Search results

Fiscal stress has forced local governments to pay increasing attention to revenue trends and has increased the importance of financial forecasting in local government. After reviewing the role of revenue forecasting in financial planning and discussing the use of regression and econometric analysis in revenue forecasting, this article applies this technique to forecast several key revenue components in a medium-sized city. Three general conclusions may be drawn: (1) systematic revenue forecasting and long-range planning are necessities, not luxuries, (2) risk aversion to "technical" revenue forecasting can be overcome, and (3) the implementation of a systematic revenue forecasting system does not require a battery of "rocket scientists." As municipal revenue bases come to rely less on relatively stable property taxes and more on less stable sources such as sales taxes, fees, and charges, the use of a regression and econometric based model should prove increasingly fruitful.

A striking feature of inflation in developing countries is the high variability from one period to another. This feature would seem to make forecasting of the inflation rate a difficult task. This article applies three different forecasting techniques to predict the monthly rate of inflation in India over the period June, 1971 to May, 1980: The three methods used include the more conventional regression method and the newer time‐series and combined regression‐time‐series methods. A subsidiary objective is to examine the empirical applicability of the monetarist model of inflation in a developing economy. It is found that the combined regression‐time‐series model and the regression model are good predictors of the monthly rate of inflation in India. The results also indicate that the monetarist model performs well in predicting the monthly inflation rate in a regression framework.

Although India is seen as a key player in the global economy, it is still below its potential level of growth. In this age of globalism, integration with the global economy through trade and foreign investments fosters domestic growth. For India, although this integration has strengthened over the years, there are certain gaps that remain to be addressed. Though numerous studies in the literature have tried to find answers to these questions, an important aspect that has not been considered by these studies relates to India’s federal structure and the role of states in determining the aggregate economic outcome. As Foreign Direct Investment (FDI) inflows to India are concentrated in a few states, this paper aims to provide an assessment of the reasons behind this trend.

This paper aims to investigate the reasons behind the interstate differences with respect to FDI inflows in India. The analytical work undertaken for this paper is based on secondary data, collected and collated from various sources. The approach adopted for this paper includes a heat graph analysis to examine whether there is a clear pattern in terms of the state-specific factors for high FDI states versus the low FDI states. This data analysis is followed by an econometric estimation to gauge the impact of state-specific factors in determining the FDI inflows.

As per the secondary data–driven heat graph and econometric analysis, factors like industrial output, social sector expenditure, judicial quality, connectivity indicators, labor cost and availability of credit, act as differentiators between high and low FDI-receiving states. It then becomes imperative to bridge the gap between the two sets of states in terms of these specific factors. Implementation and success of policy interventions can only be derived at the state level and therefore needs more decentralized approach.

This paper tries to identify the reasons that are responsible for FDI inflows being concentrated in a few Indian states. This involves a comprehensive analysis of several variables to understand whether there is a clear pattern where high-FDI states are also in a better position with respect to these attributes. This effort to factor in the federal aspect of a macroeconomic indicator like FDI provides new dynamic to this area of work.

Planning, both “operational” and “strategic”, relies on accurate forecasting. Planning in tourism is no less dependent on accurate forecasts. However, tourism demand forecasting has been dominated by the application of regression/econometric techniques. Past studies on the forecasting accuracy of econometric/regression models suggest that forecasts generated by these models are not necessarily superior to forecasts generated by simple time series techniques. Seven time series forecasting techniques were used to generate forecasts of international tourist arrivals from Thailand to Hong Kong. The results confirm that simple techniques may be just as accurate and often more time‐and cost‐effective than more complex ones. Practitioners in the tourism industry may confidently use any of the forecasting techniques demonstrated here for their short‐term planning activities.

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.

This study aims to investigate a new determinant of corporate cash holdings of Australian energy firms: economic policy uncertainty (EPU). Based on two motives for holding cash: precautionary and speculative motives, the authors argue that EPU increases financing constraints or induces firms to postpone investment projects, thereby increasing their cash holdings. The authors examine whether the Australian policy-related economic uncertainty affects cash holdings of Australian energy companies.

This research uses a data set of Australian energy firms from 2010 to 2020 and the Australian EPU index, which measures the uncertainty in economic policy, using news coverage of eight major Australian newspapers. To address the potential endogeneity bias and ensure the robustness of the results, three models are used: ordinary least squares, fixed-effects and dynamic generalized method of moments.

The authors find that the EPU index has a significant and positive effect on cash holdings, after controlling for firm-specific factors. While firm size and dividend payments have mixed and insignificant effects, other determinants are significant, such as growth opportunities, net working capital, cash flow, cash flow risk, leverage and capital expenditure. The authors also find that the positive effect of EPU on cash holdings is not the manifestation of EPU affecting corporate investments but rather explained by financing constraints.

The findings have implications for policymakers and regulators in Australia as the uncertainty of their economic policies plays an important role when Australian energy companies determine their cash holding level to manage liquidity risks.

This study is the first to document EPU index as the new determinant of corporate cash holdings of Australian energy companies. Firms in this sector have a great need of funding and liquidity for their operations and capital-intensive projects. High EPU index induces them to hold more cash to avoid liquidity shocks.

The purpose of this paper is to investigate selected corporate governance attributes and financial reporting lag and their impact on financial performance of listed firms in Ghana.

The study uses 90 firm-year data for the period 2012–2014 for firms listed on the GSE. Each annual report was individually examined and coded to obtain the financial reporting lag. Descriptive analysis was performed to provide the background statistics of the variables examined. This was followed by regression analysis, which forms the main data analysis.

The descriptive statistics indicate that over the three years, the mean value of timeliness of financial reporting (ARL) is 86 days (SD 21 days), minimum is 55 days and maximum is 173 days. The regression analysis results indicate that financial reporting lag has a negative statistically significant relationship with firm performance. This negative sign indicates that when financial performances of companies are high (good news), companies have the tendency to disclose this situation early to the public.

Firms that are not timely in the financial reporting practices will find it difficult to attract capital as the delay will affect their reputation.

This study is one of the few to measure financial reporting lag and its impact on firm financial performance in Sub-Saharan Africa.

The application of short‐term forecasting techniques to the prediction of commercial rental values generates valuable information about the dynamics of rent movements. It also captures short‐run trends more effectively than do other forecasting procedures. Makes use of ARIMA models to provide one‐step‐ahead predictions. The results show that ARIMA models perform better in the case of retail and office sectors. The forecasts for these sectors are satisfactory. Retail rents bear a relationship to their past values, whereas office rents are influenced by shocks in the market – demand or supply driven. The results of the present study are useful for incorporation in more general models of rent forecasting. Also presents a full methodology which facilitates its application.

Measurement of diminishing or divergent cross section dispersion in a panel plays an important role in the assessment of convergence or divergence over time in key economic indicators. Econometric methods, known as weak σ-convergence tests, have recently been developed (Kong, Phillips, & Sul, 2019) to evaluate such trends in dispersion in panel data using simple linear trend regressions. To achieve generality in applications, these tests rely on heteroskedastic and autocorrelation consistent (HAC) variance estimates. The present chapter examines the behavior of these convergence tests when heteroskedastic and autocorrelation robust (HAR) variance estimates using fixed-b methods are employed instead of HAC estimates. Asymptotic theory for both HAC and HAR convergence tests is derived and numerical simulations are used to assess performance in null (no convergence) and alternative (convergence) cases. While the use of HAR statistics tends to reduce size distortion, as has been found in earlier analytic and numerical research, use of HAR estimates in nonparametric standardization leads to significant power differences asymptotically, which are reflected in finite sample performance in numerical exercises. The explanation is that weak σ-convergence tests rely on intentionally misspecified linear trend regression formulations of unknown trend decay functions that model convergence behavior rather than regressions with correctly specified trend decay functions. Some new results on the use of HAR inference with trending regressors are derived and an empirical application to assess diminishing variation in US State unemployment rates is included.

Joon Y. Park is one of the pioneers in developing nonlinear cointegrating regression. Since his initial work with Phillips (Park & Phillips, 2001) in the area, the past two decades have witnessed a surge of interest in modeling nonlinear nonstationarity in macroeconomic and financial time series, including parametric, nonparametric and semiparametric specifications of such models. These developments have provided a framework of econometric estimation and inference for a wide class of nonlinear, nonstationary relationships. In honor of Joon Y. Park, this chapter contributes to this area by exploring nonparametric estimation of functional-coefficient cointegrating regression models where the structural equation errors are serially dependent and the regressor is endogenous. The self-normalized local kernel and local linear estimators are shown to be asymptotic normal and to be pivotal upon an estimation of co-variances. Our new results improve those of Cai et al. (2009) and open up inference by conventional nonparametric method to a wide class of potentially nonlinear cointegrated relations.