Time Series Analysis Techniques Research Articles

Measuring financial reporting quality of non-financial firms listed in Amman Stock Exchange

accuracy and transparency is vital for internal and external users, this study insures that financial reporting quality and financial information published for non-financial firms in ASE is not manubulated, accurate, combitable, timely published, adequate, and free of earning management, to be usefull for decision making for all users, especially for new investors. Methodology- The period 2010 – 2019 forms the data for the statistical analysis. In addition, and to measure the FRQ, the paper uses time series analysis techniques, including descriptive analysis, frequencies and multiple OLS regression were used. Using data from 910 firm-years observation of companies listed on Amman Stock Exchange for the period of 2010 to 2019. Findings- The analysis reveals that The probability for F-Statistics < 0.05, this led to accept the statistical model. on the other hand, R square is 31.3 % which means that the explanatory power of modified Jones model (Dechow et al., 1995) reach 31.3% in explain the practices of earnings management in Jordanian companies. on the other hand, the VIF values are lower than 10 and tolerances higher than 10% which mean there is no multicollinearity problem in the model. as for the 1/A_(i,t-1) shows a significant and negative relationship with total accruals, were 〖∆(Sales-AR)〗_it/A_(i,t-1) and 〖PPE〗_it/A_(i,t-1) 1/A_(i,t-1) shows a significant and positive relationship with total accruals. Conclusion- the highest number of companies practicing earnings management in the year (2012 and 2014) was 60%, while the lowest number of companies that practice earnings management in the year (2015) and it was 32%, on the other hand, the highest number of companies do not practicing earnings management in the year (2015) was 68%, while the lowest number of companies that do not practice earnings management in the year (2012 and 2014) and it was 40%. Keywords: FRQ, modified jones model, Amman Stock Exchange, non-financial firms JEL Codes: G10, G12

Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review.

This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.

Identifying Energy Inefficiencies Using Self-Organizing Maps: Case of A Highly Efficient Certified Office Building

Living and working in comfort while a building’s energy consumption is kept under control requires monitoring a system’s consumption to optimize the energy performance. The way energy is generally used is often far from optimal, which requires the use of smart meters that can record the energy consumption and communicate the information to an energy manager who can analyze the consumption behavior, monitor, and optimize energy performance. Given that the heating, ventilation, and air conditioning (HVAC) systems are the largest electricity consumers in buildings, this paper discusses the importance of incorporating occupancy data in the energy efficiency analysis and unveils energy inefficiencies in the way the system operates. This paper uses 1-year data of a highly efficient certified office building located in the Houston area and shows the power of self-organizing maps and data analysis in identifying up to 4.6% possible savings in energy. The use of time series analysis and machine-learning techniques is conducive to helping energy managers discover more energy savings.

Quantum Dynamic Mode Decomposition Algorithm for High-Dimensional Time Series Analysis

The dynamic mode decomposition (DMD) algorithm is a widely used factorization and dimensionality reduction technique in time series analysis. When analyzing high-dimensional time series, the DMD algorithm requires extremely large amounts of computational power. To accelerate the DMD algorithm, we propose a quantum-classical hybrid algorithm that we call the quantum dynamic mode decomposition (QDMD) algorithm. Given a time series X ∈ R n × ( m + 1) with n ≫ m , the QDMD algorithm first executes quantum singular value decomposition on a matrix related to X and obtains a quantum state containing the main singular values and singular vectors of the decomposed matrix, then performs a low-sampling-frequency process on the obtained quantum state and computes the low-dimensional projection of the DMD operator through the sampling results. Finally, the algorithm computes the DMD eigenvalues and prepares the amplitude-encoding states of the DMD modes using the obtained classical information and X . Considering the main variables, the complexity of the QDMD algorithm is O ~ M m polylog n / ϵ , where M = O ~ m 3 / ϵ 2 denotes the number of samples. Compared with the classical DMD algorithm, which has complexity O ~ n m 2 log 1 / ϵ , the QDMD algorithm provides an exponential acceleration of n , at the cost of greater dependence on M and ϵ . We test the effects of M on the QDMD algorithm in the specific application scenarios of data denoising, scene background extraction, and fluid dynamics analysis. We determined that the QDMD algorithm requires only a small number of samples M in specific applications, further demonstrating the quantum advantage of the QDMD algorithm in high-dimensional data analysis.

The influence of sustainable energy demands on energy efficiency: Evidence from China

Recently, there has been a high level of energy demand worldwide, which has piqued regulators and researchers’ interest in producing efficient energy. Therefore, this research investigates the impact of multiple energy demands on China's energy efficiency (renewable energy production). The researchers use secondary data extracted from World Development Indicators for the period 1986 to 2019. They use time series analysis techniques, such as the ADF test for stationarity, the ARDL model to evaluate the association between the variables, and the Granger causality test to evaluate the directional nexus amongst the variables. The findings show that multiple energy demands have a positive association with energy efficiency in China. Several implications and recommendations are made by the study to facilitate future research and regulation.

Predicting traffic congestion based on time series analysis

Traffic congestion is a serious problem in many cities, resulting in lost time, increased air pollution, and reduced quality of life. In the past few years, time series models have been widely used to predict traffic flows and congestion. This study analyzes traffic data collected over several years and develops a predictive model based on time series analysis techniques. The model takes into account various factors that contribute to congestion, such as time of day, day of the week, and junction. The results show that the model effectively predicts traffic congestion with a high degree of accuracy, which can be used to make rational decisions and reduce urban traffic congestion.

Trade liberalization, institutions, and economic growthin Malawi

The study provides an empirical assessment of how institutions and trade liberalization affect Malawi’s economicexpansion. It tackles the absence of empirical research into how institutions affect economic growth and howtrade liberalization policy affects institutions’ influence on growth (interaction effect). The study also seeks tofind out if economic growth, however, affects institutions as theories differ on causality. The study uses a timeseries analysis and autoregressive distribution lag (ARDL) technique to obtain short-run and long-run results. Thestudy was conducted from 1988, the official inception year of trade liberalization in Malawi, to 2014. The empiricalresults show that political and economic institutions, as well as trade liberalization, affect Malawi’s economicgrowth in both the short term and the long term. Trade liberalization and political institutions negatively affecteconomic growth in the short run and long run, whereas economic institutions positively affect economic growthin the short run and long run. The findings also show that when strong economic institutions rather than strongpolitical institutions are present, the effect of trade liberalization on economic development is more prominent(positive). Finally, the study also finds that it is institutions that affect economic growth in Malawi and not the otherway around.

Statistical analysis and forecasting of cotton yield dynamics in Kashkadarya region of Republic of Uzbekistan

There are phenomena that are significant to research because of how they grow and change through time in practically every discipline. One could attempt to direct a process, forecast the future using knowledge of the past, or characterize the distinctive aspects of a series using a finite quantity of information. The techniques used to handle time series are heavily influenced by the techniques created by mathematical statistics for distribution series. The most basic to the most complicated time series analysis techniques exist in statistics today. The article discusses the statistical analysis of a time series, specifically the average yield of cotton in the Kashkadarya region, Uzbekistan, and the Republics, using data from the Central Statistical Office of Uzbekistan from 2001 to 2020. The study involved constructing point and interval estimates for the average cotton yield with a 95% guarantee, identifying different types of trends, and predicting future yields for the region. Through the use of the Durbin-Watson statistical criteria, it was discovered that there is an autocorrelation dependence in the average cotton yield, indicating that the yield for the current year is dependent on yields from past years. The methods used in this study can be applied to further research conducted by students and scientists.

Yapay Sinir Ağları (YSA) ve ARIMA Modelleri ile Türkiye’de Aylık Sıfır km Otomobil Satış Adetlerinin Tahmin Edilmesi

Çalışmada, yapay zekâ temelli tahmin modellerinden Yapay Sinir Ağları (YSA) ve Zaman Serileri Analizi modellerinden Arima ile Türkiye’deki aylık sıfır km otomobil satış adetleri tahmin edilmiştir. Bağımlı değişken aylık sıfır km otomobil satışlarıdır. Bağımsız değişkenler ise aylık otomobil ihracatı (USD), aylık otomobil ithalatı (USD), aylık Amerikan Dolar kuru (TL), aylık Türkiye ihracatı (USD) ve aylık Türkiye ithalatı (USD)’dir. Türkiye İstatistik Kurumu (TÜİK), Türkiye Cumhuriyet Merkez Bankasından (TCMB) elde edilen aylık veriler (Ocak 2002-Aralık 2020, 228 ay-19 yıl) kullanılarak, Ocak 2021 ile Mart 2022 arasında gerçekleşen 15 aylık sıfır km otomobil satış adetleri tahmin edilmiştir. YSA modelinin performansı Arima modeli ile karşılaştırılmış, tahminlerin farkları ve sonuçlar yorumlanmıştır. Çalışmada Arima’nın YSA’ya kıyasla daha iyi sonuç verdiği tespit edilmiştir.

Covid-19 And Its After-Effects On Macau’s Gaming Industry

This study measured the magnitude of Covid-19’s impact on the casino gaming industry in Macau, analyzed the current Asian gaming market, and provided an overview of the industry outlook in the post-Covid era. Using the Time Series analysis techniques, statistically significant decreases in all sections of the industry have been identified and quantified. In addition, quarterly gaming revenue for the future three years were forecasted. Based on current market conditions and the results of data analyses, this study suggests that: 1) a more diversified tourism and gaming service portfolio is needed; 2) collaborations with surrounding areas need to be established; 3) Macau’s gaming industry will need to explore new markets for future sustainable growth; 4) the rebound of Macau’s gaming industry in the near future will largely depend on China’s COVID policy.

Astronomically forced changes in groundwater reservoirs and sea level during the greenhouse world

<p indent="0mm">Sea level rise caused by global warming is one of the biggest challenges facing humankind. Deepening the understanding of the mechanism of global sea level change during the greenhouse period is the key to meeting this challenge. Large-scale, rapid sea-level changes frequently occurred during the greenhouse period lacking ice sheets. However, conventional sea level change models cannot explain this phenomenon, exposing their shortcomings. Estimation of global pore space in sediments and big data studies have indicated that global sea level changes may also be related to climate-related continental groundwater volume changes. We review the mechanism of sea level change caused by astronomically driven continental groundwater activities and propose a new Sponge Continent hypothesis: astronomical forcing drives climate change, causing filling and discharge of continental aquifers (by analogy, a sponge), which may in turn impact large-scale changes in global sea levels and inland lake level during the greenhouse period. We summarize recent technological breakthroughs related to the reconstruction of deep time sea- and lake-level changes. First, lake level change has been proposed as an indicator for the fluctuation of continental groundwater volume. Second, the sedimentary noise model and SediRate-Fischer method were proposed for the reconstruction of sea- and lake-level changes from sedimentary successions. Among these, the sedimentary noise model is developed based on the principle of the Milankovitch theory of climate change and time series analysis techniques. The noise model contains two complementary methods: dynamic noise after orbital tuning (DYNOT) and lag-1 autocorrelation coefficient (<italic>ρ</italic>₁). The DYNOT method measures the noise intensity in paleoclimate time series, i.e., the ratio of non-Milankovitch variation to total variation. By calculating the lag-1 autocorrelation coefficient of the target paleoclimate time series, the <italic>ρ</italic>₁ model can independently test the noise changes related to sea level variations. The <italic>ρ</italic>₁ value decreases with the increase of noise intensity, and vice versa. The relative intensity of the sedimentary noise is anti-phased with the sea level change. When the sea level increases, the sedimentary noise decreases, and vice versa. This model can also be applied to the studies of lake level changes. Third, cyclo-magnetostratigraphy can provide a global synchronous stratigraphic correlation framework, which is vital for the evaluation of the aquifer eustasy and Sponge Continent hypothesis. The hypothesis of aquifer eustasy and Sponge Continent can be supported by geological evidence from the greenhouse period. This paper reviews recent progress related to astronomically forced sea level changes and/or lake level changes in chronological order. Some of them support the predicted antiphase relationship between the sea level curve and the contemptuous lake level curve as predicted by the Sponge Continent hypothesis. However, many unsolved issues about the nature and mechanisms of sea level change are to be explored. First, estimates of groundwater volume remain in dispute. Second, there is a lack of quantitative indicators for deep time groundwater variations. Third, the timing and mechanism of groundwater changes in continental aquifers are still unconstrained. Potential research directions are also discussed, including groundwater storage estimate, research methods, storage-drainage mechanism of groundwater reservoirs, etc.

Using Dynamic Structural Equation Modeling to Examine Between- and Within-Persons Factor Structure of the DASS-21.

The recent integration of traditional time series analysis and confirmatory factor analysis techniques allows researchers to evaluate the psychometric properties of measurement instruments at between- and within-persons levels while accounting for autoregressive dependencies. The current study applies a dynamic structural equation modeling (SEM) latent factor analysis (i.e., DSEM-CFA) to a sample of 333 individuals who completed the DASS-21 at their regular therapy sessions. The results of the DSEM-CFA illuminate the reliability, invariance, and structural features of each DASS-21 subscale both between and within persons. The results suggest that the DASS-21 reliably measures depression, anxiety, and stress symptoms when evaluating differences between persons, but does not reliably assess within-persons fluctuations in symptoms over time. The results also suggest that currently accepted methods of modeling sensitivity to change within an instrument are likely lacking and the DSEM-CFA provides insight into reliability within and between persons, which is extremely important for instruments used across time.

A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem

Information on crop yield is important for food security, in particular under the conditions of climate change and growing population worldwide. We developed a new fully distributed, high spatial resolution, model of biomass accumulation and crop yield applicable to a highly heterogeneous desert-oasis agroecosystem. The bulk of required input data is obtained by retrieving pixel-wise biogeophysical variables from a suite of very diverse satellite data. Both temperature and water stress conditions at field-scale are given full consideration, while the model was designed to strike a balance between model applicability and satisfactory characterization of the heterogeneous desert-oasis system to estimate field-scale yield. The development of this model relies on three main innovations. First, the start and end of the growing season were estimated for each pixel by calibrating the high spatial and temporal resolution observations of Normalized Difference Vegetation Index (NDVI) by Sentinal-2 (S2) MSI (Multi-Spectral Instrument) against limited local phenological information. Second, to monitor crop water stress, account taken of irrigation, a process-based water and energy balance model was applied to estimate the actual evapotranspiration (ET). This requires knowledge of soil water availability, which is characterized by downscaling the ASCAT (Advanced SCATterrometer) soil moisture data product. To capture the dominant features of the eco-hydrological conditions in the desert and oasis agroecosystem, ET was further downscaled from the 1 km resolution. Third, likewise the water stress indicator, the air temperature stress indicator was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system, by generating time series of air temperature at 1 km spatial resolution using the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) data product. In the temporal dimension, gaps were mitigated by applying time series analysis techniques to reconstruct cloud-free time series of LST, NDVI, fAPAR and albedo. These innovations add up to a high resolution characterization of crop response to the geospatial variability of weather and climate forcing in the desert-oasis agroecosystem. The model was applied to the dominant crops, i.e., spring wheat, maize, sunflower, and melon, in the oases of the Shiyang River Basin (northwestern China) characterized by a rather fragmented land use. The high resolution of pixel-wise ecohydrological parameters, i.e., crop phenology, temperature stress and water stress factors successfully reflect differences of crops with different phenology and location in the oases. The relative errors for wheat and maize yields compared to the census data are less than 5% at district level. At the county level, the relative errors of wheat yields of Liangzhou, Minqin, Gulang, Jinchuan, and Yongchang equal to 0.87%, 24.2%, 9.7%, 12.5%, and 7.2%. For maize, the dominant crop, the error on estimated yields was less than 5%, except in Gulang. The relative error on estimated yield for sunflower was less than 10% compared to agricultural census data. The relative error on estimated melon yield was 16%. This performance highlights the applicability of the model to estimate field-scale yields in agroecosystems characterized by fragmented land use.

Predicting the Number of Reported Pulmonary Tuberculosis in Guiyang, China, Based on Time Series Analysis Techniques.

Tuberculosis (TB) is one of the world's deadliest infectious disease killers today, and despite China's increasing efforts to prevent and control TB, the TB epidemic is still very serious. In the context of the COVID-19 pandemic, if reliable forecasts of TB epidemic trends can be made, they can help policymakers with early warning and contribute to the prevention and control of TB. In this study, we collected monthly reports of pulmonary tuberculosis (PTB) in Guiyang, China, from January 1, 2010 to December 31, 2020, and monthly meteorological data for the same period, and used LASSO regression to screen four meteorological factors that had an influence on the monthly reports of PTB in Guiyang, including sunshine hours, relative humidity, average atmospheric pressure, and annual highest temperature, of which relative humidity (6-month lag) and average atmospheric pressure (7-month lag) have a lagging effect with the number of TB reports in Guiyang. Based on these data, we constructed ARIMA, Holt-Winters (additive and multiplicative), ARIMAX (with meteorological factors), LSTM, and multivariable LSTM (with meteorological factors). We found that the addition of meteorological factors significantly improved the performance of the time series prediction model, which, after comprehensive consideration, included the ARIMAX (1,1,1) (0,1,2)12 model with a lag of 7 months at the average atmospheric pressure, outperforms the other models in terms of both fit (RMSE = 37.570, MAPE = 10.164%, MAE = 28.511) and forecast sensitivity (RMSE = 20.724, MAPE = 6.901%, MAE = 17.306), so the ARIMAX (1,1,1) (0,1,2)12 model with a lag of 7 months can be used as a predictor tool for predicting the number of monthly reports of PTB in Guiyang, China.

Convolutional neural networks for ultrasound corrosion profile time series regression

A customised convolutional neural network (CNN) architecture is proposed in this paper to make estimations about the thickness values (minimum and mean) of corroded profiles from an ultrasonic time-series measurement (A-scan). The CNN architecture is determined after a hyper-parameter optimisation which leads to the best performing network. The model is trained using synthetic data and tested on both synthetic and experimental datasets. A comparison is made with (1) a state-of-the-art network for time series analysis and (2) conventional thickness estimation techniques such as peak envelope and threshold crossing. The proposed network provides an accurate estimation of the thickness values and outperforms the conventional techniques in both synthetic and experimental datasets. When compared to a conventional threshold-crossing technique for minimum thickness prediction, the proposed network is more consistent and less sensitive to changes in the threshold.

Salt marsh-atmosphere CO2 exchanges in Patos Lagoon Estuary, Southern Brazil

Blue carbon ecosystems are recognized as carbon sinks and therefore for their potential for climate mitigation. While carbon stocks and burial rates have been quantified and estimated regionally and globally, there are still many knowledge gaps on carbon fluxes exchanged particularly at the interface vegetation-atmosphere. In this study we measured the atmospheric CO2 concentrations in a salt marsh located in the Patos Lagoon Estuary, southern Brazil. Eddy correlation techniques were applied to account for the CO2 exchange fluxes between the vegetation and the atmosphere. Our dataset refers to two sampling periods spanning from July up to November 2016 and from January to April 2017. By using time series analysis techniques including wavelet and cross-wavelet analysis, our results show the natural cycles of the CO2 exchanges variability and the relationship of these cycles with other environmental variables. We also present the amplitudes of the salt marsh-atmosphere CO2 fluxes’ diurnal cycle for both study periods and demonstrate that the CO2 fluxes are modulated by the passage of transient atmospheric systems and by the level variation of surrounding waters. During daytime, our site was as a CO2 sink. Fluxes were measured as -6.71 ± 5.55 μmol m-2 s-1 and -7.95 ± 6.44 μmol m-2 s-1 for the winter-spring and summer-fall periods, respectively. During nighttime, the CO2 fluxes were reversed and our site behaved as a CO2 source. Beside the seasonal changes in sunlight and air temperature, differences between the two periods were marked by the level of marsh inundation, winds and plant biomass (higher in summer). The net CO2 balance showed the predominance of the photosynthetic activity over community respiration, indicating the role of the salt marsh as a CO2 sink. When considering the yearly-averaged net fluxes integrated to the whole area of the Patos Lagoon Estuary marshes, the total CO2 sink was estimated as -87.6 Mg C yr-1. This paper is the first to measure and study the vegetation-atmosphere CO2 fluxes of a salt marsh environment of Brazil. The results will contribute to the knowledge on the global carbon budget and for marsh conservation and management plans, including climate change policies.

ANALISIS PERKEMBANGAN PENDUDUK TERHADAP TINGKAT PARTISIPASI ANGKATAN KERJA DI INDONESIA

The demographic structure and change that happens through time is one of the most influential factors on economic development. Ignoring the demographic structure might result in discrepancies, which will affect planning issues. Ultimately, this results in inequalities in several economic, social, and political spheres. Meanwhile, the economic sector is one of the most effective areas of the demographic structure. This research aims to determine the extent to which population development factors impact Indonesia's work force participation rate. This research employs a time-series analysis technique using the ARDL Error Correction Model analytical instrument. ARDL models may be used to models with varying degrees of stationarity. Short-term fertility characteristics had a significant and negative influence on the work force participation rate, according to the findings of the investigation. On the other side, the dependence ratio and short-term government expenditures have a significant and positive influence on the work force participation rate. However, global factors have insignificant influence on the labor force participation rate in long-term.

Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy.

The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime. However, limited efforts have been made in the crime prediction area due to the deficiency of spatiotemporal crime data. Several machine learning, deep learning, and time series analysis techniques are exploited, but accuracy issues prevail. Thus, this study proposed a Bidirectional Long Short Term Memory (Bi-LSTM) and Exponential Smoothing (ES) hybrid for crime forecasting. The proposed technique is evaluated using New York City crime data from 2010-2017. The proposed approach outperformed as compared to state-of-the-art Seasonal Autoregressive Integrated Moving Averages (SARIMA) with low Mean Absolute Percentage Error (MAPE) (0.3738, 0.3891, 0.3433,0.3964), Root Mean Square Error (RMSE)(13.146, 13.669, 13.104, 13.77), and Mean Absolute Error (MAE) (9.837, 10.896, 10.598, 10.721). Therefore, the proposed technique can help law enforcement agencies to prevent and control crime by forecasting crime patterns.

Methods of Nonlinear Time Series Cycle Analysis in Big Data Environment and IoT Application

There have been a lot of changes in nonlinear time series analysis techniques over the last few decades. They came from their time series background and have grown some of these techniques to try to fill in a gap in the ability to model and predict certain types of data, like chaotic and fixed systems. It is possible to find a lot of these systems all over different places in both natural and human worlds. This study explains how these techniques came to be, what they are based on, and how some of them can predict what will happen in the future. This study is trying to figure out how they work and what they could be used for. The contribution of this study is a different way to look at time series data. It looks at how the data is made and how big the time series is. Here, we talk about how the model comes together in terms of order. In this example, we look at the time series of the sectorial indices on the National Stock Exchange (India) to show that the model is getting close. Convergence also points out that, when we fit the same number of data points to the same model, we get the same thing every time. This shows that a true model correctly predicted the value of the future and that data always comes together in nature. A study found that the size of a big data time series should be taken into account when looking at the time series’ realization. This means that the DGP intervals for each study period can be the best representative intervals for that time period.

Dividend policy issues in the European pharmaceutical industry: new empirical evidence

This paper examines dividend policy issues in the European pharmaceutical industry. This sector is of particular interest because of the high research and development expenditures and the associated risks characterizing the business models of many firms in this industry. In fact, from the perspective of corporate finance theory, this is a particular challenge for the managers of these corporations that may also have implications for the dividend policy implemented by the firms forming this sector. Moreover, the level of internal financing and litigation risks also seem to be high in the pharmaceutical industry. These facts could also affect the payout policy of the firms. Employing techniques of time series analysis, there is no evidence for dividend signaling and clear evidence for dividend smoothing in the European pharmaceutical industry. Given that dividend increases under certain assumptions can negatively affect the firms' ability to finance new investments in general and research and development projects in particular, these results of our empirical investigations could be described as highly plausible.