Model Explanatory Variables Research Articles

Effects of inflation on foreign direct investment in Nigeria

Inflation's effects on the volume of foreign direct investment (FDI) in Nigeria from 1995 to 2020 are examined in this study. In order to establish the relationship between the dependent variable (foreign direct investment) and the independent variables (inflation, real interest rate, and exchange rate), secondary data were gathered from the CBN Statistical Bulletin, Nigeria Bureau of Statistics and the World Bank Database. Johansen cointegration test was used to ascertain the long-term relationship between FDI and the independent variables, while the enhanced Dicker-Fuller test (ADF) was used to examine the data's time series property. It was decided to use the Vector Error Correction Model (VECM), and the error coefficient, which is correctly signed and significant, shows that the independent variables and FDI have a long-term relationship. The model's explanatory variables' effects on FDI were statistically insignificant. But as indicated by the probability of the F-statistic at the 5% level of significance, the overall regression was significant. The study suggests that efforts be made to use additional macroeconomic policies as a way to boost foreign direct investment in the economy and that the government and monetary authorities should not solely rely on the tool and model for controlling inflation.

Asymmetric Impacts of Public Debt on Economic Growth: Empirical Evidence From Vietnam

Purpose: The objective studies the influence of government debt on the expansion of the Vietnamese economy was the subject of the research presented in this article. Theoretical framework: Most governments in developing nations have budget deficits due to excessive spending and inadequate revenue. When the government chooses to pay the budget deficit through borrowing, it incurs a liability known as public debt and public debt on economic growth. Design/methodology/approach: In regression analysis involving time series data, if the regression model contains the variables' present values and the lagged values (past values), this model is known as the lagged distribution model. If the model's explanatory variables include one or more lagged values of the dependent variable, the model is called the autoregressive model. Nonlinear Auto Regressive Distributed Lag regression was applied. Findings: The findings reveal that government debt has a considerable and disproportionate effect on sustained economic growth in the short and long run using a Nonlinear Autoregressive Distributed Lag (NARDL) model with quarterly data over twenty years beginning in 2000. Research, Practical & Social implications: The results indicate a disproportional association between public sector debt levels and short- and long-term economic growth. The results are consistent with recent empirical studies showing a nonlinear relationship between some nations' public debt and economic development. Originality/value: Government debt should support short- and long-term economic growth through funding production. Consequently, government debt should not burden the economy when the high amount of debt exceeds the capacity to repay.

A cross-sectional study of Leishmania spp. in draft horses from the Distrito Federal, Brazil: Seroprevalence, spatial distribution, and associated factors

Equine leishmaniasis is a neglected tropical disease caused by the protozoan of the Leishmania genus, and it has been reported in several countries around the world, especially Brazil. Therefore, the present investigation aims to conduct a cross-sectional study to determine the prevalence, spatial distribution, and associated factors with seropositivity for Leishmania spp. in draft horses from the Distrito Federal, Brazil. The serological survey was conducted on 411 animals, employing the Indirect Immunofluorescence Test (IFA) and Enzyme-Linked Immunosorbent Assay (ELISA). The Kappa (κ) and gross agreement indexes evaluated the Leishmania spp. seropositivity by IFA and ELISA test. The statistical analysis was performed using the Chi-square test and logistic regression. The spatial analysis showed the areas with the highest number of seropositive and the Moran autocorrelation analyses between the spatial distribution and the epidemiological model's explanatory variables. A 27.01 % co-positivity was observed with a κ index of 52.64 %. The final model considered the variables: access to water bodies (p-value = 0.008, Odds Ratio (OR) = 2.26, Confidence Interval (CI) = 1.24–4.13), the absence of the use of ectoparasiticide (p-value = 0.008, OR = 1.93 CI = 1.18–3.15) and traveling animal (p-value = 0.059, OR = 1.54, CI = 0.98–2.41). The Kernel map showed hot areas with a high concentration of nine positive animals per area and some lighter areas ranging from five to seven positive animals per area where control measures should be performed. The Moran autocorrelation analysis was significant for the variables: traveling animal (Moran's I = 0.540 and pseudo-p-value = 0.001) and the absence of use ectoparasiticide (Moran's I = 0.259 and pseudo-p-value = 0.005). The current study exposes a high seroprevalence of Leishmania spp. in horses in the Distrito Federal, Brazil. Moreover, it proposes that traveling animal, the access to water bodies and the absence of the use of ectoparasiticide are significantly associated with seropositivity for Leishmania spp. in draft horses, which may contribute to the implementation of prophylactic and controls measures where leishmaniasis is already stalled.

AN ECONOMETRIC PANEL-MIDAS MODEL OF ASSET RETURNS IN THE BRAZILIAN STOCK MARKET

We present the specification, estimation and testing of an econometric model intended to explain and forecast individual returns of securities listed on the Brazilian stock market. The model's explanatory variables include macroeconomic, fundamental and behavioural variables sampled at different frequencies. The model uses the MIDAS regression methodology, which supports estimation of regressions with variables sampled at different frequencies. The sample includes non-financial institutions listed in the Brazilian stock exchange from 2010 to 2016. The results indicate that the model is robust in explaining and forecasting quarterly returns of individual shares listed on that market.

Evaluation of two statistical approaches for estimating pollutant loads at adjacent combined sewer overflow structures.

Quantifying pollutant loads from combined sewer overflows (CSOs) is necessary for assessing impacts of urban drainage on receiving water bodies. Based on data obtained at three adjacent CSO structures in the Louis Fargue catchment in Bordeaux, France, this study implements multiple linear regression (MLR) and random forest regression (RFR) approaches to develop statistical models for estimating emitted loads of total suspended solids (TSS). Comparison between hierarchical clustering selection and random selection of CSO events for model calibration is included in model development. The results indicate that selection of the model's explanatory variables depends on both the type of approach and the CSO structure. By using the cluster technique to select representative events for model calibration, model predictability is generally improved. For the available dataset, MLR may have advantages over RFR in terms of verification performance and lower range of error due to splitting events for calibration and verification. But RFR model uncertainty bands are considerably narrower than the MLR ones.

An Econometric Panel-Midas Model of Asset Returns in the Brazilian Stock Market

The paper describes the specification, estimation, and testing of an unrestricted structural econometric model design to explain and forecast individual returns of securities listed on the Brazilian stock market. The model's explanatory variables include macroeconomic, fundamental and behavioural variables which are sampled at different frequencies, since they are informed within different time periods, i.e. daily, monthly or quarterly. Accordingly, the model is specified and estimated using the MIDAS (Mixed Data Sampling) regression methodology, which supports estimation of regressions with variables sampled at different frequencies. The sample includes non-financial institutions listed in the Brazilian stock exchange from 2010 to 2016. The results indicate that the model is robust in explaining and forecasting quarterly returns of individual shares listed in that market.

Aggregation and equilibrium with multinomial logit models

The problem of predicting the market share (or the usage) of a given product with disaggregate discrete choice models can be decomposed into two components: aggregation and equilibrium. The aggregation issue is rooted in the distribution of the choice model's explanatory variables across the population, while the equilibrium problem stems from the dependence of some of the explanatory variables on the alternatives' usage. This paper outlines the problem and suggests a mathematical programming formulation for it. This program is shown to have the same solution as the simultaneous aggregation/equilibrium problem, and thus solving the program also solves this problem. The paper also includes a description of an algorithm for minimizing the aggregation/equilibrium program and a simple example which illustrates the formulation and the algorithm. The focus of the paper is on multinomial logit models and on the area of travel demand, but the results are extended to a general class of random utility models and to other problems.