What is a pooled cross-sectional time series?

TERRY E. DIELMAN* A data base that provides a multivariate statistical histo- ry for each of a number of individual entities is called a pooled cross-sectional and time series data base in the econometrics literature. In marketing and survey litera- ture the terms panel data or longitudinal data are often used.

What is a pooled cross sectional analysis?

Definition 1 (Pooled cross-section data) Randomly sampled cross sections of. individuals at different points in time. Example: Current population survey (CPS) in 1978 and 1988. Definition 2 (Panel Data) Observe cross sections of the same individuals at. different points in time.

What is cross-sectional and time series regression?

In statistics and econometrics, a cross-sectional regression is a type of regression in which the explained and explanatory variables are all associated with the same single period or point in time.

How would you differentiate cross-sectional time series pooled and panel data?

To answer the question an example of either type of data would help, e.g. panel data follows the same units over time (like a household survey such as the panel study of income dynamics) whereas pooled data is data over different years but from different cross sections (such as the current population study).

What is a pooled regression model?

Pooled regression is standard ordinary least squares (OLS) regression without any cross-sectional or time effects. The error structure is simply , where the are independently and identically distributed (iid) with zero mean and variance .

Can regression be done for cross-sectional data?

When we have more than a single variable in cross-sectional applications, we can use regression tools in much the same way as for time-series data, but we have to be even more cautious about causal interpretation.

What is pooled data in econometrics?

Pooled data is a mixture of time series data and cross-section data. One example is GNP per capita of all European countries over ten years. Panel, longitudinal or micropanel data is a type that is pooled data of nature.

What is a pooled time series analysis?

Pooled Times Series Analysis combines time series and cross- sectional data to provide the researcher with an efficient method of analysis and improved estimates of the population being studied.

Is CAPM a time series regression?

The tests of the CAPM provided by the time-series regression (2) and the cross-section regression (5) differ in terms of what is used as explanatory variable. In the time-series regression (2) the explanatory variable is the excess market return, RMt – Rft, and we estimate βi.

Is cross-sectional regression linear?

The time series cross-sectional regression analysis (TSCSREG) procedure analyzes linear econometric models that often arise when cross-sectional and time series data are combined. The TSCSREG procedure analyzes panel data sets that consist of a number of sets of time series data on each of several individuals.

What is a CAPM regression?

• The CAPM puts structure –i.e., how investors form efficient portfolios- to Markowitz’s (1952) mean-variance optimization theory. • The CAPM assumes only one source of systematic risk: Market Risk.

What is pooled time series and cross sectional data?

Pooled Time Series and Cross-Sectional Data. A cross-sectional dataset consists of a sample of individuals, households, firms, cities, states, countries, or any other micro- or macroeconomic unit taken at a given point in time. Sometimes the data on all units do not correspond to precisely the same time period.

What is a time series dataset?

A time series dataset contains information on a variable or a set of variables over time. Examples of time series data include stock prices, money supply, the consumer price index, gross domestic product ( GDP ), annual homicide rates, and automobile sales figures. Figure 2 illustrates the time series for NASDAQ on July 7, 2006.

Is there a period effect for pphpy regression?

I first regressed PPHPY on year and year dummies (leaving the intercept as 0 to avoid perfect multicollinearity). This gave me period effects for the aggregated data (ie something like a period effect across all age groups, I think).

Why is time series data important in macroeconomics?

Data on individuals, households, firms, and cities at a given point in time are important for testing microeconomic hypotheses and evaluating economic policies. A time series dataset contains information on a variable or a set of variables over time.