How Does Firm Age Influence Growth When Evaluated at Mean Values?

In summary, linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It works by finding the best-fitting line that represents this relationship and uses this line to make predictions. There are two types of linear regression: simple, which involves one independent variable, and multiple, which involves more than one. This method has many applications in various fields, such as economics and data analysis. However, it also has limitations, including the assumption of linearity, independence of variables, and sensitivity to outliers.
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mlarson9000
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Homework Statement


Evaluate the partial effect of age of a firm on growth.(evaluated at the means)


Homework Equations



Growth=[tex]\beta[/tex]0+[tex]\beta[/tex]1age+[tex]\beta[/tex]2age^2+[tex]\beta3[/tex]size*age+[tex]\beta[/tex]4plant*age


The Attempt at a Solution



We're supposed to do something like this:
Growth=[tex]\beta[/tex]0+[tex]\beta[/tex]1age+[tex]\beta[/tex]2(age-[tex]\mu[/tex]a)^2+[tex]\beta[/tex]3(size-[tex]\mu[/tex]s)*(age-[tex]\mu[/tex]a)+[tex]\beta[/tex]4(plant-[tex]\mu[/tex]p*(age-[tex]\mu[/tex]a)

or

Growth=[tex]\beta[/tex]0+[tex]\beta[/tex]1age+[tex]\beta[/tex]2age^2+[tex]\beta[/tex]3(size-[tex]\mu[/tex]s)*(age-[tex]\mu[/tex]a)+[tex]\beta[/tex]4(plant-[tex]\mu[/tex]p*(age-[tex]\mu[/tex]a)

=[tex]\beta[/tex]1+2[tex]\beta[/tex]2age

=[tex]\beta[/tex]1+2[tex]\beta[/tex]2[tex]\mu[/tex]
 
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+\beta3(\mu-\mus)+\beta4(\mu-\mup)

where \mu is the mean age of the firms, \mus is the mean size of the firms, and \mup is the mean plant size of the firms.

To evaluate the partial effect of age on growth, we can hold the other variables (size and plant size) at their mean values and vary the age variable. This means that the \beta1 term, representing the base effect of age on growth, will remain the same. However, the \beta2 term, representing the quadratic effect of age on growth, will change as we vary the age variable. This will give us the partial effect of age on growth, evaluated at the means of the other variables.

In summary, the partial effect of age on growth can be calculated by varying the age variable while holding the other variables at their mean values, using the equation Growth=\beta0+\beta1age+\beta2age^2+\beta3(size-\mus)*(age-\mua)+\beta4(plant-\mup*(age-\mua). This will give us the change in growth for a unit change in age, while the other variables are held constant at their mean values.
 

Related to How Does Firm Age Influence Growth When Evaluated at Mean Values?

1. What is linear regression?

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It assumes that there is a linear relationship between the variables and uses this relationship to make predictions.

2. How does linear regression work?

Linear regression works by finding the best-fitting line that represents the relationship between the dependent and independent variables. It does this by minimizing the sum of the squared differences between the actual values and the predicted values.

3. What is the difference between simple and multiple linear regression?

Simple linear regression involves only one independent variable, while multiple linear regression involves more than one independent variable. This means that multiple linear regression can account for the influence of multiple factors on the dependent variable.

4. What are some common applications of linear regression?

Linear regression is commonly used in fields such as economics, finance, social sciences, and marketing to analyze and predict trends and relationships between variables. It is also used in machine learning and data analysis to make predictions and classify data.

5. What are some limitations of linear regression?

Linear regression assumes a linear relationship between the variables and may not be suitable for data with nonlinear patterns. It also assumes that the variables are independent of each other, and may not perform well if this assumption is violated. Additionally, it is sensitive to outliers and may not be accurate if they are present in the data.

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