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1 – 10 of over 2000Anthanasius Fomum Tita and Pieter Opperman
Homeownership provides shelter and is a vital component of wealth, and house purchase signifies a lifetime achievement for many households. For South Africa confronted with social…
Abstract
Purpose
Homeownership provides shelter and is a vital component of wealth, and house purchase signifies a lifetime achievement for many households. For South Africa confronted with social and structural challenges, homeownership by the low and lower middle-income household is pivotal for its structural transformation process. In spite of these potential benefits, research on the affordable housing market in the context of South Africa is limited. This study aims to contribute to this knowledge gap by answering the question “do changes in household income per capita have a symmetric or asymmetric effect on affordable house prices?”
Design/methodology/approach
A survey of the international literature on house prices and income revealed that linear modelling that assumes symmetric reaction of macroeconomic variables dominates the empirical strategy. This linearity assumption is restrictive and fails to capture possible asymmetric dynamics inherent in the housing market. The authors address this empirical limitation by using asymmetric non-linear autoregressive distributed lag models that can test and detect the existence of asymmetry in both the long and short run using data from 1985Q1 to 2016Q3.
Findings
The results revealed the presence of an asymmetric long-run relationship between affordable house prices and household income per capita. The estimated asymmetric long-run coefficients of logIncome[+] and logIncome[−] are 1.080 and −4.354, respectively, implying that a 1% increase/decrease in household income per capita induces a 1.08% rise/4.35% decline in affordable house prices everything being equal. The positive increase in affordable house prices creates wealth, helps low and middle-income household climb the property ladder and can reduce inequality, which provides support for the country’s structural transformation process. Conversely, a decline in affordable house prices tends to reduce wealth and widen inequality.
Practical implications
This paper recommends both supply- and demand-side policies to support affordable housing development. Supply-side stimulants should include incentives to attract developers to affordable markets such as municipal serviced land and tax credit. Demand-side policy should focus on asset-based welfare policy; for example, the current Finance Linked Income Subsidy Programme (FLISP). Efficient management and coordination of the FLISP are essential to enhance the affordability of first-time buyers. Given the enormous size of the affordable property market, the practice of mortgage securitization by financial institutions should be monitored, as a persistent decline in income can trigger a systemic risk to the economy.
Social implications
The study results illustrate the importance of homeownership by low- and middle-income households and that the development of the affordable market segment can boost wealth creation and reduce residential segregation. This, in turn, provides support to the country’s structural transformation process.
Originality/value
The affordable housing market in South Africa is of strategic importance to the economy, accounting for 71.4% of all residential properties. Homeownership by low and lower middle-income households creates wealth, reduces wealth inequality and improves revenue collection for local governments. This paper contributes to the empirical literature by modelling the asymmetric behaviour of affordable house prices to changes in household income per capita and other macroeconomic fundamentals. Based on available evidence, this is the first attempt to examine the dynamic asymmetry between affordable house prices and household income per capita in South Africa.
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Alexander Chudik, Kamiar Mohaddes, M. Hashem Pesaran and Mehdi Raissi
This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with…
Abstract
This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (
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Zachary Alexander Smith and Muhammad Zubair Mumtaz
The purpose of this paper is to examine whether there is significant evidence that hedge fund managers engage in deceptive manipulation of their reported performance results.
Abstract
Purpose
The purpose of this paper is to examine whether there is significant evidence that hedge fund managers engage in deceptive manipulation of their reported performance results.
Design/methodology/approach
A model of hedge fund performance has been developed using standard regression analysis incorporating dependent lagged variables and an autoregressive process. In addition, the extreme bounds analysis technique has been used to examine the robustness and sensitivity of the explanatory variables. Finally, the conditional influence of the global stock market’s returns on hedge fund performance and the conditional return behavior of the Hedge Fund Index’s performance have been explored.
Findings
This paper begins by identifying a model of hedge fund performance using passive index funds that is well specified and robust. Next, the lag structure associated with hedge fund returns has been examined and it has been determined that it seems to take the hedge fund managers two months to integrate the global stock market’s returns into their reported performance; however, the lagged variables were reduced from the final model. The paper continues to explore the smoothing behavior by conditioning the dependent lagged variables on positive and negative returns and find that managers are conservative in their estimates of positive performance events, but, when experiencing a negative result, they seem to attempt to rapidly integrate that effect into the return series. The strength of their integration increases as the magnitude of the negative performance increases. Finally, the performance of returns for both the Hedge Fund Index and the passive indices were examined and no significant differences between the conditional returns were found.
Research limitations/implications
The results of this analysis illustrate that hedge fund performance is not all that different from the performance of passive indices included in this paper, although it does offer investors access to a unique return distribution. From a management perspective, we are reminded that we need to be cautious about hastily arriving at conclusions about something that looks different or feels different from everything else, because, at times, our preconceived notions will cause us to avoid participating in something that may add value to our organizations. From an investment perspective, sometimes having something that looks and behaves differently from everything else, improves our investment experience.
Originality/value
This paper provides a well-specified and robust model of hedge fund performance and uses extreme bounds analysis to test the robustness of this model. This paper also investigates the smoothing behavior of hedge fund performance by segmenting the returns into two cohorts, and it finds that the smoothing behavior is only significant after the hedge funds produce positive performance results, the strength of the relationship between the global stock market and hedge fund performance is more economically significant if the market has generated a negative performance result in the previous period, and that as the previous period’s performance becomes increasingly negative, the strength of the relationship between the Hedge Fund Index and the global stock market increases.
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Fredrick Otieno Okuta, Titus Kivaa, Raphael Kieti and James Ouma Okaka
The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose…
Abstract
Purpose
The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.
Design/methodology/approach
The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.
Findings
The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.
Practical implications
A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.
Originality/value
While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.
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Bongumusa Prince Makhoba, Irrshad Kaseeram and Lorraine Greyling
The primary purpose of the study is to analyse the asymmetric effects of public debt on economic growth, using secondary data over the period 1980–2018 in South Africa.
Abstract
Purpose
The primary purpose of the study is to analyse the asymmetric effects of public debt on economic growth, using secondary data over the period 1980–2018 in South Africa.
Design/methodology/approach
This study estimated a Smooth Transition Regression (STAR) and Nonlinear Autoregressive Distributed Lag (NARDL) approach, using time series data to analyse the asymmetric effect of public debt on economic growth in South Africa.
Findings
The findings revealed a significant nonlinear relationship between public debt and economic growth in South Africa. The results showed an inverted U-Shape relationship, implying a significant positive influence of public debt on economic growth during the low-debt regime. While during a high-debt regime, public debt exerted a significant negative effect on economic growth. The study proposes that policymakers ought to consider targeting a sustainable debt threshold that would enhance efficient use of public finances consistent with long-term economic prosperity.
Originality/value
This paper asymmetries and threshold effects between public debt and economic growth in South Africa, through the application of dynamic nonlinear models namely, Smooth Transition Regression (STAR) and Nonlinear Autoregressive Distributed Lag (NARDL) approach. Studies on the relationship under examination have predominantly been confined in advanced economies. This study provides rigorous empirical evidence from the South African perspective.
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Iffat Zehra, Muhammad Kashif and Imran Umer Chhapra
This paper aims to examine association of money demand with key macroeconomic variables in Pakistan. The paper also investigates the asymmetric effect of real effective exchange…
Abstract
Purpose
This paper aims to examine association of money demand with key macroeconomic variables in Pakistan. The paper also investigates the asymmetric effect of real effective exchange rate (REER) on money demand.
Design/methodology/approach
The study employs both linear autoregressive distributed lag (ARDL) and non-linear autoregressive distributed lag (NARDL) model. Annual data from 1970 to 2018 is used which is subjected to non-linearity through partial sum concept. Empirical analysis is conducted to prove if money demand is influenced by currency appreciation or depreciation, for long and short run.
Findings
Cointegration test indicates existence of a long-run relationship between money demand and its determinants. Results from NARDL model suggest negative relation between money demand and inflation in long and short run. Real income shows positive but a very minimal and insignificant effect on money demand in long and short run. Impact of call money rates is statistically significant and negative on M1 and M2. Wald tests and differing coefficient sign confirm presence of asymmetric relation of REER in long run with M2, whereas in short run we observe a linear, symmetrical relation of REER with M1 and M2. Stability diagnostic tests (CUSUM and CUSUMSQ) verify stability of M2 demand model in Pakistan.
Practical implications
Results signify that role of money demand is imperative as a monetary policy tool and it can be utilized to achieve objective of price stability. Additionally, exchange rate movements should be critically examined by monetary authorities to avoid inflationary pressures resulting from an increase in demand for broad monetary aggregate.
Originality/value
The paper contributes to scarce monetary literature on asymmetrical effects of exchange rate in Pakistan. Impact of variables has been studied through linear approach, but this paper is unique since it attempts to explore non-linear relationships.
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Monica Singhania and Neha Saini
The paper attempts to revisit the nexus between economic growth, carbon emissions, trade openness, financial effectiveness and FDI for a sample of seven developed and developing…
Abstract
Purpose
The paper attempts to revisit the nexus between economic growth, carbon emissions, trade openness, financial effectiveness and FDI for a sample of seven developed and developing countries using curvilinear relationship as per environmental Kuznets curve (EKC) hypothesis over long term.
Design/methodology/approach
The authors determine the unit root properties of variables (using Clemente–Montañés–Reyes unit root test with double mean shifts and AO model and augmented Dickey–Fuller test) for structural breaks at different levels. Autoregressive distributed lag (ARDL) and error correction model (ECM) methodology was used to estimate long- and short-run parameters among the selected variables in sample countries from 1965 to 2016. Vector error correction (VEC) and Granger causality approach was used to determine the direction of causality.
Findings
The authors confirmed long-run relationship among the variables and highlighted high economic growth and energy consumption as the main causes of environmental degradation. While in India financial development and FDI inflows depict a negative association with environmental sustainability, however, such relationship was positive in the United Kingdom (UK), which is often considered as a benchmark for policymakers. The authors’ findings were in agreement with existing research insights in reporting FDI and financial development as the major contributors towards (unsustainable) sustainable environment through emissions in case of (developing country like India) developed country like UK. For other sample countries (China, Brazil, Japan, South Africa, United States of America (USA)), the authors’ model failed to capture financial development and FDI as significant contributors of carbon emissions. However, unidirectional causality running from energy to carbon emission was observed leading to the policy adoption of incentivizing alternative energy-based resources to increase energy efficiency across the energy value chain.
Research limitations/implications
Manufacturing with renewable energy, in collaboration with private and foreign players, under an institutional framework is desirable. Policy instruments including mandatory administrative controls, economic incentives and voluntary schemes that promote energy efficiency building blocks need to be established. A sound legal system for implementing technological innovation, financial subsidy incentives, interest-free loan programmes and development of financial sector supports creation and thriving of energy efficient units, often a perquisite for accelerated development.
Originality/value
By undertaking a comparative analysis, the authors address the research gap through revisiting EKC hypothesis with different set of trade policy and financial development framework. To the best of the authors’ knowledge, earlier studies were limited to one-country data analysis and did not consider the comparative data set of developed and developing countries with reference to financial development and FDI components.
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Michiel de Pooter, Francesco Ravazzolo, Rene Segers and Herman K. van Dijk
Several lessons learnt from a Bayesian analysis of basic macroeconomic time-series models are presented for the situation where some model parameters have substantial posterior…
Abstract
Several lessons learnt from a Bayesian analysis of basic macroeconomic time-series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time-series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical, and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented.
Irina Alexandra Georgescu, Simona Vasilica Oprea and Adela Bâra
The COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of…
Abstract
Purpose
The COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of geopolitical risk (GPR) and other indices on TA over 1995–2023.
Design/methodology/approach
We employ a nonlinear autoregressive distributed lag (NARDL) model to analyze the effects, capturing both the positive and negative shocks of these variables on TA.
Findings
Our research demonstrates that the NARDL model is more effective in elucidating the complex dynamics between macroeconomic factors and TA. Both an increase and a decrease in GPR lead to an increase in TA. A 1% negative shock in GPR leads to an increase in TA by 1.68%, whereas a 1% positive shock in GPR also leads to an increase in TA by 0.5%. In other words, despite the increase in GPR, the number of tourists coming to Russia increases by 0.5% for every 1% increase in that risk. Several explanations could account for this phenomenon: (1) risk-tolerant tourists: some tourists might be less sensitive to GPR or they might find the associated risks acceptable; (2) economic incentives: increased risk might lead to a depreciation in the local currency and lower costs, making travel to Russia more affordable for international tourists; (3) niche tourism: some tourists might be attracted to destinations experiencing turmoil, either for the thrill or to gain firsthand experience of the situation; (4) lagged effects: there might be a time lag between the increase in risk and the actual impact on tourist behavior, meaning the effects might be observed differently over a longer period.
Originality/value
Our study, employing the NARDL model and utilizing a dataset spanning from 1995 to 2023, investigates the impact of GPR, gross domestic product (GDP), real effective exchange rate (REER) and economic policy uncertainty (EPU) on TA in Russia. This research is unique because the dataset was compiled by the authors. The results show a complex relationship between GPR and TA, indicating that factors influencing TA can be multifaceted and not always intuitive.
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