Dummy Variable Model Research Articles

Analyzing Impact of a Crisis on Bank Financial Ratios

In this study we provide a practical framework and methodology for analyzing the effects of banking shocks (economic or financial in nature) on bank fundamentals, that avoids the use of complicated econometrics methods. For this, we focus our attention to the effects of the 2007-2008 global financial crisis on the four largest US banks and examine the variation of trends in the select financial ratios for those institutions using quarterly regulatory data running from 2002-Q4 to 2020-Q2. We start by plotting time series charts of those financial ratios for each bank and compare the before-crisis, transition and after-crisis periods. For this, we simply fit trend lines with three parameters of shift, slope, and volatility to the banking data. The shift parameter describes the level change of the variable when before- and after-crisis periods are compared. The slope parameter pronounces the difference in steepness of the trend lines, while the volatility parameter is associated with all three periods and describe the variation in the data during each period. Our results indicate that capital ratios, an important regulatory financial ratio, are higher across the board in the after-crisis period compared to before-crisis period, suggesting a positive shift. We don’t see significant changes in slope parameter for the capital ratio series leading us to suggest the use of dummy variable regression model where slope is treated as a fixed constant. We further show that pre-crisis and transition periods are characterized by higher volatilities that ultimately subside in the after-crisis period. Lastly, we conclude by suggesting that financial practitioners use the shift, slope and volatility parameters in understanding trends in financial time series data since it is easy to implement and interpret the results compared to more sophisticated econometric models.

Analyzing Impact of a Crisis on Bank Financial Ratios

In this study we provide a practical framework and methodology for analyzing the effects of banking shocks (economic or financial in nature) on bank fundamentals, that avoids the use of complicated econometrics methods. For this, we focus our attention to the effects of the 2007-2008 global financial crisis on the four largest US banks and examine the variation of trends in the select financial ratios for those institutions using quarterly regulatory data running from 2002-Q4 to 2020-Q2. We start by plotting time series charts of those financial ratios for each bank and compare the before-crisis, transition and after-crisis periods. For this, we simply fit trend lines with three parameters of shift, slope, and volatility to the banking data. The shift parameter describes the level change of the variable when before- and after-crisis periods are compared. The slope parameter pronounces the difference in steepness of the trend lines, while the volatility parameter is associated with all three periods and describe the variation in the data during each period. Our results indicate that capital ratios, an important regulatory financial ratio, are higher across the board in the after-crisis period compared to before-crisis period, suggesting a positive shift. We don’t see significant changes in slope parameter for the capital ratio series leading us to suggest the use of dummy variable regression model where slope is treated as a fixed constant. We further show that pre-crisis and transition periods are characterized by higher volatilities that ultimately subside in the after-crisis period. Lastly, we conclude by suggesting that financial practitioners use the shift, slope and volatility parameters in understanding trends in financial time series data since it is easy to implement and interpret the results compared to more sophisticated econometric models.

Research and Development Intensity and Effective Tax Rate: Empirical Evidence from India

This article examines the link between research and development (R&D) intensity and effective tax rates (ETRs) in India. We added a new perspective to the current base erosion and profit shifting debate in two ways. First, the study extends the literature on base erosion and profit shifting by providing empirical evidence on the link between R&D intensity and ETR in the context of emerging economy of India. Second, we investigate the R&D intensity and ETR relationship in a dynamic context using Bias Corrected Least Square Dummy Variable Model. We find that R&D intensity has a significant and negative effect on both average and current ETR. The results show that the effect of R&D intensity on current effective tax was more than average ETRs. We also find that larger firms are associated with lower ETR. A significant negative association was also observed between ETR and capital intensity. The dynamic analysis confirms the habit persistence in the ETR that current period ETR is highly dependent on its previous year ETR.

DETERMINANTS OF ASSETS GROWTH OF FOREIGN OWNED BANKS IN INDONESIA

The aim of this research is to assess the dominant factors enabling foreign owned banks to increase their assets in Indonesia and to confirm whether the “follow the customers hypothesis” is also applicable in motivating foreign owned banks to do business in Indonesia to support investment and trade activities of the companies originated from foreign countries. Using the panel data of 28 foreign owned commercial banks in Indonesia between 2006-2015 obtained from Indonesian Banking Directory, Indonesian Banking Statistics databases, foreign country central bank websites, a least square dummy variable (LSDV) regression model was applied to examine the effect of Bilateral Trade, Foreign Direct Investment (FDI), Interest Rate Differences, Domestic Deposits, Parent banks Return on Assets (ROA) and Length of Time presence of the foreign owned banks in Indonesia on Assets or Size of Foreign Owned Banks. The main findings is that the decisions by foreign owned banks to operate and to expand its business in Indonesia is predominantly affected by the increase in realization of projects funded by Foreign Direct Investment from counterpart countries, third parties fund or domestic deposit denominated in foreign currencies, profitability of the parent banks in home country and long time presence in Indonesia to enable parent bank and their branches or subsidiaries gain better operating experience, better general managerial expertise and better knowledge of local environment. Bilateral Trade and Interest Rate Differences between home and host country has no impacts at all on Assets of Foreign Owned Banks. “Follow the customer hypothesis” is applicable in Indonesia only in terms of FDIs but not applicable in terms of bilateral trade. The findings will help management of the banks in designing more reliable business plan and also used as input or tools for policy makers prior issuing the license for the newly open foreign owned banks offices or increase of its branch offices. It is suggested foreign owned banks to consider empowering its trade financing scheme which will increase the banks size or assets.

The underlying trend of OPEC energy intensity and the environmental implications

AbstractGiven the upward trend of OPEC energy intensity, policymakers need a good understanding of the underlying factors and the environmental impacts when considering future energy policies. An index decomposition analysis is used to decompose OPEC energy intensity covering 1971–2017. The link between the decomposed energy indices and CO2 emissions is examined using structural time series and least square dummy variable corrected models. Both models also estimate the underlying carbon emission trend (UCET) which arguably reflects the impact of non‐economic factors. For OPEC as a group, increases in energy intensity are linked to both energy inefficiency and structural shifts towards energy‐intensive activities. About 62 per cent of the increases are attributed to the former, and the remaining 38 per cent is due to the later. The country‐level results also show major contributions from both components to energy intensity. The econometric results show that shifts towards energy‐intensive activities and, notably, deteriorating energy efficiency generally go in tandem with substantial increases in CO2 emissions. The estimated UCET is upward sloping indicating carbon‐emitting behaviour, taste and lifestyle. Therefore, policies aimed at conserving energy and limiting the concentration of energy‐intensive activities in the oil‐exporting countries should be considered alongside other policies that attempt to influence behaviours and lifestyles.

Forecasting the Import Price of Hardwood Bleached Kraft Pulp Using a Time-Series Model

Managing the import price risk of hardwood bleached kraft pulp (Hw-BKP) is a major concern of the Korea paper industry due to high price volatility. The primary objective of this study is to develop forecasting models for the import price of Hw-BKP as a tool to reduce price risk. Six different types of extended autoregressive models with various rolling window size were evaluated based on multiple accuracy measures. Results show that forecasts from the models with exogenous and monthly dummy variables are more accurate than the others. An autoregressive model with exogenous and monthly dummy variables model (ARMX) using a window size of 36 months outperforms the other models. When applying the model, it is possible to reduce the mean absolute error (MAE) by 24.6% and the root mean square error (RMSE) by 35.3% compared to a naive forecast model.

Modelling chronic winter hazards as a function of precipitation and temperature

In previous research, winter weather conditions like precipitation and cold were recognized as factors affecting the change in traffic volume, and a simulation has been conducted to model the effect of weather factor on variations in traffic flow. A winter weather hazard model considering natural chronic hazards in the winter season has been proposed. To achieve this purpose, this research formulated a dummy variable winter weather traffic model while considering explanatory variables such as expected daily volume, snowfall, and temperature. This model was derived using six-year traffic data that were collected on a weigh-in-motion site on Highway 44, in Alberta, Canada. Precipitation and cold data collected from a weather station were linked with traffic data to model traffic variations caused by weather factors. The performance of the model, on the ground of temporal transferability and model specification, was tested using data from different years. The temporal transferability test confirmed that the model can be successfully applied regardless of the year. It was also revealed that each vehicle class prefers a different model specification in estimating correct traffic volume.

The Carbon Dilemma: Intervening Effects of Technological Competition and Open Innovation

The relationship between carbon emissions and firms’ performance has long been the subject of debate, but no solid conclusions have yet been reached. This study provides a dynamic view of this relationship and presents a performance paradox derived from carbon emissions with an additional analysis on the roles of both technological competition and open innovation. Based on a sample of 584 Taiwanese high-tech manufacturing firms over six years (2012-2017) with 3,504 firm-year observations, and applying the least square with dummy variable (LSDV) regression model, we find support for our proposed hypotheses. Specifically, we find that carbon emissions present a performance paradox for firms, in that prior profitability performance (e.g., ROA) induces a high level of carbon emission which, in turn, deteriorates subsequent market performance (e.g., Tobin’s Q). We also find that the intensity of technological competition strengthens the positive relationship between prior profitability and carbon emissions, while firms’ engagement in coupled open innovation weakens the negative relationship between carbon emissions and market performance. Our study provide explanations for why firms do not or cannot reduce their carbon emissions while they are profitable, as well as how they can avoid this paradox. We also offer suggestions for theory and practice.

A Sales Forecasting Model for the Consumer Goods with Holiday Effects

In reality, there are so-called holiday effects in the sales of many consumer goods, and their sales data have the characteristics of double trend change of time series. In view of this, by introducing the seasonal decomposition and ARIMA model, this paper proposes a sales forecasting model for the consumer goods with holiday effects. First, a dummy variable model is constructed to test the holiday effects in consumer goods market. Second, using the seasonal decomposition, the seasonal factor is separated from the original series, and the seasonally adjusted series is then obtained. Through the ARIMA model, a trend forecast to the seasonally adjusted series is further carried out. Finally, according to the multiplicative model, refilling the trend forecast value with the seasonal factor, thus, the final sales forecast results of the consumer goods with holiday effects can be obtained. Taking the cigarettes sales in G City, Guizhou, China as an example, the feasibility and effectiveness of this new model is verified by the example analysis results.

The Effect of Tuition Fee Constraints on Financial Management: Evidence from Korean Private Universities

This study examined the effect of tuition fee control policy on universities’ financial management. Using data from 93 private universities in Korea from 2006 to 2015, we investigated the effect of tuition fees and government subsidies on labor cost, operating expenses, research expenses, and so on. Based on principal and agency theory, we used the analysis of average percentage change in expenditure and panel data analysis with the help of a Least Squares Dummy Variable (LSDV) model and polynomial regression. The results show that the increase rate of tuition fees decreased after 2011, with government subsidies increasing. The LSDV analysis indicates that universities increase labor costs, operating expenses, and student support fees, while there are no differences in research expenses, laboratory fees, and expenditures from investments and other assets. Polynomial regression reveals that, based on resources, universities behave differently in their spending. With these results, this study suggests a method to lessen information asymmetry and goal conflict, such as a performance-based research system and an incentive-based budget system in universities.

Agricultural Trends in Yavatmal Maharashtra - A District Level Analysis

This paper attempts to assess the changes in cropping pattern in Yavatmal district for the period from 1991 to 2010. We divide the period into two distinct periods: 1991-2000 and 2001-2010. The trends in the production of cereal crops, pulses and cash crops were observed using mean comparison t test and dummy variable regression model. The statistical and simple econometric exercises support the noticeable change occurred in the cropping pattern in the Yavatmal district during the period of economic reforms. The production of wheat was increased marginally during the period, but production of jowar crops drastically declined. The crops such as soybean and sunflower took over jowar during the study period. The trend showing decrease in overall production of cereals is a cause of concern for the government in particular and public in general. If the trend continues, it would be worrisome in terms of production of traditional crops.

Panel data analysis of employment and unemployment in Sub-Saharan Africa and its impact on annual percentage change in GDP growth

ABSTRACTSub-Sahara Africa has over the years been exposed to diverse arguments and agitations about the provision of employment to alleviate poverty in the sub region with the aim of achieving full employment. Whereas some economists and bibliographers believe of the significant impact that employment and unemployment have on annual percentage change in GDP growth, some do not. The main objective of the study is to access the impact that employment and unemployment have on annual percentage change in GDP growth in Sub-Saharan African countries. World Development Indicator (WDI) data was collected from the World Bank from the year 2000 to 2014 for the purpose of the study. A sample of 12 sub-Saharan African countries were randomly selected using a random number generator and variables taken. Two statistical models were used; the Pooled Ordinary Least Square (OLS) regression model and the Least Square Dummy Variable (LSDV) regression model with fixed effect was used to empirically access impact that employment and unemployment have on annual percentage change in GDP growth. Both the Pooled Ordinary Least Square regression model and the Least Square Dummy Variable regression model tested to be statistically significant but LSDV regression model explained a greater percentage of the variability of the model than the Pooled OLS for employment on GDP growth rate. The pooled OLS regression model was not statistically significant at 5% level of significance in assessing the impact that unemployment has on annual percentage change in GDP growth. Further empirical analysis of the study also revealed a positive relationship between annual percentage change in GDP growth and employment for the female population (Empl15+Female), employment rate for the male population (Empl15+Male) and employment rate for the Total Youth population (Empl15-24 total). Also, an inverse relationship existed between unemployment male youth population (15-24years), and female youth population (15-24years) on annual percentage change in GDP growth. The study can be improved by considering other factors that impact GDP growth other than employment and unemployment.Keywords: Employment, Unemployment, GDP growth rate, Pooled OLS, LSDV

Origin-based biomass allometric equations, biomass partitioning, and carbon concentration variations of planted and natural Larix gmelinii in northeast China

The area of larch (Larix gmelinii) plantations in northeast China has increased following the massive afforestation program implemented by China’s central government to provide biomass for global carbon sink and the development of industrial sectors. Hence, a better comprehension of equations used to characterize the estimation of regional biomass is much needed. A total of 301 sample trees of both planted and natural larch from northeast China were used to construct the additive systems of general and dummy variable models using weighted nonlinear seemingly unrelated regression (NSUR) to investigate the effect of forest origin on tree compartments and total biomass equations. Three different combinations of predictors were used in the two additive systems, and each biomass equation has its own specific weighting function to achieve the homoscedasticity in model residuals. Biomass partitioning, root-to-shoot ratio (RSR), and carbon concentration between the two origins were also analyzed. The results indicated that (1) the additive system of origin-based dummy variable model clearly outperformed the general additive model according to their respective predictors’ combinations and the difference between them were found to be significant; (2) based on the jackknife models’ validation, the two additive systems delivered good biomass predictions, of which the best models’ R2 were 0.939, 0.987, 0.910, 0.878, and 0.989 for root, stem, branch, foliage, and total biomass equations, respectively; (3) biomass allocations were varied between the two origins, and the RSR was higher in natural forests (0.32 ± 0.16) compared to plantations (0.25 ± 0.06) both averagely (±SD) and entirely across all diameter classes; (4) the mean carbon concentrations of root, stem, branch, and foliage observed in naturally regenerated trees were 1.81%, 2.90%, 1.23%, and 2.30% higher than those in plantations, respectively. The newly developed additive systems of origin-based equations can provide a more precise estimation for single-tree biomass of Larix gmelinii in China’s National Forest Inventory.

MODEL GRAVITASI INTERAKSI PERDAGANGAN INDONESIA DENGAN MALAYSIA DAN SINGAPURA

This study aims to look at the impact of interactions (gravity method) in explaining the trade relations between two countries. This study uses a different test, a dummy variable regression and VAR models to see the response of the shocks to the interaction of the gravity method. The data used starts from 2010 to 2018. The results show the gravity method can explain the trade relationship between Indonesia and Malaysia and Singapore. Indonesian exports to Malaysia respond negatively to interaction shocks while to Singapore to interaction shocks respond positively. Besides interactions, the AEC which began in 2016 also influenced the volume of Indonesian exports to Malaysia and Singapore.

Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach

ABSTRACT This study explores the forecasting of Major League Baseball game ticket sales and identifies important attendance predictors by means of random forests that are grown from classification and regression trees (CART) and conditional inference trees. Unlike previous studies that predict sports demand, I consider different forecasting horizons and only use information that is publicly accessible in advance of a game or season. The models are trained using data from 2013 to 2014 to make predictions for the 2015 regular season. The static within-season approach is complemented by a dynamic month-ahead forecasting strategy. Out-of-sample performance is evaluated for individual teams and tested against different least-squares dummy variable regression models and a naïve lagged attendance forecast. My empirical results show high variation in team-specific prediction accuracy with respect to both models and forecasting horizons. Linear and tree-ensemble models, on average, do not vary substantially in predictive accuracy; however, least-squares regression fails to account for various team-specific peculiarities, despite accounting for team fixed effects and censoring attendance predictions to fit to stadium capacities.

Risk and return analysis of listed financial companies in Ghana (a study of Cal Bank, Ecobank GH Ltd, Ghana Commercial Bank ( GCB) and Standard Chartered Bank (SCB))

ABSTRACTGlobally, Investors as well as business men and women are very optimistic about the future and therefore defer current use of resources for use at a later period with a higher expected rate of return. This is not so different in the case of Ghana. The main objective of the study is to analyse the risk and return of listed financial companies in Ghana. A sample of four financial companies listed on the Ghana Stock Exchange (Cal Bank Limited, Ecobank Ghana Limited, GCB Bank Limited and Standard Chartered Bank Ghana Limited) were selected and financial ratio (return on equity, return on asset, current ratio, quick ratio and financial leverage) computed using excel and analyzed using STATA. The study fitted a Pooled Ordinary Least Square (OLS) and Least Square Dummy Variable (LSDV) regression model with fixed effect model since fixed effect was tested to be statistically significant in the model. Empirical analysis of the data revealed that Banks with high risk levels proved to have a higher profitability as compared to the other banks under study. Standard Chartered Bank Ghana Limited and Ecobank Ghana Limited showed the highest levels of profitability, with Standard Chartered Bank Ghana Limited topping the charts in most years. However, on average, Cal Bank Ghana recorded the lowest rates of profitability.Also, a positive relationship existed between profitability (return on equity) and current ratio and financial leverage whilst an inverse relationship between return on equity and quick ratio. Overall model for both the Pooled OLS and the LSDV regression model tested to be statistically significant and a greater percentage (99.99%) of the total variability in return on equity was explained by the independent variables (return on asset, current ratio, quick ratio and financial leverage).Key words: Risk, Return, Pooled OLS regression model, LSDV regression model.

Estimating effects of information and communication technology (ICT) on the productivity of manufacturing industries in South Africa

This paper serves to examine whether the growth in labour productivity (LP) in South Africa’s manufacturing sector, following policy reforms after 1994, can be attributed to ICT use. To achieve this, we examine the link between ICT intensity and LP growth of 23 manufacturing industries for the period 1970–2016 and sub-periods 1970–1995 and 1996–2016. The industries are disaggregated into two groups, which are ‘more ICT-intensive’ and ‘less ICT-intensive’, using the ICT intensity index. Four dummy variable regression models are applied to test for the relationship between ICT intensity of industries and LP growth. The findings suggest that LP of the more ICT-intensive industries accelerated more than that of their counterparts. The results underscore the need for policy measures to increase ICT use with the aim of improving LP performance of industries.

Estimating aboveground forest carbon density using Landsat 8 and field-based data: a comparison of modelling approaches

ABSTRACTAccurately estimating and mapping forest aboveground carbon density (AGCD) is important for evaluating forest carbon sequestration efficiency and dynamics. This study compares different modelling approaches for estimating AGCD using Landsat 8 Operational Land Imager (OLI) imagery and field forest inventory data. We tested it in Yongshun County, Hunan Province, China. Linear regression (LR) and Random Forest (RF) were used to map crown density (CD). We fitted models with and without CD dummy variables for AGCD estimations. The fitting results and performances of these two types of AGCD models were analysed and compared. There was a difference in the estimation of remote-sensing-based CD models and AGCD models; the coefficients of determination (R2) were 0.66 for LR and 0.84 for RF, respectively, in CD estimations; R2 values were 0.46 for LR and 0.65 for RF, respectively, in AGCD estimations as indicated by basic AGCD models. RF was the best algorithm for CD and AGCD estimations. All of the dummy variable AGCD models provided more accurate AGCD estimates than their basic AGCD counterparts. The dummy variable models (DLR and DRF), which took the observed CD as a dummy variable, were the most accurate for the R2 were 0.61 and 0.85, respectively. The AGCD estimations were significantly overestimated and underestimated by basic AGCD models in thin and dense CD classes, respectively. Nevertheless, none of the AGCD estimates produced by any of the dummy variable models showed this problem. The models that used predicted CD as a dummy variable were more consistent with those that considered observed CD as a dummy variable. The accuracies of AGCD estimations were significantly improved by the dummy variable AGCD models, especially in thin and dense plots; in addition, the CD estimations produced by CD models could be used as dummy variables in AGCD remote-sensing-based models. This work provides that a method considering CD dummy variable in remote sensing-based models was reliable for mapping AGCD at a regional scale.

The impact of remittances on economic growth in Bangladesh, India, Pakistan and Sri Lanka

This paper investigates the impact of workers’ remittances on economic growth of four South Asian emerging countries by employing balanced panel data from 1977 to 2016. Pooled OLS, fixed effects, random effects and dummy variable interaction models are used to estimate the impact of remittances. The empirical regression analysis confirms a negative effect of remittances on economic growth in Bangladesh, Pakistan and Sri Lanka. Conversely, remittances have a positive impact on economic growth in India. This study also indicates a joint significant and negative relationship between remittances and economic growth in four countries.

Foreign direct investment, globalisation challenges and economic development: an African sub-regional analysis

Foreign direct investment (FDI) inflow is a global phenomenon for Africa and developing economies alike. It is expected that globalisation would foster inflow of foreign capital, technology transfer and consequently economic development. This study aimed at examining the effect of FDI on economic development for developing economies in the SSA sub-region. The study used pooled data for 39 SSA countries for the period within years 2000 and 2018. The analysis method used for the examination was the fixed effect least square dummy variable regression model. The study found that FDI is tangibly significant in relation to economic development for SSA sub-region that depended less on it. The study recommends that, the government of SSA subregion should consider the extent to which they would exploit existing opportunities that globalisation affords vis-à-vis the inherent threats in order to maximise the gains and minimise the losses by guiding carefully the sector that foreign investment flows into.