Brock Dechert Scheinkman Research Articles

Assessing effects of agriculture and industry on CO2 emissions in Bangladesh

Climate change is a critical global issue, driven primarily by the continuous rise in carbon dioxide (CO2) levels. Addressing this challenge requires innovative solutions and proactive measures to mitigate its impact. This study investigates the impact of Bangladesh’s industrialization, agriculture, and imports on CO2 emissions, exploring both linear and asymmetric relationships to inform sustainable development strategies. Advanced modeling techniques, namely autoregressive distributed lag (ARDL) and nonlinear autoregressive distributed lag (NARDL) models are used to evaluate the impact of Bangladesh’s agricultural and industrial sectors on CO2 emissions. Time-series data ranging from 1990 to 2022 are analyzed to ensure data stationarity, employing the augmented Dickey-Fuller (ADF) test. Subsequently, the existence of non-linear associations is validated using the Brock-Dechert-Scheinkman (BDS) test, with further confirmation through bounds testing to establish both symmetric and asymmetric long-run cointegrating relationships. Long and short-run coefficients are assessed using linear and asymmetry ARDL models, revealing that industrialization contributes to increased carbon emissions in Bangladesh. While the ARDL model reports that the effect of agriculturalization on CO2 emissions is insignificant in the long-run, the asymmetry ARDL model suggests a rapid reduction in carbon emissions due to agriculturalization, observed both in the long and short-run. Additionally, imports have considerable impact on carbon emissions. Diagnostic tests have confirmed the adequacy of the model, while stability tests have validated the estimated parameters’ stability. Finally, the direction of association between variables is determined by applying linear and nonlinear Granger causality tests. This study underscores the importance of promoting sustainable industrial practices, enhancing agricultural efficiency, and regulating imports as pivotal strategies for mitigating CO2 emissions and achieving enduring environmental sustainability in Bangladesh.

The Symmetric and Asymmetric Nexus Between Monetary Policy Interest Rate and Core Inflation in Jordan

Objectives: This study investigates monetary policy's symmetric and asymmetric impacts on core inflation in Jordan. It employs linear and nonlinear autoregressive distributed lag (NARDL) models. The research also seeks to assess the relationship between the overnight deposit rate, money supply growth, and real GDP growth on core inflation and to explore potential linear or nonlinear effects. Methods: The study applies ARDL and NARDL models to estimate the relationships above. It utilizes data from the first quarter of 1998 to the first quarter of 2023. Tests for Granger causality are conducted, and the Brock-Dechert-Scheinkman (BDS) test is employed to ascertain the presence of nonlinear paths within the study variable. Robustness checks are conducted to ensure parameter stability. Results: The analysis provides compelling empirical evidence supporting a long-term relationship between the overnight deposit rate and core inflation. The Brock-Dechert-Scheinkman (BDS) test confirms the existence of nonlinear dynamics. The ARDL model reveals a linear relationship: a 1% interest rate increase leads to a 0.26% reduction in core inflation in the long run. In contrast, the Nonlinear Autoregressive Distributed Lag (NARDL) analysis unveils an asymmetric effect: a 1% increase in the overnight deposit rate decreases core inflation by 0.43%, while a 1% reduction increases it by 0.37%. Conclusions: These findings have critical implications for monetary policymakers. They underscore the importance of judiciously considering interest rate adjustments to manage inflation dynamics, acknowledging the nonlinear and asymmetric effects inherent in the relationship between the overnight deposit rate and core inflation. When crafting monetary policy strategies, policymakers must consider these nuanced dynamics to address inflation concerns effectively.

Inspecting the efficiency of cryptocurrency markets: New evidence

The purpose of this study is to examine the market efficiency of cryptocurrencies, specifically at a weak level. The study focuses on six prominent cryptocurrencies selected based on their significant market capitalization: Bitcoin (BTC), Tether (USDT), Ethereum (ETH), Binance Coin (BNB-USD), Ripple (XRP-USD), and Cardano USD (ADA-USD). The analysis utilizes unit root, Ljung–Box, variance ratio, runs, and the Brock–Dechert–Scheinkman (BDS) tests to assess different aspects of market efficiency. The data spans from September 2017 to April 2023, encompassing a wide time frame to capture potential shifts in market behavior. The results of all the tests, except the BDS test, indicate that the tested cryptocurrencies' markets are inefficient. However, the BDS test yielded different results, suggesting that BTC and ETH exhibit market efficiency compared to the other cryptocurrencies. This discrepancy indicates that the BDS test may be capturing different aspects of the time series behavior. The practical implication is that investors and market participants should exercise caution and consider the varying levels of efficiency when making decisions regarding these cryptocurrencies. Also, investors should consider a range of factors, including technical and fundamental analyses, when making investment decisions in a dynamic and evolving market.

Non-Linear Impact of Public Debt on Inflation in Rwanda

Abstract This paper examines Rwanda’s debt-inflation dynamics and the asymmetric effect of government debt on inflation using time series data spanning the years 1980–2021. The study applied a battery of tests to examine the debt-inflation linkage in this country, including the bounds F-test for cointegration, the Brock-Dechert-Scheinkman (BDS) nonlinearity test, the Wald test for asymmetries and the nonlinear autoregressive distributed lag (NARDL) model for short run and long run asymmetric impact. The results of the NARDL bounds F-test for cointegration support a long-run nonlinear link between public debt and inflation in Rwanda. The findings of the BDS test reveal that the series is nonlinear in all dimensions, whereas the results of the Wald test show that public debt has an asymmetric impact on inflation, regardless of the timescale considered. The NARDL findings show that while a rise in public debt tends to be inflationary in the short run, a decline in public debt typically leads to a decrease in inflation in the long run. The study also demonstrates that inflation responds more rapidly and strongly to positive than negative shocks in public debt. The country should think about continuous enforcement of fiscal consolidation measures and reforms, as well as implementing economic growth-friendly policies to reduce debt increases in the short run. This will induce a decline in inflation and enhance macroeconomic stability in the long run.

Rural Energy Transition for Cooking in India—Revisiting the Drivers

The recent analysis from IEA (International Energy Agency) on energy transition in India highlights that cooking continues to be the weakest link in the energy transition process for rural households and that rural energy transition of households to cleaner fuel is nonlinear in nature. Several programs have been designed to plague the voids and address this nonlinearity, but the transition to cleaner alternatives for cooking did not happen in the pace it should ideally have. Therefore, an empirical exercise was carried out at a national level to revisit the disconnect between the income growth and energy transition and identify the drivers of the energy transition process in cooking at the national as well as at the subnational state level for a developing country. The paper adds to the current scholarship on drivers of household energy transition by analyzing the relationship between household energy choices and non-income determinants and proves the nonlinearity in energy consumption of rural households of Bihar. Analyzing unit level record from National Sample Survey, an empirical exercise was carried out by using multinomial logit model to identify the potential determining factors at the individual household and group level. The group effect analysis through fixed and random effect has been conducted purposely to understand if social and cultural norms or community level factors within a village society have any effect on the cooking energy transition of rural households and if that offsets the effect of household income in energy transition for cooking. Furthermore, to statistically examine the perceived non-linearity in the consumption of cooking fuel such as firewood by rural households, Brock–Dechert–Scheinkman (BDS) test was conducted for rural households of 38 districts of Bihar. The analysis helps in inferring that subsidy on modern fuel and/or other cooking alternatives alone may not suffice to drive the transition process, but more targeted intervention rooted in the local cultural context in consonance with social and cultural norms or community level factors could be more effective for sustained rural energy transition.

Comparative Analysis of Fractal Properties of Solar Faculae

Present study is aimed at investigating the solar faculae area from 1990 to 2007 which partially covered the22nd and 23rd solar cycle. Rescaled Range Analysis (RRA) and Detrended Fluctuation Analysis (DFA) have beenadopted to evaluate the behaviour of nonlinear dynamics of solar faculae area. Results show that the value of Hurstexponent for solar faculae area from RRA and DFA is negatively correlated. It means it is non-persistent and longrange correlated. Obtained result is inaccurate so the only solution is to transform the data into stationary data by takingdifferencing. RRA is applied on residuals and RRA to evaluate the fractal property of the time series. Solar faculae areainvestigated in this study is fractal in nature and predictable ass well. Moreover, the time series of solar faculae area isnon-linear as established by the Brock – Dechert – Scheinkman (BDS) test results

Comparative Analysis of Fractal Properties of Solar Faculae

Present study is aimed at investigating the solar faculae area from 1990 to 2007 which partially covered the 22nd and 23rd solar cycle. Rescaled Range Analysis (RRA) and Detrended Fluctuation Analysis (DFA) have been adopted to evaluate the behaviour of nonlinear dynamics of solar faculae area. Results show that the value of Hurst exponent for solar faculae area from RRA and DFA is negatively correlated. It means it is non-persistent and long-range correlated. Obtained result is inaccurate so the only solution is to transform the data into stationary data by taking differencing. RRA is applied on residuals and RRA to evaluate the fractal property of the time series. Solar faculae area investigated in this study is fractal in nature and predictable ass well. Moreover, the time series of solar faculae area is non-linear as established by the Brock – Dechert – Scheinkman (BDS) test results.

Assessing the complex dynamics of entrepreneurial ecosystems: A nonstationary approach

The notion of entrepreneurial ecosystems (EEs) has received growing interest from scientists, practitioners and policy-makers over the past decade. Whereas previous research has predominantly focused on identifying the main components and attributes of different ecosystems, the understanding of how EEs evolve over time is still limited. In this study, we build on recent conceptualizations of EEs as complex adaptive systems and apply three methods from chaos theory, the Pointwise D2 (PD2), the Brock-Dechert-Scheinkman (BDS) test and Local Largest Lyapunov Exponents (LLLEs), to study the nonlinear dynamics of EEs. To illustrate our ideas, we analyze the development of the Singapore entrepreneurial ecosystem (SEE) from 1970 to 2018, using time series data on the monthly creation of new ventures. Our results suggest that the evolution of an EE can be considered as a nonlinear chaotic process that changes over time. Implications for theory and practice, as well as limitations and future research directions, are discussed.

Nexus between Economic Volatility, Trade Openness and FDI: An Application of ARDL, NARDL and Asymmetric Causality

The motivation behind this study is to examine whether regional economic volatility and trade openness has influenced the pattern of foreign direct investment (FDI) inflows in select South Asian countries during the period 1975–2019. In order to do so, we applied several nonlinear tests, including the unit root test, ordinary least squares (OLS) test, autoregressive distributed lag (NARDL) test, and causality test. The findings of the nonlinear unit root test suggested that the variables are stationary at the first deviation, following nonlinear systems. Furthermore, the presence of nonlinearity in empirical estimations has been proven through nonlinear OLS and Brock Dechert Scheinkman (BDS) tests. Referring to the results of the Wald test with NARDL, a long-term asymmetrical relationship between the research variables has been confirmed, and long-term and short-term asymmetry was also observed in all tested empirical models. Furthermore, the results of the study on directional causality and asymmetric assumption support the feedback hypothesis in explaining the directional causality between regional economic volatility, trade openness, and inflows of FDI.

Study on the nonlinear and chaotic behavior of exchange traded funds listed in Hong Kong Stock Exchanges

This study examines the nonlinearity and chaotic behavior of the time series of returns of two exchange traded funds (ETFs) listed in Hong Kong Stock Exchanges, namely Hong Kong Tracker Fund (HKTF) and iShares FTSE A50 (ISFT), and the adequacy of autoregressive-generalized autoregressive conditional heteroskedasticity (AR-GARCH) models to capture nonlinearity. A set of nonlinearity tests consistently indicates the presence of nonlinearity in both return time series and the Brock–Dechert–Scheinkman (BDS) test of nonlinearity on AR-GARCH residuals, and the inability of AR-GARCH models to capture the nonlinearity in the return series at different stages of the model-building process. Testing for chaos is a rather delicate part in this study and is done by estimating the correlation dimension for both ETFs’ return series. The correlation dimension saturates at a finite value, and the saturation indicates the presence of chaos in two ETFs considered for this study.

Nonlinear unit root and nonlinear causality in natural gas - economic growth nexus: Evidence from Nigeria

This paper carries out an econometric examination of nonlinear unit root and nonlinear causality in the natural gas-economic growth nexus in Nigeria. The techniques employed include the Kapetanios-Shin-Shell (KSS) nonlinear unit root test, Kruse nonlinear unit root test, Brock-Dechert-Scheinkman (BDS) nonlinearity test, Nonlinear Ordinary Least Squares (NOLS) model, and, the Hatemi-J asymmetric causality test. The results revealed that natural gas consumption and economic growth series follow nonlinear trend process as indicated by the KSS and Kruse tests, and that the relationship between natural gas consumption and economic growth in Nigeria is nonlinear as confirmed by the BDS and NOLS tests, where an increase in natural gas consumption increases economic growth. The asymmetric causality test revealed evidence of bidirectional causality between the positive impact of natural gas consumption and economic growth, and, unidirectional causality running from economic growth to negative impact of natural gas consumption. For further research, the paper suggests extending the discussions on the trend process of energy consumption to the Organization of Petroleum Exporting Countries (OPEC) or to other regional energy organizations using panel data techniques.

On Complex Network Construction of Rain Gauge Stations Considering Nonlinearity of Observed Daily Rainfall Data

Rainfall data is frequently used as input and analysis data in the field of hydrology. To obtain adequate rainfall data, there should be a rain gauge network that can cover the relevant region. Therefore, it is necessary to analyze and evaluate the adequacy of rain gauge networks. Currently, a complex network analysis is frequently used in network analysis and in the hydrology field, Pearson correlation is used as strength of link in constructing networks. However, Pearson correlation is used for analyzing the linear relationship of data. Therefore, it is now suitable for nonlinear hydrological data (such as rainfall and runoff). Thus, a possible solution to this problem is to apply mutual information that can consider nonlinearity of data. The present study used a method of statistical analysis known as the Brock–Dechert–Scheinkman (BDS) statistics to test the nonlinearity of rainfall data from 55 Automated Synoptic Observing System (ASOS) rain gauge stations in South Korea. Analysis results indicated that all rain gauge stations showed nonlinearity in the data. Complex networks of these rain gauge stations were constructed by applying Pearson correlation and mutual information. Then, they were compared by computing their centrality values. Comparing the centrality rankings according to different thresholds for correlation showed that the network based on mutual information yielded consistent results in the rankings, whereas the network, which based on Pearson correlation exhibited much variability in the results. Thus, it was found that using mutual information is appropriate when constructing a complex network utilizing rainfall data with nonlinear characteristics.

Short-term wind speed forecasting based on autoregressive moving average with echo state network compensation

In order to improve the forecasting accuracy of short-term wind speed, a forecasting method based on autoregressive moving average with echo state network compensation is proposed in this article. First, the linear and nonlinear characteristics of short-term wind speed can be determined by Brock–Dechert–Scheinkman statistics method. Then, autoregressive moving average model is used for modeling and to forecast the linear component of short-term wind speed. The linear component of short-term wind speed sequence is obtained. Artificial bee colony algorithm–optimized echo state network model is used as the forecasting model of forecasting error sequences with the nonlinear characteristic. Finally, the final forecasting value is obtained by adding forecasting values of autoregressive moving average model and forecasting error values of echo state network model. k-fold cross-validation is used to improve the generalization ability of the forecasting model. The simulation comparison results show that the proposed forecasting method has higher prediction accuracy with the smaller prediction error. The forecasting indicators are also better than other forecasting methods.

Seeking a Chaotic Order in the Cryptocurrency Market

In this study, we investigate the existence of chaos in the global cryptocurrency market. Specifically, we analyze parameters of chaotic order, nonlinearity, sensitivity to the initial conditions, monofractality, and multifractality. For this purpose, we conduct a comprehensive series of tests, including Brock–Dechert–Scheinkman (BDS) test, largest Lyapunov exponent, box-counting, and monogram analysis for fractal dimension, and multiple tests for long-range dependence (Aggregated Variances, Peng, Higuchi, R/S Analysis, and Multifractal Detrended Fluctuation Analysis (MFDFA)). All tests are performed over a variety of major cryptocurrencies: Bitcoin, Litecoin, Ethereum, and Ripple. The empirical results support the existence of chaos in the cryptocurrency market. Accordingly, cryptocurrency returns are not random and follow a chaotic order. Therefore, long term predictions are not possible, contrary to most of the discussions ongoing in the media and the public.

A new hybrid model for nonlinear and non-stationary runoff prediction at annual and monthly time scales

Due to the effects of frequent anthropogenic activities and climate change, the natural annual runoff series presents typical nonlinear, non-stationary and multiple-scale characteristics, which triggers the common problem of low accuracy for long-term runoff predictions. Therefore, the main goal of this study was to improve the long-term prediction accuracy for the nonlinear and non-stationary runoff series by introducing a new hybrid approach based on improved ensemble intrinsic time-scale decomposition (IEITD) and improved nearest neighbor bootstrapping regressive (INNBR) methods. First, the Brock-Dechert-Scheinkman (BDS) test and Augmented Dickey-Fuller (ADF) test were used to identify the nonlinear and non-stationary characteristics of annual runoff series, respectively, and the Modified Mann–Kendall (MM-K) test and Mann–Kendall (M-K) abrupt change test methods were applied to explore the drivers of non-stationarity. On this basis, the IEITD was used to decompose the original annual runoff series into several proper rotation components (PRCs) and a monotonic residual trend term to make the non-stationary runoff time series stationary. Then, the INNBR model was applied to predict the respective PRCs. The residual series was predicted by the polynomial fitting method. Finally, the predictive results of each PRC and residual series were summed to obtain an ensemble forecast for the runoff series. The performance of the new hybrid approach was tested by the annual (nine hydrological stations) and monthly (three hydrological stations) runoff data covering 1956–2011 in the Luanhe River basin in China. Results suggested: (1) the annual runoff time series of nine hydrological stations presented obviously dependent nonlinear structure; (2) the annual runoff series of nine hydrological stations were all non-stationary; (3) human activity, rather than change in precipitation, was the major driving factor of runoff decline in the Luanhe River basin; (4) For the performance evaluation criteria of Nash-Sutcliffe efficiency coefficient (NSEC), compared with Nearest Neighbor Bootstrapping Regressive (NNBR) and INNBR, the precision of IEITD-INNBR model almost increase by 227% and 37% on the average of nine hydrological stations; (5) the new hybrid approach of combining the IEITD and INNBR models outperformed the other two models (NNBR and INNBR) tested, and it is capable of capturing the nonlinear, non-stationary and multiple-scale characteristics of complex runoff time series and obtaining higher predictive precision.

Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches

Human intestinal absorption (HIA) of the drugs administered through the oral route constitutes an important criterion for the candidate molecules. The computational approach for predicting the HIA of molecules may potentiate the screening of new drugs. In this study, ensemble learning (EL) based qualitative and quantitative structure–activity relationship (SAR) models (gradient boosted tree, GBT and bagged decision tree, BDT) have been established for the binary classification and HIA prediction of the chemicals, using the selected molecular descriptors. The structural diversity of the chemicals and the nonlinear structure in the considered data were tested by the similarity index and Brock–Dechert–Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in the literature. All the statistical criteria parameters derived for the performance of the constructed SAR models were above their respective thresholds suggesting for their robustness for future applications. In complete data, the qualitative SAR models rendered classification accuracy of >99%, while the quantitative SAR models yielded correlation (R2) of >0.91 between the measured and predicted HIA values. The performances of the EL-based SAR models were also compared with the linear models (linear discriminant analysis, LDA and multiple linear regression, MLR). The GBT and BDT SAR models performed better than the LDA and MLR methods. A comparison of our models with the previously reported QSARs for HIA prediction suggested for their better performance. The results suggest for the appropriateness of the developed SAR models to reliably predict the HIA of structurally diverse chemicals and can serve as useful tools for the initial screening of the molecules in the drug development process.

Predicting toxicities of ionic liquids in multiple test species – an aid in designing green chemicals

Ionic liquids (ILs) due to their unique characteristics have attained much importance for future applications, although they may pose environmental risks to aquatic ecosystems that have to be assessed. This paper presents a novel computational approach for estimating the toxicity of ILs in multiple test species of different trophic levels in accordance with the OECD guidelines. Here, ensemble learning based global structure–activity relationship (SAR) models were established for qualitative (two- and four-category) and quantitative toxicity predictions of ILs in Vibrio fischeri and successfully applied to the algae and daphnia species. Diversity and nonlinearity of the considered datasets were evaluated using the Tanimoto similarity index, Kruskal–Wallis and Brock–Dechert–Scheinkman statistics. The gradient boosted tree (GBT) and bagged decision tree (BDT) SAR models were constructed using simple descriptors and validated by stringent statistical tests. In V. fischeri, algae and daphnia data, the classification accuracies rendered by GBT and BDT models were 84.44–100% (two-category) and 92.23–98.74% (four-category), while the two models yielded correlations (R2) of 0.857–0.982 between the measured and predicted toxicity values with mean squared errors of 0.21–0.04. The SARs also identified structural elements of ILs responsible for their toxicities. The successful results obtained in three test species of different trophic levels reveal that the proposed approach can be useful as a screening tool to easily aid, from the early stages of the processes, the design of aquatic environmentally friendly ILs.

In silico prediction of cellular permeability of diverse chemicals using qualitative and quantitative SAR modeling approaches

The permeability of the molecules through the cultured Caco-2 cells is an established in vitro method for the assessment of the absorption of oral drugs. The computational approach for predicting the cellular permeability of molecules may potentiate the screening of new drugs. In this study, gradient boosted tree (GBT) approach based qualitative and quantitative structure–activity relationship (SAR) models have been established for binary classification (moderate–poor and highly permeable) and permeability prediction of molecules using the Caco-2 cell dataset. The structural diversity of the chemicals and nonlinear structure in the considered data were tested by the similarity index and Brock–Dechert–Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in QSAR literature. In complete data, the qualitative SAR model rendered classification accuracy of 99.26%, while the quantitative SAR model yielded a correlation (R2) of 0.917 between the measured and predicted permeability values with the mean squared error (MSE) of 0.08. The results suggest for the appropriateness of the developed SAR models to reliably predict the cellular permeability of diverse chemicals in Caco-2 cells and can be useful tools for initial screening of molecules in the drug development process.

A generalized conditional heteroscedastic model for temperature downscaling

This study describes a method for deriving the time varying second order moment, or heteroscedasticity, of local daily temperature and its association to large Coupled Canadian General Circulation Models predictors. This is carried out by applying a multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) approach to construct the conditional variance–covariance structure between General Circulation Models (GCMs) predictors and maximum and minimum temperature time series during 1980–2000. Two MGARCH specifications namely diagonal VECH and dynamic conditional correlation (DCC) are applied and 25 GCM predictors were selected for a bivariate temperature heteroscedastic modeling. It is observed that the conditional covariance between predictors and temperature is not very strong and mostly depends on the interaction between the random process governing temporal variation of predictors and predictants. The DCC model reveals a time varying conditional correlation between GCM predictors and temperature time series. No remarkable increasing or decreasing change is observed for correlation coefficients between GCM predictors and observed temperature during 1980–2000 while weak winter–summer seasonality is clear for both conditional covariance and correlation. Furthermore, the stationarity and nonlinearity Kwiatkowski–Phillips–Schmidt–Shin (KPSS) and Brock–Dechert–Scheinkman (BDS) tests showed that GCM predictors, temperature and their conditional correlation time series are nonlinear but stationary during 1980–2000 according to BDS and KPSS test results. However, the degree of nonlinearity of temperature time series is higher than most of the GCM predictors.

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes.