Econometrics

24 pages, 858 KiB

Open AccessArticle

Investigation of Equilibrium in Oligopoly Markets with the Help of Tripled Fixed Points in Banach Spaces

by Atanas Ilchev, Vanya Ivanova, Hristina Kulina, Polina Yaneva and Boyan Zlatanov

Econometrics 2024, 12(2), 18; https://doi.org/10.3390/econometrics12020018 - 17 Jun 2024

Viewed by 962

In the study we explore an oligopoly market for equilibrium and stability based on statistical data with the help of response functions rather than payoff maximization. To achieve this, we extend the concept of coupled fixed points to triple fixed points. We propose [...] Read more.

In the study we explore an oligopoly market for equilibrium and stability based on statistical data with the help of response functions rather than payoff maximization. To achieve this, we extend the concept of coupled fixed points to triple fixed points. We propose a new model that leads to generalized triple fixed points. We present a possible application of the generalized tripled fixed point model to the study of market equilibrium in an oligopolistic market dominated by three major competitors. The task of maximizing the payout functions of the three players is modified by the concept of generalized tripled fixed points of response functions. The presented model for generalized tripled fixed points of response functions is equivalent to Cournot payoff maximization, provided that the market price function and the three players’ cost functions are differentiable. Furthermore, we demonstrate that the contractive condition corresponds to the second-order constraints in payoff maximization. Moreover, the model under consideration is stable in the sense that it ensures the stability of the consecutive production process, as opposed to the payoff maximization model with which the market equilibrium may not be stable. A possible gap in the applications of the classical technique for maximization of the payoff functions is that the price function in the market may not be known, and any approximation of it may lead to the solution of a task different from the one generated by the market. We use empirical data from Bulgaria’s beer market to illustrate the created model. The statistical data gives fair information on how the players react without knowing the price function, their cost function, or their aims towards a specific market. We present two models based on the real data and their approximations, respectively. The two models, although different, show similar behavior in terms of time and the stability of the market equilibrium. Thus, the notion of response functions and tripled fixed points seems to present a justified way of modeling market processes in oligopoly markets when searching whether the market has reached equilibrium and if this equilibrium is unique and stable in time Full article

► Show Figures

Figure 1

26 pages, 597 KiB

Open AccessArticle

Modeling the Economic Impact of the COVID-19 Pandemic Using Dynamic Panel Models and Seemingly Unrelated Regressions

by Ioannis D. Vrontos, John Galakis, Ekaterini Panopoulou and Spyridon D. Vrontos

Econometrics 2024, 12(2), 17; https://doi.org/10.3390/econometrics12020017 - 14 Jun 2024

Viewed by 1283

Abstract

The importance of assessing and estimating the impact of the COVID-19 pandemic on financial markets and economic activity has attracted the interest of researchers and practitioners in recent years. The proposed study aims to explore the pandemic’s impact on the economic activity of [...] Read more.

The importance of assessing and estimating the impact of the COVID-19 pandemic on financial markets and economic activity has attracted the interest of researchers and practitioners in recent years. The proposed study aims to explore the pandemic’s impact on the economic activity of six Euro area economies. A class of dynamic panel data models and their corresponding Seemingly Unrelated Regression (SUR) models are developed and applied to model the economic activity of six Eurozone countries. This class of models allows for common and country-specific covariates to affect the real growth, as well as for cross-sectional dependence in the error processes. Estimation and inference for this class of panel models are based on both Bayesian and classical techniques. Our findings reveal that significant heterogeneity exists among the different economies with respect to the explanatory/predictive factors. The impact of the COVID-19 pandemic varied across the Euro area economies under study. Nonetheless, the outbreak of the COVID-19 pandemic profoundly affected real economic activity across all regions and countries. As an exogenous shock of such magnitude, it caused a sharp increase in overall uncertainty that spread quickly across all sectors of the global economy. Full article

► Show Figures

Figure 1

26 pages, 3411 KiB

Open AccessArticle

Predicting the Direction of NEPSE Index Movement with News Headlines Using Machine Learning

by Keshab Raj Dahal, Ankrit Gupta and Nawa Raj Pokhrel

Econometrics 2024, 12(2), 16; https://doi.org/10.3390/econometrics12020016 - 11 Jun 2024

Viewed by 2380

Abstract

Predicting stock market movement direction is a challenging task due to its fuzzy, chaotic, volatile, nonlinear, and complex nature. However, with advancements in artificial intelligence, abundant data availability, and improved computational capabilities, creating robust models capable of accurately predicting stock market movement is [...] Read more.

Predicting stock market movement direction is a challenging task due to its fuzzy, chaotic, volatile, nonlinear, and complex nature. However, with advancements in artificial intelligence, abundant data availability, and improved computational capabilities, creating robust models capable of accurately predicting stock market movement is now feasible. This study aims to construct a predictive model using news headlines to predict stock market movement direction. It conducts a comparative analysis of five supervised classification machine learning algorithms—logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN)—to predict the next day’s movement direction of the close price of the Nepal Stock Exchange (NEPSE) index. Sentiment scores from news headlines are computed using the Valence Aware Dictionary for Sentiment Reasoning (VADER) and TextBlob sentiment analyzer. The models’ performance is evaluated based on sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC). Experimental results reveal that all five models perform equally well when using sentiment scores from the TextBlob analyzer. Similarly, all models exhibit almost identical performance when using sentiment scores from the VADER analyzer, except for minor variations in AUC in SVM vs. LR and SVM vs. ANN. Moreover, models perform relatively better when using sentiment scores from the TextBlob analyzer compared to the VADER analyzer. These findings are further validated through statistical tests. Full article

► Show Figures

Figure 1

14 pages, 2200 KiB

Open AccessArticle

Exponential Time Trends in a Fractional Integration Model

by Guglielmo Maria Caporale and Luis Alberiko Gil-Alana

Econometrics 2024, 12(2), 15; https://doi.org/10.3390/econometrics12020015 - 31 May 2024

Viewed by 1031

Abstract

This paper introduces a new modelling approach that incorporates nonlinear, exponential deterministic terms into a fractional integration framework. The proposed model is based on a specific test on fractional integration that is more general than the standard methods, which allow for only linear [...] Read more.

This paper introduces a new modelling approach that incorporates nonlinear, exponential deterministic terms into a fractional integration framework. The proposed model is based on a specific test on fractional integration that is more general than the standard methods, which allow for only linear trends.. Its limiting distribution is standard normal, and Monte Carlo simulations show that it performs well in finite samples. Three empirical examples confirm that the suggested specification captures the properties of the data adequately. Full article

► Show Figures

Figure 1

19 pages, 806 KiB

Open AccessArticle

Financial and Oil Market’s Co-Movements by a Regime-Switching Copula

by Manel Soury

Econometrics 2024, 12(2), 14; https://doi.org/10.3390/econometrics12020014 - 24 May 2024

Viewed by 1040

Abstract

Over the years, oil prices and financial stock markets have always had a complex relationship. This paper analyzes the interactions and co-movements between the oil market (WTI crude oil) and two major stock markets in Europe and the US (the Euro Stoxx 50 [...] Read more.

Over the years, oil prices and financial stock markets have always had a complex relationship. This paper analyzes the interactions and co-movements between the oil market (WTI crude oil) and two major stock markets in Europe and the US (the Euro Stoxx 50 and the SP500) for the period from 1990 to 2023. For that, I use both the time-varying and the Markov copula models. The latter one represents an extension of the former one, where the constant term of the dynamic dependence parameter is driven by a hidden two-state first-order Markov chain. It is also called the dynamic regime-switching (RS) copula model. To estimate the model, I use the inference function for margins (IFM) method together with Kim’s filter for the Markov switching process. The marginals of the returns are modeled by the GARCH and GAS models. Empirical results show that the RS copula model seems adequate to measure and evaluate the time-varying and non-linear dependence structure. Two persistent regimes of high and low dependency have been detected. There was a jump in the co-movements of both pairs during high regimes associated with instability and crises. In addition, the extreme dependence between crude oil and US/European stock markets is time-varying but also asymmetric, as indicated by the SJC copula. The correlation in the lower tail is higher than that in the upper. Hence, oil and stock returns are more closely joined and tend to co-move more closely together in bullish periods than in bearish periods. Finally, the dependence between WTI crude oil and the SP500 stock index seems to be more affected by exogenous shocks and instability than the oil and European stock markets. Full article

► Show Figures

Figure 1

21 pages, 3730 KiB

Open AccessArticle

On the Validity of Granger Causality for Ecological Count Time Series

by Konstantinos G. Papaspyropoulos and Dimitris Kugiumtzis

Econometrics 2024, 12(2), 13; https://doi.org/10.3390/econometrics12020013 - 9 May 2024

Viewed by 1893

Abstract

Knowledge of causal relationships is fundamental for understanding the dynamic mechanisms of ecological systems. To detect such relationships from multivariate time series, Granger causality, an idea first developed in econometrics, has been formulated in terms of vector autoregressive (VAR) models. Granger causality for [...] Read more.

Knowledge of causal relationships is fundamental for understanding the dynamic mechanisms of ecological systems. To detect such relationships from multivariate time series, Granger causality, an idea first developed in econometrics, has been formulated in terms of vector autoregressive (VAR) models. Granger causality for count time series, often seen in ecology, has rarely been explored, and this may be due to the difficulty in estimating autoregressive models on multivariate count time series. The present research investigates the appropriateness of VAR-based Granger causality for ecological count time series by conducting a simulation study using several systems of different numbers of variables and time series lengths. VAR-based Granger causality for count time series (DVAR) seems to be estimated efficiently even for two counts in long time series. For all the studied time series lengths, DVAR for more than eight counts matches the Granger causality effects obtained by VAR on the continuous-valued time series well. The positive results, also in two ecological time series, suggest the use of VAR-based Granger causality for assessing causal relationships in real-world count time series even with few distinct integer values or many zeros. Full article

► Show Figures

Figure 1

21 pages, 774 KiB

Open AccessArticle

Short-Term Hourly Ozone Concentration Forecasting Using Functional Data Approach

by Ismail Shah, Naveed Gul, Sajid Ali and Hassan Houmani

Econometrics 2024, 12(2), 12; https://doi.org/10.3390/econometrics12020012 - 5 May 2024

Viewed by 1844

Abstract

Air pollution, especially ground-level ozone, poses severe threats to human health and ecosystems. Accurate forecasting of ozone concentrations is essential for reducing its adverse effects. This study aims to use the functional time series approach to model ozone concentrations, a method less explored [...] Read more.

Air pollution, especially ground-level ozone, poses severe threats to human health and ecosystems. Accurate forecasting of ozone concentrations is essential for reducing its adverse effects. This study aims to use the functional time series approach to model ozone concentrations, a method less explored in the literature, and compare it with traditional time series and machine learning models. To this end, the ozone concentration hourly time series is first filtered for yearly seasonality using smoothing splines that lead us to the stochastic (residual) component. The stochastic component is modeled and forecast using a functional autoregressive model (FAR), where each daily ozone concentration profile is considered a single functional datum. For comparison purposes, different traditional and machine learning techniques, such as autoregressive integrated moving average (ARIMA), vector autoregressive (VAR), neural network autoregressive (NNAR), random forest (RF), and support vector machine (SVM), are also used to model and forecast the stochastic component. Once the forecast from the yearly seasonality component and stochastic component are obtained, both are added to obtain the final forecast. For empirical investigation, data consisting of hourly ozone measurements from Los Angeles from 2013 to 2017 are used, and one-day-ahead out-of-sample forecasts are obtained for a complete year. Based on the evaluation metrics, such as

R^{2}

, root mean squared error (RMSE), and mean absolute error (MAE), the forecasting results indicate that the FAR outperforms the competitors in most scenarios, with the SVM model performing the least favorably across all cases. Full article

► Show Figures

Figure 1

23 pages, 374 KiB

Open AccessArticle

Stein-like Common Correlated Effects Estimation under Structural Breaks

by Shahnaz Parsaeian

Econometrics 2024, 12(2), 11; https://doi.org/10.3390/econometrics12020011 - 18 Apr 2024

Viewed by 1406

Abstract

This paper develops a Stein-like combined estimator for large heterogeneous panel data models under common structural breaks. The model allows for cross-sectional dependence through a general multifactor error structure. By utilizing the common correlated effects (CCE) estimation technique, we propose a Stein-like combined [...] Read more.

This paper develops a Stein-like combined estimator for large heterogeneous panel data models under common structural breaks. The model allows for cross-sectional dependence through a general multifactor error structure. By utilizing the common correlated effects (CCE) estimation technique, we propose a Stein-like combined estimator of the CCE full-sample estimator (i.e., estimation using both the pre-break and post-break observations) and the CCE post-break estimator (i.e., estimation using only the post-break sample observations). The proposed Stein-like combined estimator benefits from exploiting the pre-break sample observations. We derive the optimal combination weight by minimizing the asymptotic risk. We show the superiority of the CCE Stein-like combined estimator over the CCE post-break estimator in terms of the asymptotic risk. Further, we establish the asymptotic properties of the CCE mean group Stein-like combined estimator. The finite sample performance of our proposed estimator is investigated using Monte Carlo experiments and an empirical application of predicting the output growth of industrialized countries. Full article

16 pages, 505 KiB

Open AccessArticle

The Gini and Mean Log Deviation Indices of Multivariate Inequality of Opportunity

by Marek Kapera and Martyna Kobus

Econometrics 2024, 12(2), 10; https://doi.org/10.3390/econometrics12020010 - 17 Apr 2024

Viewed by 1752

Abstract

The most common approach to measuring inequality of opportunity in income is to apply the Gini inequality index or the Mean Log Deviation (MLD) index to a smoothed distribution (i.e., a distribution of type mean incomes). We show how this approach can be [...] Read more.

The most common approach to measuring inequality of opportunity in income is to apply the Gini inequality index or the Mean Log Deviation (MLD) index to a smoothed distribution (i.e., a distribution of type mean incomes). We show how this approach can be naturally extended to include life outcomes other than income (e.g., health, education). We propose two measures: the Gini and MLD indices of multivariate inequality of opportunity. We show that they can be decomposed into the contribution of each outcome and the dependence of the outcomes. Using these measures, we calculate inequality of opportunity in health and income across European countries. Full article

► Show Figures

Figure 1

15 pages, 312 KiB

Open AccessArticle

A Pretest Estimator for the Two-Way Error Component Model

by Badi H. Baltagi, Georges Bresson and Jean-Michel Etienne

Econometrics 2024, 12(2), 9; https://doi.org/10.3390/econometrics12020009 - 16 Apr 2024

Viewed by 1714

Abstract

For a panel data linear regression model with both individual and time effects, empirical studies select the two-way random-effects (TWRE) estimator if the Hausman test based on the contrast between the two-way fixed-effects (TWFE) estimator and the TWRE estimator is not rejected. Alternatively, [...] Read more.

For a panel data linear regression model with both individual and time effects, empirical studies select the two-way random-effects (TWRE) estimator if the Hausman test based on the contrast between the two-way fixed-effects (TWFE) estimator and the TWRE estimator is not rejected. Alternatively, they select the TWFE estimator in cases where this Hausman test rejects the null hypothesis. Not all the regressors may be correlated with these individual and time effects. The one-way Hausman-Taylor model has been generalized to the two-way error component model and allow some but not all regressors to be correlated with these individual and time effects. This paper proposes a pretest estimator for this two-way error component panel data regression model based on two Hausman tests. The first Hausman test is based upon the contrast between the TWFE and the TWRE estimators. The second Hausman test is based on the contrast between the two-way Hausman and Taylor (TWHT) estimator and the TWFE estimator. The Monte Carlo results show that this pretest estimator is always second best in MSE performance compared to the efficient estimator, whether the model is random-effects, fixed-effects or Hausman and Taylor. This paper generalizes the one-way pretest estimator to the two-way error component model. Full article

► Show Figures

Figure 1

16 pages, 2945 KiB

Open AccessArticle

Biases in the Maximum Simulated Likelihood Estimation of the Mixed Logit Model

by Maksat Jumamyradov, Murat Munkin, William H. Greene and Benjamin M. Craig

Econometrics 2024, 12(2), 8; https://doi.org/10.3390/econometrics12020008 - 27 Mar 2024

Viewed by 1707

Abstract

In a recent study, it was demonstrated that the maximum simulated likelihood (MSL) estimator produces significant biases when applied to the bivariate normal and bivariate Poisson-lognormal models. The study’s conclusion suggests that similar biases could be present in other models generated by correlated [...] Read more.

In a recent study, it was demonstrated that the maximum simulated likelihood (MSL) estimator produces significant biases when applied to the bivariate normal and bivariate Poisson-lognormal models. The study’s conclusion suggests that similar biases could be present in other models generated by correlated bivariate normal structures, which include several commonly used specifications of the mixed logit (MIXL) models. This paper conducts a simulation study analyzing the MSL estimation of the error components (EC) MIXL. We find that the MSL estimator produces significant biases in the estimated parameters. The problem becomes worse when the true value of the variance parameter is small and the correlation parameter is large in magnitude. In some cases, the biases in the estimated marginal effects are as large as 12% of the true values. These biases are largely invariant to increases in the number of Halton draws. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Econometrics, Volume 12, Issue 2 (June 2024) – 11 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI