The Nature of Econometrics and Economic data the next. While most econometric procedures can be used with both cross-sectional and time series data, more needs to be done in specifying econometric models for time series data before standard econometric methods can be justified. In addition, modifi- cations and embellishments to standard econometric techniques have been developed account for and exploit the dependent nature of economic time series and to address other issues, such as the fact that some economic variables tend to display clear trends over time Another feature of time series data that can require special attention is the data fre quency at which the data are collected In economics, the most common frequencies are daily, weekly, monthly, quarterly, and annually. Stock prices are recorded at daily intervals(excluding Saturday and Sunday). The money supply in the U.S. economy reported weekly. Many macroeconomic series are tabulated monthly, including infla tion and employment rates. Other macro series are recorded less frequently, such as every three months(every quarter). Gross domestic product is an important example of a quarterly series. Other time series, such as infant mortality rates for states in the United States, are available only on an annual basis Many weekly, monthly, and quarterly economic time series display a strong seasonal pattern, which can be an important factor in a time series analysis. For ex- ample, monthly data on housing starts differs across the months simply due to changin eather conditions. We will learn how to deal with seasonal time series in Chapter 10 Table 1.3 ins a time series data set obtained from an article by Castillo- Freeman and Freeman(1992) on minimum wage effects in Puerto Rico. The earliest year in the data set is the first observation, and the most recent year available is the last Ttable 1.3 Minimum Wage, Unemployment, and Related Data for Puerto Rico obsno year agcol unemp 1950 0.20 20.1 154 878.7 5 1952 0.23 22.6 4.8 0159 1986 281.6 1987 3.35 58.2 16.8 4496.7the next. While most econometric procedures can be used with both cross-sectional and time series data, more needs to be done in specifying econometric models for time series data before standard econometric methods can be justified. In addition, modifi￾cations and embellishments to standard econometric techniques have been developed to account for and exploit the dependent nature of economic time series and to address other issues, such as the fact that some economic variables tend to display clear trends over time. Another feature of time series data that can require special attention is the data fre￾quency at which the data are collected. In economics, the most common frequencies are daily, weekly, monthly, quarterly, and annually. Stock prices are recorded at daily intervals (excluding Saturday and Sunday). The money supply in the U.S. economy is reported weekly. Many macroeconomic series are tabulated monthly, including infla￾tion and employment rates. Other macro series are recorded less frequently, such as every three months (every quarter). Gross domestic product is an important example of a quarterly series. Other time series, such as infant mortality rates for states in the United States, are available only on an annual basis. Many weekly, monthly, and quarterly economic time series display a strong seasonal pattern, which can be an important factor in a time series analysis. For ex￾ample, monthly data on housing starts differs across the months simply due to changing weather conditions. We will learn how to deal with seasonal time series in Chapter 10. Table 1.3 contains a time series data set obtained from an article by Castillo￾Freeman and Freeman (1992) on minimum wage effects in Puerto Rico. The earliest year in the data set is the first observation, and the most recent year available is the last Chapter 1 The Nature of Econometrics and Economic Data 9 Table 1.3 Minimum Wage, Unemployment, and Related Data for Puerto Rico obsno year avgmin avgcov unemp gnp 1 1950 0.20 20.1 15.4 878.7 2 1951 0.21 20.7 16.0 925.0 3 1952 0.23 22.6 14.8 1015.9 37 1986 3.35 58.1 18.9 4281.6 38 1987 3.35 58.2 16.8 4496.7 d 7/14/99 4:34 PM Page 9