Assignment 2
In this exercise, we fit demand equations for consumer goods using annual US time series data, and test some of the hypotheses generated by the economic theory of the consumer.
The equations we fit have the loglinear form:
log x_{t} = β_{0} + β_{p} log p_{t} + β_{q} log q_{t} + β_{y} log y_{t} + u_{t}, (1)
where  
x_{t} p_{t} q_{t} y_{t} u_{t}  =
= = = = 
real expenditure on the good price index of the good
price index of the other goods consumers’ real income disturbance term. 

The Data 
Copy the file QM Ex3.tsm from the web page. The data for this exercise are made available by Dr. Christopher Dougherty of LSE, and accompany his book Introduction to Econometrics (OUP). The data file is called newdemand.xls. This file contains annual observations for the period 19592003 on US real aggregate expenditures and prices for a selected range of commodity groups. Table 1 below lists the variable names.
The actual (billions of) dollars expended in each commodity category can be found as the product of the real expenditure and price variables. The latter are often called the “implicit deflators”. In practice, the deflating is generally done at a more disaggregated level.
Taking food, for example, prices will be measured for all the many different subcategories (variates of bread, meat, vegetables, and etc.) and used to create series of real expenditure for each. Adding these together produces the FOOD series, and the PFOOD is calculated as the ratio of actual to real expenditure, times 100 to express as a percentage. This is why all the prices equal 100 in the year 2000.
Adding together all real expenditures produces the variable TPE, and PTPE is the personal ex penditure deflator. DPI is TPE plus real savings – in other words, money savings deflated by PTPE.
We usually treat the saving/consumption decisions as separate and prior to the decision about what to spend on what goods. This approach points to using total expenditure as the ‘income’ variable in demand equations. However, DPI is often used in practice, and we should know how crucial the choice is. Check how sensitive your results are to changing the income measure.

Table 1:
The following series are also in the data set.
Total personal expenditure TPE PTPE Disposable personal income DPI
US Population (thousand) POP
After selecting a commodity for analysis, the data first have to be organised and transformed.
In the following steps we refer to a nonexistent commodity category called CAT. In practice, replace CAT everywhere in what follows by the code of your chosen category.
1.Doubleclick the TSM file QM Ex3.tsm to start the program and load the main data set. 2.Create a new data file containing just the variables needed for the analysis, as follows. In the
Setup / Data Transformation and Editing dialog, highlight CAT, PCAT, TPE, PTPE, DPI, and POP in the variable list. Then give the command Edit / Save Selected. When the file dialog opens, type cat.xls and press OK.
q = P X − px .
X − x
In particular, suppose x is the expenditure on commodity category CAT, p is the price index PCAT, X is TPE and P is PTPE. In the Setup / Data Transformation and Editing dialog, click the “Formula · · ·” button and type the following into the text field:
QCAT = (TPE*PTPE – CAT*PCAT) / (TPE – CAT).
(note: Variable names are case sensitive. Be sure to use capitals here!). Click << Go >>. The QCAT variable should appear in the variable list. If not, check your formula carefully!.
Note that your formulae are saved, and can be edited and reused as required, use Next and
Previous to navigate the list. Press Close to close the Formula window.
Exercise (do these for two contrasting commodities).
Basic Demand Model
a[i] = a[j],
where i and j are the parameter numbers for β_{p} and β_{q} – see the Values / Equation dialog to get these numbers. Make sure that the option “Wald Test of Constraints” is selected in model / Linear Equation before rerunning the regression. Be careful to explain in your answer why these two methods should always yield the same result.
Additional Variables.

6.(Optional) A parital adjustment model: add the lagged dependent variable to the equation. (The easiest way to do this is to add Log CAT as a Type 2 variable, and set the “Lags” scrollbar to 1. Note that the current value is excluded automatically.)

Consider how to interpret this equation. If the coefficient of Log CAT(1) is γ, compare your estimates of β_{p}/(1 γ), β_{q}/(1 γ) and β_{y}/(1 γ) with the elasticities obtained previously. Explain this connection. How can you test the restrictions of demand theory in this setup?
Model Stability Analysis
A time series analysis assume that the model is constant over time, but there are a number of reasons why this may not be true.
There are two ways (both rather crude) to test for the existence of such effects.
7.Do the Chow parameter stability test, after splitting the sample into two roughly equal parts. 8.Let the intercept of the model changes systematically with time. This effect is created by

including the trend dummy variable in the model, the variable t = 1, 2, , n. To add a time trend, click the “Trend” check box in the Linear Regression dialog. Test its significance in your equations, and if it is significant, check whether the outcome if any of your previous test depends upon it.
You may find that when the trend term is included the model collapses, with nonsensical estimates for the other parameters. For example, your income elasticity could become negative. If so, this is likely to be due to the problem of multicollinearity. There may not be enough independent variation in the variables to measure the elasticities effectively, once the linear trend is taken into account. It is useful to know that this kind of thing is occurring, because it shows the data are not ‘rich’ enough (i.e., do not vary enough) to allow changes in tastes to be distinguished from the price and/or income effects.