nonparametric function in both models that are then estimated using a hybrid estimation procedure that benefits from the additive nature of the models. Simulation studies illustrate the advantages of postulated models in terms of predictive ability compared to GJR models that aggregates high frequency covariates to the same frequency as the output variable. An illustration is provided with stock return data from the Philippines. [ABSTRACT FROM AUTHOR]

We investigate corporate bond defaults from 1995 to 2020 using hand‐collected data from hard‐copy publications in Korea. Using an under‐sampling method, we construct default prediction models based on machine learning models as well as a logistic model. The empirical results show that the random forest model outperforms the others. However, regardless of the models used, model performance in financial crisis periods is significantly worse than it is in non‐crisis periods. This finding suggests the need for additional information to improve model performance during crises when the default prediction is the most relevant. Furthermore, the dominant predictor of defaults before the global financial crisis was the debt ratio, while subsequently, the coverage ratio has become the most important predictor. [ABSTRACT FROM AUTHOR]

Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and 'short' data imply a 'ragged edge' at both the beginning and the end of regional data sets, which adds a further complication. In this paper, via an application to the UK, we investigate various ways of including a wide range of short data into a regional mixed-frequency vector autoregression (MF-VAR) model. These short data include hitherto unexploited regional value-added tax turnover data. We address the problem of the two ragged edges by estimating regional factors using different missing data algorithms that we then incorporate into our MF-VAR model. We find that nowcasts of regional output growth are generally improved when we condition them on the factors, but only when the regional nowcasts are produced before the national (UK-wide) output growth data are published. [ABSTRACT FROM AUTHOR]