Marshall B Reinsdorf
Biographic Data
| ID | 8879820 |
|---|---|
| NAME | Marshall B Reinsdorf |
| GIVEN NAMES | Marshall B |
| FAMILY NAME | Reinsdorf |
| SIGNATURE | REINSDORF M B |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1998 |
| LATEST PUBLICATION YEAR | 1999 |
| H-INDEX | 0 |
Using Scanner Data to Construct CP1 Basic Component Indexes
This article considers how scanner data could be used in constructing component indexes for the U.S. Consumer Price Index. One product, coffee, in two cities generates over 1.8 million observations in just over two years, so coping with the sheer volume of data would be a challenge. Some other findings are (1) some aggregation of prices into “unit-value” averages is necessary for practical reasons and to avoid bias, (2) chained Laspeyres indexes …
Using Scanner Data to Construct CPI Basic Component Indexes
This article considers how scanner data could be used in constructing component indexes for the U.S. Consumer Price Index. One product, coffee, in two cities generates over 1.8 million observations in just over two years, so coping with the sheer volume of data would be a challenge. Some other findings are (1) some aggregation of prices into 'unit-value' averages is necessary for practical reasons and to avoid bias, (2) chained Laspeyres indexes …
Formula Bias and Within-stratum Substitution Bias in the U.S. CPI
In 1978 BLS adopted an estimator for U.S. CPI component indexes that incorporates scientific samples of outlets and varieties and that allows the calculation of reliable standard errors. Consumer substitution of outlets and varieties offering better values could cause bias in these indexes. A more important source of bias is, however, a tendency to give excessive weight to items whose initial price is temporarily low. The empirical evidence is st…
No prominent works on this page.
Formula Bias and Within-stratum Substitution Bias in the U.S. CPI
In 1978 BLS adopted an estimator for U.S. CPI component indexes that incorporates scientific samples of outlets and varieties and that allows the calculation of reliable standard errors. Consumer substitution of outlets and varieties offering better values could cause bias in these indexes. A more important source of bias is, however, a tendency to give excessive weight to items whose initial price is temporarily low. The empirical evidence is st…
Using Scanner Data to Construct CP1 Basic Component Indexes
This article considers how scanner data could be used in constructing component indexes for the U.S. Consumer Price Index. One product, coffee, in two cities generates over 1.8 million observations in just over two years, so coping with the sheer volume of data would be a challenge. Some other findings are (1) some aggregation of prices into “unit-value” averages is necessary for practical reasons and to avoid bias, (2) chained Laspeyres indexes …
Using Scanner Data to Construct CPI Basic Component Indexes
This article considers how scanner data could be used in constructing component indexes for the U.S. Consumer Price Index. One product, coffee, in two cities generates over 1.8 million observations in just over two years, so coping with the sheer volume of data would be a challenge. Some other findings are (1) some aggregation of prices into 'unit-value' averages is necessary for practical reasons and to avoid bias, (2) chained Laspeyres indexes …
Econometrics (3 works) · Economics (3 works) · Mathematics (3 works) · Statistics (3 works) · Computer Science (2 works) · Economics of Agriculture and Food Markets (2 works) · Monetary Policy and Economic Impact (2 works) · Efficiency Analysis Using DEA (1 works) · Geology (1 works) · Geology (1 works)