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The purpose of this paper is to identify, analyse and document local traditional indicators used in drought forecasting in the Mzingwane Catchment and to assess the possibility of integrating traditional rainfall forecasting, using the local traditional indicators, with meteorological forecasting methods.

Self-administered structured questionnaires were conducted on 101 respondents in four districts of the Mzingwane Catchment area, namely, Beitbridge, Mangwe, Esighodini and Mwenezi from February to August 2012. In addition, key informant interviews and focus group discussions were also used in data collection and the collected data were analysed for drought history and demographics; drought adaptation and the use of drought forecasting methods in the catchment using Statistical Package for Social Science.

The paper reveals the growing importance of precipitation forecasts among Mzingwane communities, particularly the amount, timing, duration and distribution of rainfall. Rainfall was cited as the major cause of drought by 98 per cent of the respondents in the catchment. Whilst meteorological rainfall forecasts are available through various channels, they are not readily accessible to rural communities. Furthermore, they are not very reliable at local level. The paper shows that communities in the Mzingwane Catchment still regard local traditional knowledge forecasting as their primary source of weather forecasts. The paper finds that plant phenology is widely used by the local communities in the four districts for drought forecasting. Early and significant flowering of Mopane trees (Colophospermum mopane) from September to December has been identified to be one of the signals of poor rainfall season in respect to quantity and distribution and subsequent drought. Late and less significant flowering of Umtopi trees (Boscia albitrunca) from September to December also signals a poor rainfall season.

The paper fulfils an identified need to study and document useful traditional drought indicators. Furthermore, the paper provides a platform for possible integration of traditional drought forecasting and meteorological forecasting and ensure sustainable rural livelihood development. The paper is useful to both meteorological researchers and resource-constrained communities in Mzingwane Catchment.

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent literature. In this article, we discuss classical and Bayesian methods of estimating mixed-frequency VARs, and use them for forecasting and structural analysis. We also compare mixed-frequency VARs with other approaches to handling mixed-frequency data.

This paper looks at the application of economic and financial series in forecasting IPD monthly rental series. The approach follows that employed in classical business cycle work that seeks to decompose series into trend, cyclical and noise components and is the first time that it has been applied to IPD monthly data. Trend extraction is obtained by means of the Hodrick‐Prescott filter. Several potential indicator series are investigated together with their lead characteristics. The short‐term forecasts of these series are compared with naïve methods and a composite indicator. The results show the naïve methods, especially the Holt‐Winters method, and certain leading indicator series produce satisfactory short‐term forecasts, but the success is both sector and time‐dependent. This suggests that it is a worthwhile endeavour in identifying potential leading indicator series. The methodology presented in this paper should be seen as complementing existing approaches that employ standard econometric procedures in modelling rental growth.

The purpose of this paper is to investigate the effect of selected organizational factors on the performance of employees charged with sales forecasting, and to compare this across the different organizational environments of Central-Eastern European (CEE) retail chains.

The research involves seven major pan-European retail chain companies, with a total number of 201 respondents. Data were collected via a questionnaire [computer-aided personal interview (CAPI) and human-aided personal interview (HAPI) method] with a five-point scale evaluation of both dependent (organizational factors) and independent (performance indicator) variables. Cluster analysis was then used to derive the characteristics of average organizational environments, and correlation analysis was used to investigate the direction and size of the performance effect.

The results confirmed that different organizational environments have differing effects on the performance of forecasters. It also showed that the “hard core” factors (performance evaluation and information systems) do not have a dominant effect on employee performance in any of the environments regardless of their quality, and are aggregately outclassed by “soft” factors (communication lines and management support). Finally, the research indicated that among the personal attributes related to individual forecasters, domain and forecasting work experience have significant, beneficial effects on forecasting performance, whereas formal education level was detected to have a negative effect and can be, at best, considered as non-contributor.

The research results along with available literature enable us to define four management theses (focus on system, less on people; soft factors are equal to hard ones; higher formal education does not contribute to forecasting performance; and do not overestimate the social and morale situation on the working place) as well as four stages of organizational development, creating a practitioner’s guide to necessary steps to improve an environment’s key factors, i.e. performance evaluation, information systems and forecasting work experience.

Although there are regular studies examining the effect of organizational factors on employee performance, very few have explored this relationship in a forecasting context, i.e. in the case of employees charged with sales forecasting. Furthermore, the paper brings evidence on this topic from the CEE area, which is not covered in most prominent forecasting management studies.

This research paper is informed by a study to assess performance of local knowledge drought forecasts (LKDFs) in the Mzingwane catchment which is located in the Limpopo River Basin in Zimbabwe. The purpose of this paper is to validate local traditional knowledge (LTK) indicators being applied in Mzingwane catchment and verify their accuracy and reliability in drought forecasting and early warning.

LTK forecast data for 2012/2013 season were collected through structured questionnaires administered to 40 selected household heads and focus group discussions. Observations and key informant interviews with chiefs and the elderly (>55 years) were also used to collect additional LTK forecast data. Meteorological data on seasonal rainfall were collected from the meteorological Services Department of Zimbabwe (MSD). Two sets of comparisons were conducted namely the hind-cast comparison where the LKDF system results were evaluated against what the season turned out to be and forecast comparison where local LKDF system results were compared with downscaled meteorological forecasts.

The results showed that the majority of the LTK indicators used were accurate in forecasting weather and drought conditions when compared to the observed data of what the season turned out to be. LTK forecasts were found to be more accurate than meteorological forecast at local scale. This study has shown that the reliability of LTKs is high as demonstrated by the fact that the predicted event occurs.

Further validation be carried out for a number of seasons, in order to standardise the LTK indicators per geographical area.

The research creates platform for adoption of LTKs into formal forecasting systems. The research is useful to both meteorological researchers and resource constrained communities in Mzingwane catchment.

This paper aims to make a systematic review of the manpower prediction model of the construction industry. It aims to determine the forecasting model's development trend, analyse the use limitations and applicable conditions of each forecasting model and then identify the impact indicators of the human resource forecasting model from an economic point of view. It is hoped that this study will provide insights into the selection of forecasting models for governments and groups that are dealing with human resource forecasts.

The common search engine, Scopus, was used to retrieve construction manpower forecast-related articles for this review. Keywords such as “construction”, “building”, “labour”, “manpower” were searched. Papers that not related to the manpower prediction model of the construction industry were excluded. A total of 27 articles were obtained and rated according to the publication time, author and organisation of the article. The prediction model used in the selected paper was analysed.

The number of papers focussing on the prediction of manpower in the construction industry is on the rise. Hong Kong is the region with the largest number of published papers. Different methods have different requirements for the quality of historical data. Most forecasting methods are not suitable for sudden changes in the labour market. This paper also finds that the construction output is the economic indicator with the most significant influence on the forecasting model.

The research results discuss the problem that the prediction results are not accurate due to the sudden change of data in the current prediction model. Besides, the study results take stock of the published literature and can provide an overall understanding of the forecasting methods of human resources in the construction industry.

Through this study, decision-makers can choose a reasonable prediction model according to their situation. Decision-makers can make clear plans for future construction projects specifically when there are changes in the labour market caused by emergencies. Also, this study can help decision-makers understand the current research trend of human resources forecasting models.

Although the human resource prediction model's effectiveness in the construction industry is affected by the dynamic change of data, the research results show that it is expected to solve the problem using artificial intelligence. No one has researched this area, and it is expected to become the focus of research in the future.

In recent work, we have developed a theory of economic forecasting for empirical econometric models when there are structural breaks. This research shows that well-specified models may forecast poorly, whereas it is possible to design forecasting devices more immune to the effects of breaks. In this chapter, we summarise key aspects of that theory, describe the models and data, then provide an empirical illustration of some of these developments when the goal is to generate sequences of inflation forecasts over a long historical period, starting with the model of annual inflation in the UK over 1875–1991 in Hendry (2001a).

The purpose of the paper is to forecast economic indicators of the Saudi economy in the context of low oil prices which have taken a toll on the Saudi oil-dependent economy between 2014 and 2017. Trades and investments have plummeted, leading to significant budget deficits. In response, the government unveiled a plan called Saudi Vision 2030 in 2016 which has triggered structural economic reforms leading to an unprecedented strategy of transition from an oil-driven economy to a modern market economy.

This paper forecasts with spectral analysis economic indicators of the Saudi economy up to 2030 to provide a clearer picture of the future economy assuming that the effects of recent reforms have not yet been traced by most of the economic indicators.

2018–2030 forecasts are all bearish except West Texas Intermediate (WTI) oil price expected to average $64.40 during the period 2019–2030. Two additional exceptions are the Saudi population that should grow to 40 million in 2030 and the swelling gross domestic product (GDP) generated by the non-oil sector resulting from bold actions of the Saudi government who is willing to become less dependent on revenues generated by the oil sector.

Government policymakers, economists and investors would have with spectral forecasts better insight and understanding of the Saudi economy dynamics at the early stage of major economic reforms implemented in the country. In 2020, the COVID-19 pandemic has brutally hurt the Saudi economy following a collapse in the global demand for oil and an oversupplied industry. The impact on the Saudi economy will depend on the optimal response brought by its government.

Saudi Vision 2030 plan has already triggered a deep transformation of the Saudi society that is reviewed in this paper.

The forecast of Saudi economic indicators is a timely topic considering the challenges facing the economy and reforms being undertaken. Applying an original forecasting technique to economic indicators adds to the originality of the paper.

This study aims to provide a comprehensive analysis of various approaches to the residential property market evolution modelling and to examine the macroeconomic fundamentals that have shaped this market development in Ukraine in recent years.

The study uses a comprehensive data set encompassing relevant macroeconomic indicators and historical apartment prices. Multifactor linear regression (MLR) and ridge regression (RR) models are constructed to identify the impact of multiple predictors on apartment prices. Additionally, the ARIMAX model integrates time series analysis and external factors to enhance modelling and forecasting accuracy.

The investigation reveals that MLR and RR yield accurate predictions by considering a range of influential variables. The hybrid ARIMAX model further enhances predictive performance by fusing external indicators with time series analysis. These findings underscore the effectiveness of a multidimensional approach in capturing the complexity of housing price dynamics.

This research contributes to the real estate modelling and forecasting literature by providing an analysis of multiple linear regression, RR and ARIMAX models within the specific context of property price prediction in the turbulent Ukrainian real estate market. This comprehensive analysis not only offers insights into the performance of these methodologies but also explores their adaptability and robustness in a market characterized by evolving dynamics, including the significant influence of external geopolitical factors.

A major concern for policymakers and researchers is to ascertain the movement of price levels and employment rates. Predicting the trends of these variables will assist the government in making policies to stabilize the economy. The objective of this chapter is to forecast the unemployment rate and Consumer Price Index (CPI) for the period 2022 to 2031 for the Indian economy. For this purpose, the authors analyse the prediction capability of the univariate auto-regressive integrated moving average (ARIMA) model and the vector autoregressive (VAR) model. The dataset for India's annual CPI and unemployment rate pertains to a 30-year time period from 1991 to 2021. The result shows that the inflation forecasts derived from the ARIMA model are more precise than that of the VAR model. Whereas, unemployment rate forecasts obtained from the VAR model are more reliable than that of the ARIMA model. It is also observed that predicted unemployment rates hover around 5.7% in the forthcoming years, while the forecasted inflation rate witnesses an increasing trend.