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panel tobit model in stata forex

an autoregressive panel probit model where the autocorrelation in the in estimating the random effects model in the software STATA. Two packages, Stata 10 [1] and Limdep 9 [2], each contain two estimators variable models (which include probit and tobit). He proposes what is sometimes. Rectifying the control discrepancy in the FX hedging Tobit regression increases the magnitude and significance of the exposure coefficient. MINING BITCOIN CLOUD

Of all these, the tourism industry was the most affected by COVID in , while the level of urbanization, technology, and ecological environment developments in the three provinces has become similar over time. Only eco-environmental pressure has a significant positive correlation with the degree of uncoordinated coupling, while the tourism scale, economic urbanization, eco-environmental response, and investment in technology have a significant negative correlation.

Keywords: uncoordinated coupling, spatiotemporal characteristics, influencing factors, Yunnan—Guizhou—Sichuan region 1. The tourism industry, urbanization and scientific and technological investments are the driving forces behind economic transformation and development, while the ecological environment forms a basis for ensuring high-quality economic development.

Specifically, as a green and efficient industry, tourism plays an important role in high-quality economic development, becoming a new engine with which local governments can promote urbanization. At the same time, it can provide funds for scientific and technological innovation and can also promote the transformation of scientific and technological achievements into productivity [ 2 ]. Cities are both important tourist destinations and productive areas of technology research and development.

High levels of urbanization and urban economic prosperity have injected vitality into the development of both tourism and science and technology [ 3 ]. Technology is the primary productive force; it is a strong driving force behind urban construction and promotes the transformation and upgrading of the tourism industry [ 4 ].

In the process of development, the three factors listed above are inseparable from the ecological environment, and a good ecological environment provides basic support for their development [ 5 ]. Tourism, urbanization, technology, and the ecological environment both promote and restrict each other.

Coordinating the relationship between these four elements is of great significance to the realization of high-quality sustainable regional development. In the rapid development of an economy, the interaction between tourism development, urban construction, scientific and technological innovation, and the ecological environment presents a non-benign interaction phenomenon that belongs to the stage problem of regional development and which has a certain inevitability.

Therefore, this paper identifies the uncoordinated coupling relationship between tourism, urbanization, technology, and the ecological environment; it does so using reverse thinking, constructing a bridge from uncoordinated coupling to coordinated coupling in theory and enriching the theoretical system of coupling and coordination.

Theoretical Background 2. Literature Review Research on the interactive relationship between tourism, urbanization, technology and the ecological environment is in its early stages and has produced valuable research results; however, most studies have predominantly focused on pairwise systems—there are relatively few studies on the relationship between the four systems.

Stansfield [ 8 ] then discussed the importance of urban tourism research from a new perspective, giving people a new understanding of the relationship between the development of tourism and urbanization. Gladstone [ 9 ] analyzed some characteristics of tourism urbanization in the United States and metropolitan leisure areas. Qian, et al. Some Western countries that had experienced earlier industrial development took the lead in studying the relationship between urbanization and the ecological environment.

Howard [ 12 ] first proposed and outlined the garden city theory, while Shahbaz, et al. Table 4. Returns of scale. And sometimes it cannot expand the investment of production factors and should make full use of resources. Analysis of the Influencing Factors of Efficiency This chapter will study the influence factors of efficiency further, providing a theoretical support for the low-efficiency enterprises. Variable Definition 1 Equity concentration Equity concentration refers to the concentration ratio of the major shareholders.

This paper chooses the proportion of the first largest shareholder to represent the equity concentration. To a certain extent, it reflects the financing situation of enterprises. If the way of finance is debt, the high debt ratio can result in the large financial leverage, and then affects the operational efficiency. This paper chooses asset-liability ratio to represent the capital structure. When a company is pursuing high profits, it can show that the operating efficiency of the enterprise is higher.

In this paper, net interest rates are used to show the profitability of the enterprise. Education has a certain role in promoting the development of productive forces. In this paper, the percentage of employees with higher education is selected to represent the degree of education. Research Hypothesis Based on the above-mentioned factors affecting the efficiency of enterprises, this paper makes the following assumptions: 1 Hypothesis 1: Other things equal, there is a negative correlation between equity concentration and technical efficiency.

According to the principles of economy, it is possible for the majority shareholder to damage the efficiency of the whole company in order to maximize the personal interests. In general, the more the companies loan, the larger they need to pay the interest.

High quality labors will bring more inspiration to create greater value for the company. According to the above research, this paper constructs the following test model and uses regression method to test the hypothesis. Empirical Results and Analysis In this part, we will take the panel data of 30 example companies collected during as foundation, and make the overall efficiency values obtained from the previous DEA model as the explained variables, 4 influencing factors as the explaining variables.

According to the DEA model, the range of overall efficiency is [0,1], so the data are not continuous. Using least squares method to calculate is invalid because the parameter estimates are biased and inconsistent. To avoid this limitation, this paper uses Tobit regression model to analyze. The model is as follows: 3 is overall efficiency, explaining variables is influencing factors, is coefficient of explaining variable. X1 is the proportion of the first largest shareholder, X2 is asset-liability ratio, X3 is net interest rates, X4 is the percentage of employees with higher education.

By using Tobit model for regression analysis, we can determine the influencing factors and how they affect the efficiency of real estate enterprises. This paper uses Stata 11 software to do the Tobit regression analysis of the panel data. The result is shown in Table 5. We analyze and explain the results above: 1 The correlation coefficient of the largest shareholder is positive, but its concomitant probability is 0.

This paper refers to the research of other scholars Wang Jianqiang et al. Regression results of Tobit model. It indicates that this index and the efficiency are negatively related. Asset-liability ratio is the ratio of total liabilities to total assets. For the operators, the higher the value, the more funds company loans, so the more interest payments are paid.

The majority of enterprises maintains a high level of asset liability ratio, and show an upward trend. The net interest rate measures the ability of the enterprise to use all the assets to gain the benefit. In the case of the total assets of enterprises remain unchanged, an increase in net interest rate, indicating that the better the level of input and output, the more improved the efficiency.

It indicates that this index and the efficiency are positive related. Employees are an intangible asset. However, Zhao Qiong pointed when introducing some other explaining variables, statistical significance between education level and efficiency will drop [23]. Conclusions DEA method has been the most popular method to explain the efficiency for decades. This method fits very well with empirical observations; therefore, it is popular with economists, although it has some shortcomings and limitations.

In this paper, DEA method and Tobit model are used to analyze the operating efficiency and influencing factors of the listed real estate companies.

Panel tobit model in stata forex world grand prix darts 2022 betting websites panel tobit model in stata forex


This research uses the tobit model for several reasons. Firstly, the leverage by definition is truncated between zero and one. Secondly, the results from the OLS estimation method for truncated data are inconsistent and biased. Thirdly, the tobit estimates are consistent and asymptotically normal Amemiya, The tobit model is a suitable approach for the truncated dependent variable to investigate the impact of macroeconomic variables on leverage of U.

The following paragraphs explain the tobit model and the way of interpreting the coefficients. Originally, Tobin suggests the tobit model to study the household expenditures on durable goods taking into account their non-negativity, while only in Arthur Goldberger referred to this model as a tobit model. This is an econometric model suitable for a truncated dependent variable with normal error terms. The tobit model is appropriate for applications where the dependent variable is continuous but its range may be constrained, such as truncated data here leverage.

The tobit model, also called a censored regression model, is designed to estimate linear relationships between variables when there is either left- or right-censoring in the dependent variable also known as censoring from below and above, respectively. Censoring from above takes place when cases with a value at or above some threshold, all take on the value of that threshold, so that the true value might be equal to the threshold, but it might also be higher.

In the case of censoring from below, values those that fall at or below some threshold are censored. Please Note: The purpose of this page is to show how to use various data analysis commands. It does not cover all aspects of the research process which researchers are expected to do. In particular, it does not cover data cleaning and checking, verification of assumptions, model diagnostics and potential follow-up analyses.

Examples of tobit regression Example 1. In the s there was a federal law restricting speedometer readings to no more than 85 mph. This is a classic case of right-censoring censoring from above of the data. The only thing we are certain of is that those vehicles were traveling at least 85 mph. Example 2. A research project is studying the level of lead in home drinking water as a function of the age of a house and family income.

The water testing kit cannot detect lead concentrations below 5 parts per billion ppb. The EPA considers levels above 15 ppb to be dangerous. These data are an example of left-censoring censoring from below. Example 3. Consider the situation in which we have a measure of academic aptitude scaled which we want to model using reading and math test scores, as well as, the type of program the student is enrolled in academic, general, or vocational.

The same is true of students who answer all of the questions incorrectly. All such students would have a score of , although they may not all be of equal aptitude. We have a hypothetical data file, tobit. The academic aptitude variable is apt , the reading and math test scores are read and math respectively.

Note that in this dataset, the lowest value of apt is No students received a score of i. Percent Cum. In the histogram below, the discrete option produces a histogram where each unique value of apt has its own bar. The freq option causes the y-axis to be labeled with the frequency for each value, rather than the density.

Because apt is continuous, most values of apt are unique in the dataset, although close to the center of the distribution there are a few values of apt that have two or three cases. Note the collection of cases at the top of each scatterplot due to the censoring in the distribution of apt.

Analysis methods you might consider Below is a list of some analysis methods you may have encountered. Some of the methods listed are quite reasonable while others have either fallen out of favor or have limitations.

Panel tobit model in stata forex crypto revival

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