# Chapter 5: Hypothesis Tests and Model Selection#

## Example 5.1 An Investment Equation#

This example considers a linear regression model of painting auction prices such that:

In particular, it considers whether *Size* is a statistically significant determinant of *Price*. This is done by testing the hypothesis:

If *Size* is a statistically significant determinant of *Price* than the null hypothesis that \(\beta_2 = 0\) should be rejected.

### Getting Started#

To run this example on your own you will need to install the greeneLib package. This package houses all examples and associated data.

### How to#

#### Step One: Loading data#

To start, load the relevant variables from *Table 4.7* using `loadd()`

and a formula string.

```
// Load data using loadd
fname = getGAUSShome() $+ "pkgs/GreeneLib/examples/TableF4-1.csv";
monet_data = loadd(fname, "HEIGHT+ ln(Price) + WIDTH");
```

The code above:

Tranforms the raw data variable,

*Price*into our dependent variable*ln(Price)*.The raw data variables

*Height*and*Width*are loaded so we can create our dependent variables,*Aspect Ratio*and the*Size*.

#### Step Two: Create dependent variables#

Our dependent variables are created according to:

First, we compute the new variables:

```
// Compute aspect ratio
aspect = monet_data[., "WIDTH"] ./ monet_data[., "HEIGHT"];
// Compute size
size = monet_data[., "WIDTH"] .* monet_data[., "HEIGHT"];
```

Next, we will use the function `setColNames()`

to give our variables the correct names:

```
/*
** Change assigned variable names, `width`,
** to match variables
*/
aspect = setColNames(aspect, "Aspect Ratio");
size = setColNames(size, "Size");
```

Finally we will create a new dataframe containing our estimation data:

```
// Create regression data using the horizontal concatenation operator
reg_data = monet_data[., "ln_Price_"] ~ size ~ aspect;
```

#### Step Three: Estimate our linear model#

Finally, we call `olsmt()`

to run ordinary least squares estimation and store our results for later hypothesis testing.

Notice that we can transform our *size* variable to *ln(Size)* directly in the formula string.

```
/*
** Calling olsmt
** Note that the print out includes
** coefficients along with the t-stats
** which test the hypothesis that
** the coefficients equal zero
*/
struct olsmtOut o_out;
o_out = olsmt(reg_data, "ln_Price_ ~ ln(Size) + Aspect Ratio");
```

When we call `olsmt()`

a complete set of results are printed to screen including:

Coefficient estimates.

The t-statistics testing the null hypothesis that the coefficient are equal to zero.

The p-values associated with the t-statistics.

```
Standard Prob Standardized Cor with
Variable Estimate Error t-value >|t| Estimate Dep Var
-----------------------------------------------------------------------------------
CONSTANT -8.34236 0.678203 -12.3007 0.000 --- ---
ln(Size) 1.31638 0.0920493 14.3009 0.000 0.573347 0.577572
Aspect Ratio -0.0962332 0.15784 -0.609689 0.542 -0.0244435 -0.123553
```

These results confirm that:

The

*ln(Size)*variable is statistically significant with a t-statistic equal to 14.3009.The

*Aspect Ratio*variable is not statistically significant with a t-statistic equal to -0.61.

#### Step Four: Additional testing#

Following Greene, let’s also test

```
// Test hypothesis that beta_2 =< 1
t_stat_1 = (o_out.b[2] - 1) / o_out.stderr[2];
```

The t-statistic testing that \(\beta_2 \leq 1\) is 3.437.