Time Series Econometric Models Research Articles

How price and non-price factors influence the market price of major carp: Findings from the dynamic ARDL approach

Recently, fish prices in Bangladesh have been growing, particularly the market price of major carp, even though the factors that may be contributing to this trend are unexplored. Therefore, this study considers some price and non-price factors to explore the market price dynamics of major carp species in Bangladesh. To explore the factors impacting the market price of carp fish, we apply advanced time-series econometric modeling, such as the recently devised dynamic simulated autoregressive distributed lag (ARDL) model using publicly available data on fish prices from 2005 M1 to 2021 M12. This period is attributed to the rapid transformation from mostly low-intensity farming to commercialization due to a rise in productivity and market price. Among the price factors, the findings demonstrate that the price of corn, soybean, and oilcake, and fisheries wage rate have a positive significant impact on fish price in both the short and long term. In addition, among the non-price drivers, GDP per capita, inflation rate, and fish consumption all have a significant positive influence on fish prices in the long run, whereas total production has a detrimental effect on fish prices in the long run. These results have significant policy ramifications for attaining the fish farmers' profit maximization and consumers' welfare maximization goals.

Machine learning to predict grains futures prices

AbstractAccurate commodity price forecasts are crucial for stakeholders in agricultural supply chains. They support informed marketing decisions, risk management, and investment strategies. Machine learning methods have significant potential to provide accurate forecasts by maximizing out‐of‐sample accuracy. However, their inherent complexity makes it challenging to understand the appropriate data pre‐processing steps to ensure proper functionality. This study compares the forecasting performance of Long Short‐Term Memory Recurrent Neural Networks (LSTM‐RNNs) with classical econometric time series models for corn futures prices. The study considers various combinations of data pre‐processing techniques, variable clusters, and forecast horizons. Our results indicate that LSTM‐RNNs consistently outperform classical methods, particularly for longer forecast horizons. In particular, our findings demonstrate that LSTM‐RNNs are capable of automatically handling structural breaks, resulting in more accurate forecasts when trained on datasets that include such shocks. However, in our setting, LSTM‐RNNs struggle to deal with seasonality and trend components, necessitating specific data pre‐processing procedures for their removal.

Examining economic policy uncertainty's impact on environmental sustainability: Insights from nordic nations

The inherent unpredictability of economic policies can create a challenging environment for businesses and government. Such uncertainties may, in turn, have repercussions on environmental quality efforts. Accordingly, this study thoroughly explores the intricate relationships among several vital variables: economic policy uncertainty (EPU), economic growth, natural resource rent (NRR), renewable energy consumption (REC), and environmental quality. The geographical focus of this investigation is four Nordic economies (Sweden, Finland, Norway, and Denmark). The research method is firmly anchored in the STIRPAT framework, with a notable innovative twist. The CS-ARDL simulations estimate the long-term interaction between the series. One key advantage of the estimation model is the ability and robustness to handle reverse causality and endogeneity concerns in time-series econometric models. The empirical findings from this research demonstrate that EPU, economic growth, and NRR are primary indicators of increased CO2 emissions. These factors collectively contribute to the escalating trend of carbon emissions within the Nordic nations. In stark contrast, clean energy consumption emerges as a promising factor that not only counters the rising emissions trend but actively contributes to environmental quality. The importance of transitioning towards cleaner energy sources becomes evident in light of these findings. Policy-makers should recognize that economic policy uncertainty can be a potent driver of environmental pollution and should prioritize formulating transparent and consistent policies that attract investments in clean energy solutions.

The Relationship of Economics and HRM practices: Evidence from Pakistan

This research paper analyses the survey of different contributions in the fields of economics and industrial HRM decisions by taking into account the performance of firms, HRM, advertising expenditures, and areas for the development of organizations or human resources. This study gives a review of existing accounts of literature and the evolution of different firms that reveal the re-examination of early years’ time. This is a serious gap that is not demonstrated by the researchers. It has also been done in different industries for two decades for field existence in influential and academic writers for the rank of economics and economics-based rank and review. This study was mainly done from 2000 to 2022 for the economy of Pakistan. The organizational structure is reviewed, and this paper ends with a brief review of recent developments that have occurred in these decades on the basis of time series econometric modelling.

Retracted: Research on the Correlation between Information and Communication Technology Development and Consumer Spending Based on Artificial Intelligence and Time Series Econometric Model

Retracted: Research on the Correlation between Information and Communication Technology Development and Consumer Spending Based on Artificial Intelligence and Time Series Econometric Model

Nexus between economic factors and FDI equity inflows: a causality analysis in ARDL approach

Purpose Economic literature highlights both positive and negative impact of FDI on economic growth. The purpose of this study is to confirm the relationship between various economic factors and FDI equity inflows and find out deviations, if any. This is investigated using standard time-series econometric models. The long and short run relationship is inquired with respect to market size, inflation rate, level of infrastructure, domestic investment and openness to trade. The choice of variables for Indian economy is purely based on empirical observations obtained from scientific literature review. Design/methodology/approach The study involves application of autoregressive distributive lag (ARDL) model to investigate the relationship. The long run co-integration between FDI and economic growth is tested by Pesaran ARDL model. The stationarity of data is tested by augmented Dickey Fuller test and Phillip–Perron unit root test. Error correction model is applied to study the short run relationship using Johansen’s vector error correction model method besides other tests. Findings The results show that the domestic investment, inflation rate, level of infrastructure and trade openness influence inward FDI flows. These factors have both long and short-term relationship with FDI inflows. However, market size is insignificant in influencing the foreign investments inflows. There lies an inverse relation between FDI and inflation rate. Originality/value To the best of the authors’ knowledge, the study is original. The methodology and interpretation of results are distinct and different from other similar studies.

Bank crisis resolution in India

PurposeThe academic literature has examined why bank runs happen based on the work of 2022 Nobel Prize-winning economists Diamond and Dybvig. They have found the source of banking/financial crisis in terms of mismatch between liabilities (deposits being short term and savers wanting to short-term access to their money) and assets (long term and illiquid). The Lakshmi Vilas Bank (LVB) crisis intensified when it came under Prompt Corrective Action (PCA) of the Reserve Bank of India (RBI). This situation provides the opportunity to study whether the elements embodied in the theoretical models like Diamond and Dybvig hold true for LVB crisis. This study aims to examine the reasons for the demise of LVB in India using DuPont financial model, peer group analysis and time series structural break in crucial financial parameters.Design/methodology/approachThe study examines the reason for insolvency of LVB using financial ratios, financial models (DuPont), financial distress model (Z-score) and asset-liability management. The study also adopts univariate structural break models using quarterly financial data covering the key financial measures used in the RBI’s PCA framework.FindingsLVB crisis is like Diamond–Dybvig model, in the sense, savers requiring short-term access to their money (liquidity for their deposits) on the information of high non-performing assets, which further deteriorates the illiquid nature of loan portfolio (assets) of banks. The study finds its profit margin (net interest margin and non-interest margin) and managerial efficiency had started deteriorating since 2018. The study finds that LVB’s main weakness lies in its limited credit appraisal ability, its monitoring and weak internal controls. Lending to sensitive sectors (like real estate, capital markets and commodities) and exposure to large business groups also contributed to its weakness. The study also finds huge, elevated asset-liability mismatch, especially in the short-term maturity buckets. Using univariate econometric time series model, the study also confirms financial weakness being evident much earlier than the time when resolution was undertaken by the RBI through PCA.Research limitations/implicationsThe study has implications for analysing and monitoring financial distress of banks. The study also has implications for devising banking regulation and supervision.Originality/valueThe study brings in a perspective of the banking regulations using the application of PCA framework on a listed private sector bank. The authors combine an accounting ratio model and combine risk measures that could identify the incipient risks in a bank. The authors believe this will help in refinement of banking regulations and better monitoring mechanisms.

Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models

Accurately modeling and forecasting electricity consumption remains a challenging task due to the large number of the statistical properties that characterize this time series such as seasonality, trend, sudden changes, slow decay of autocorrelation function, among many others. This study contributes to this literature by using and comparing four advanced time series econometrics models, and four machine learning and deep learning models11These models include the autoregressive model with seasonality, autoregressive models with exogenous variables, the autoregressive fractionally integrated moving average model with exogenous variables, the three state autoregressive Markov switching model with exogenous variable, Prophet, EXtreme Gradient Boosting, Long-Short-Term Memory and Support Vector Regression. to analyze and forecast electricity consumption during COVID-19 pre-lockdown, lockdown, releasing-lockdown, and post-lockdown phases. Monthly data on Qatar’s total electricity consumption has been used from January 2010 to December 2021. The empirical findings demonstrate that both econometric and machine learning models are able to capture most of the important statistical features characterizing electricity consumption. In particular, it is found that climate change based factors, e.g temperature, rainfall, mean sea-level pressure and wind speed, are key determinants of electricity consumption. In terms of forecasting, the results indicate that the autoregressive fractionally integrated moving average and the three state autoregressive Markov switching models with exogenous variables outperform all other models. Policy implications and energy-environmental recommendations are proposed and discussed.

Wholesale Food Price Index Forecasts with the Neural Network

Food price forecasts in the agricultural sector have always been a vital matter to a wide variety of market participants. In this work, we approach this forecast problem for the weekly wholesale food price index in the Chinese market during a 10-year period of January 1, 2010–January 3, 2020. To facilitate the analysis, we propose the use of the nonlinear auto-regressive neural network. Technically, we investigate forecast performance, based upon the relative root mean square error (RRMSE) as the evaluation metrics, corresponding to one hundred and twenty settings that cover different algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data. Our experimental result suggests the construction of the neural network with three delays and 10 hidden neurons, which is trained through the Levenberg–Marquardt algorithm, as the forecast model. It leads to high accuracy and stabilities with the RRMSEs of 1.93% for the training phase, 2.16% for the validation phase, and 1.95% for the testing phase. Comparisons of forecast accuracy between the proposed model and some other machine learning models, as well as traditional time-series econometric models, suggest that our proposed model leads to statistically significant better performance. Our results could benefit different forecast users, such as policymakers and various market participants, in policy analysis and market assessments.

Estimation performance comparison of machine learning approaches and time series econometric models: evidence from the effect of sector-based energy consumption on CO2 emissions in the USA.

By considering the existence of two separate analysis families and the usage of different data frequencies, this study aims to examine the effect of method choice, data frequency, and sector-based energy consumption on carbon dioxide (CO2) emissions by performing machine learning (ML) algorithms and time series econometric (TS) models simultaneously. In this situation, the study examines the United States (USA), considers sector-based energy consumption indicators as explanatory variables, uses monthly and yearly data between January 1973 and December 2021, estimates CO2 emissions, and compares the estimation performance of the models. The empirical findings reveal that (i) the ML algorithms outperform the TS models based on R2 and goodness of fit criteria; (ii) the estimation performance of the models increases with the high-frequency (i.e., monthly) data; (iii) the ML algorithms perform much better in case of high-frequency usage; (iv) some thresholds identify the effects of the sector-based energy consumption indicators on the CO2 emissions; (v) electric power and transportation sectors are the most important sectors in the estimation of the CO2 emissions for monthly and yearly data, respectively. Hence, the study provides to help the understanding role of method choice, data frequency, and sector-based energy consumption for the estimation of CO2 emissions. Based on the results, this study proposes that US policymakers should consider the ML algorithms, use higher-frequency data, and include sector-based energy consumption indicators to have a better estimation of CO2 emissions.

Estimation of Impacts of Global Factors on World Food Prices: A Comparison of Machine Learning Algorithms and Time Series Econometric Models.

It is a well-felt recent phenomenal fact that global food prices have dramatically increased and attracted attention from practitioners and researchers. In line with this attraction, this study uncovers the impact of global factors on predicting food prices in an empirical comparison by using machine learning algorithms and time series econometric models. Covering eight global explanatory variables and monthly data from January 1991 to May 2021, the results show that machine learning algorithms reveal a better performance than time series econometric models while Multi-layer Perceptron is defined as the best machine learning algorithm among alternatives. Furthermore, the one-month lagged global food prices are found to be the most significant factor on the global food prices followed by raw material prices, fertilizer prices, and oil prices, respectively. Thus, the results highlight the effects of fluctuations in the global variables on global food prices. Additionally, policy implications are discussed.

Econometric Analysis of Effects of Savings and Investments on Economic Growth in India

This paper examined the dynamic causal relationship between savings, investments and economic growth in India from 1990 to 2021, taking into account the influence of post-economic reforms on economic growth by employing time series econometric models. The results indicated that all the variables were stationary at their first difference. The VECM result confirmed that there was a long-run relationship among the variables. The decomposition results suggested that the Indian economic growth variation was explained mainly by domestic savings. The findings of the study would be useful for policymakers and governments to take appropriate development strategies and effective policies.

FinTech market development and financial inclusion in Ghana: The role of heterogeneous actors

Efforts to include many financially in Ghana continue to witness substantial progress as the nuptial between information communication technology and finance thrives. However, except for information communication technology infrastructure, the role of other actors in the development of the financial technology market and financial inclusion in Ghana is unexplored. Therefore, the study explores how other heterogeneous actors contribute to the development of the financial technology market and financial inclusion in Ghana through information technology infrastructure. Specifically, first, we identify how key actors emerged in the financial technology market of Ghana and how these actors ride on information technology infrastructure to develop FinTech and improve financial inclusion. Second, we estimate the relationship between financial inclusion and these actors using time series econometric modeling. The government, mobile phones, telcos, mobile money agents, mobile money customers, and traditional banks are identified as key actors in the market. Further, the time series model shows a significant positive relationship between the heterogeneous actors' financial inclusion in Ghana. Therefore, these actors become significant to the existence, furtherance, and sustainability of the FinTech market and financial inclusion in Ghana.

Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models

The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine learning algorithms present higher prediction performance than time series models for both periods, (ii) renewable sources are the most influential factor for the electricity prices, and (iii) the COVID-19 pandemic caused a change in the importance order of influential factors on the electricity prices. Thus, the empirical results highlight the consideration of machine learning algorithms in electricity price prediction. Based on the empirical results obtained, potential policy implications are also discussed.

Fish price volatility dynamics in Bangladesh

Price volatility is an important factor in the fish value chain influencing the costs, trade, income, and food security. Therefore, analysis of fish price volatility dynamics can provide valuable insights into the impact of aquaculture growth in Bangladesh, a developing country where much of the animal source protein is obtained through fish consumption. In this article, we use both descriptive analysis and time-series econometric modeling to understand the price volatility dynamics of four major aquaculture products at retail level, as well as the efficiency of the markets to adjust to price shocks. We analyze publicly available retail fish prices observations from January 2007 to December 2019. This period is characterized by a rapid growth in aquaculture output as the inland aquaculture sector of Bangladesh shifted away from largely low-intensity farming toward commercialization. Our results indicate Bangladesh is experiencing lower fish price volatility during recent years. Therefore, we argue that rapid growth in aquaculture output in Bangladesh has contributed to lower volatility of fish prices. The article also sheds light on the volatility persistence and finds almost all the markets are inefficiently adjusting to price shock, indicating that there is more room for improving the functioning of fish markets in Bangladesh.

The Impact of Tourism Development and Economic Growth on Poverty Reduction in Kazakhstan

Abstract The paper examines the long-run relationship between poverty reduction, economic growth, and tourism development in Kazakhstan during the period of 2001–2017. We expand the basic model by including other poverty determinants such as inequality, unemployment, and spending on health. We use the Autoregressive Distributed Lag (ARDL) approach to test the co-integration of variables, as the ARDL bound test of co-integration is less restrictive and provides more reliable coefficients than other time series econometric models. The ARDL bound test results show that there exists a long-run relationship between the said variables. The coefficients of all variables have the expected signs in the long run.

Impact of of Government's Human Capital Expenditure on Economic Growth of Nepal: An ARDL Approach

Background: Quality health services, education and training, play a pivotal role in expanding the productivity and income of the people. The government's expenditure on education, health and social sector is increasing rapidly in developing countries like Nepal. The increasing public spending has helped achieve the MDGs targets and is helping to meet the SDGs targets.
 Objectives: This study is engrossed in measuring the impact of the government’s expenditure on education, health and social sector on the economic growth of Nepal.
 Methods: The fundamental Cobb-Douglas production function is applied to measure the economic growth of the Nepalese economy. The unit root and stationarity of variables are checked through ADF, PP and KPSS. The time series econometric model of ARDL is used to explore the short-run and long-run relationship of the variables. The validity of the ARDL model is also examined to verify the authenticity of the econometric outcome.
 Results: The study has anticipated that the government's education expenditure is inversely related to real GDP. In contrast, the health expenditure has shown a negative impact in the same year but a positive one in the lagged year. Similarly, the government's social expenditure and gross capital formation have a negative effect on the same year, whereas they exhibited a significant positive impact in the lagged year. Further, In the long run, the government's education and health expenditures negatively impact (-0.7233 and -0.0055), respectively in, growth. Similarly, the government's social sector expenditure (0.20189) and gross capital formation (0.14610) positively impact Nepal's economic growth.
 Conclusion: The econometric results of this study explain that the government's expenditure on the education and health sector has a negative impact on economic growth, and the social sector and gross capital formation have a positive impact on the economic development of Nepal. The investment in such sectors by the public sector of the private sector is significant to increase the labour force's productivity and enhance the livelihood of the poor people. Further, it reveals that the human capital investment is a continuous process and takes a generation or more to expose the outcome in the real sense.

Research on the Correlation between Information and Communication Technology Development and Consumer Spending Based on Artificial Intelligence and Time Series Econometric Model

In order to explore the correlation between ICT development and consumer spending, this paper uses artificial intelligence and time series econometric models to study the correlation between ICT development and consumer spending. Moreover, this paper organically combines the advantages of wavelet analysis and hidden Markov model to construct a wavelet domain hidden Markov chain model. It is used to examine the flow of information on different scales related to the development of communication technology and consumer spending, so as to infer the potential mechanism of the interaction of different traders’ behaviors from the other side. Through cluster analysis, it can be seen that the correlation analysis method of information and communication technology development and consumption expenditure based on artificial intelligence and time series econometric model proposed in this paper has certain reliability. At the same time, there is a strong correlation between the development of communication technology and consumer spending.

Using econometric models to make management decisions at oil and gas enterprises

Subject. At present, there is a need to increase the flexibility of exporting companies and gas traders to ensure the stability and continuity of supplies in the European gas market. The specialized literature does not fully present scientifically based methods and algorithms, according to which a management decision can be made on the problem. Objectives. Our aim is to develop a methodology enabling to qualitatively and quantitatively substantiate the feasibility of building a certain portfolio of capacities, and to evaluate the effects of its implementation. Methods. The study draws on general scientific research methods. Results. We developed and tested a model that allows modeling and forecasting of high-frequency time series, using VARMA and ARIMA econometric models, to make accurate and reasonable short- and medium-term decisions, and to generate a growth strategy for enterprises operating in the oil and gas industry. Conclusions. Our findings correspond to the qualitative and quantitative assessment of the International Energy Agency, thus confirming that the model is relevant for use in the oil and gas industry. The findings can be applied in educational process in the field of ‘macroeconomics’, ‘econometrics’, in the implementation of operational and strategic planning at enterprises. They also can be useful for research and development, including testing at enterprises of other sectors and industries.

Determining the Impact of Economic Growth, Carbon Emissions, Foreign Direct Investments, and Trade Openness on Energy Consumption in the Philippines

The Philippine energy sector is currently facing the problem of rising energy demand and the dominance of coal and natural gas in the energy mix. The current objective of the Philippine energy sector is to satisfy energy demand while maintaining its goal of reducing environmental effects. The paper explores the dynamic relationship between energy consumption in per capita terms and selected variables, namely, aggregate output, carbon emissions, foreign direct investment, and trade openness in the Philippines. The data spans the period from 1981 to 2017. The paper utilizes a multivariate framework based on the theoretical premises revolving around the energy-growth nexus. Time-series econometric modeling based on the OLS regression analysis is employed for this purpose. The results of the Johansen cointegration test confirm the presence of cointegrating relationships and finds a strong long-run relationship among the variables. The regression analysis results found that economic growth and carbon emission are significantly correlated with energy consumption. The paper finds that energy consumption is negatively correlated with economic growth and positively correlated with carbon emissions. The results suggest that policymakers can enforce energy conservation policies without hampering the economy too much. The results highlight the need for pollution-abatement policies and technologies in order to minimize the effect of the energy sector on the environment. Therefore, the share of renewable energy sources in the energy mix should increase.