Replication data for: An Effective Approach to the Repeated Cross-Sectional Design
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Repeated cross-sectional (RCS) designs are distinguishable from true panels and pooled cross-sectional-time-series (PCSTS) since cross-sectional units – e.g. individual survey respondents – appear but once in the data. This poses two serious challenges. First, as with PCSTS, autocorrelation threatens inferences. However, common solutions like differencing and using a lagged dependent variable are not possible with RCS since lags for i cannot be used. Second, although RCS designs contain information that allows both aggregate- and individual-level analyses, available methods – from pooled OLS to PCSTS to time series – force researchers to choose one level of analysis. The PCSTS toolkit does not provide an appropriate solution and we offer one here: double filtering with ARFIMA methods to account for autocorrelation in longer RCS followed' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Multilevel models","Rolling cross section"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Repeated cross-sectional (RCS) designs are distinguishable from true panels and pooled cross-sectional-time-series (PCSTS) since cross-sectional units – e.g. individual survey respondents – appear but once in the data. This poses two serious challenges. First, as with PCSTS, autocorrelation threatens inferences. However, common solutions like differencing and using a lagged dependent variable are not possible with RCS since lags for i cannot be used. Second, although RCS designs contain information that allows both aggregate- and individual-level analyses, available methods – from pooled OLS to PCSTS to time series – force researchers to choose one level of analysis. The PCSTS toolkit does not provide an appropriate solution and we offer one here: double filtering with ARFIMA methods to account for autocorrelation in longer RCS followed' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Repeated cross-sectional (RCS) designs are distinguishable from true panels and pooled cross-sectional-time-series (PCSTS) since cross-sectional units – e.g. individual survey respondents – appear but once in the data. This poses two serious challenges. First, as with PCSTS, autocorrelation threatens inferences. However, common solutions like differencing and using a lagged dependent variable are not possible with RCS since lags for i cannot be used. Second, although RCS designs contain information that allows both aggregate- and individual-level analyses, available methods – from pooled OLS to PCSTS to time series – force researchers to choose one level of analysis. The PCSTS toolkit does not provide an appropriate solution and we offer one here: double filtering with ARFIMA methods to account for autocorrelation in longer RCS followed
Multilevel models; Rolling cross section