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economic historybritish colonialeconomic developmentnative administrationadministration nigeriarule africacolonial governmentcolonial sequencesequence chronologicalchronological commentarycommentary britishcolonial policygovernment ethiopianigeria historyhistory easteast africaafrica volvol bilanzbilanz kolonialismuskolonialismus principlesprinciples nativenigeria selectedselected documentsdocuments racerace politicspolitics kenyakenya kenyakenya questionquestion africanafrican answeranswer yesterdayyesterday rulersrulers makingmaking britishcolonial serviceservice colonialcolonial reckoningreckoning endend imperialimperial ruleafrica lightlight britishbritish experienceexperience colonialgovernment annotatedannotated readingreading listlist britishgovernment comparativecomparative materialmaterial foreignforeign empiresempires protectoratesprotectorates southsouth africaafrica questionquestion transfertransfer unionunion colonialpolicy especiallyespecially africaafrica governmentethiopia problemsproblems indirectindirect ruleafrica tshekeditshekedi khamakhama bechuanaland

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Years of publications: 1400 - 2022

6 records from EconBiz based on author GND ID

1. On the finite-sample accuracy of nonparametric resampling algorithms for economic time series

Berkowitz, Jeremy; Birgean, Ionel; Kilian, Lutz;
1999
Type: Arbeitspapier; Working Paper; Graue Literatur; Non-commercial literature;
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2. On the Finite Sample Accuracy of Nonparametric Resampling Algorithms for Economic Time Series

abstract

In recent years, there has been increasing interest in nonparametric bootstrap inference for economic time series. Nonparametric resampling techniques help protect against overly optimistic inference in time series models of unknown structure. They are particularly useful for evaluating the fit of dynamic economic models in terms of their spectra, impulse responses, and related statistics, because they do not require a correctly specified economic model. Notwithstanding the potential advantages of nonparametric bootstrap methods, their reliability in small samples is questionable. In this paper, we provide a benchmark for the relative accuracy of several nonparametric resampling algorithms based on ARMA representations of four macroeconomic time series. For each algorithm, we evaluate the effective coverage accuracy of impulse response and spectral density bootstrap confidence intervals for standard sample sizes. We find that the autoregressive sieve approach based on the encompassing model is most accurate. However, care must be exercised in selecting the lag order of the autoregressive approximation

Berkowitz, Jeremy; Birgean, Ionel; Kilian, Lutz;
1999
Availability: Link Link
Citations: 6 (based on OpenCitations)

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6 records from EconBiz based on author GND ID

1. On the finite-sample accuracy of nonparametric resampling algorithms for economic time series

2. On the Finite Sample Accuracy of Nonparametric Resampling Algorithms for Economic Time Series

3. Data-driven nonparametric spectral density estimators for economic time series : a Monte Carlo study

4. Data-driven nonparametric spectral density estimators for economic time series : a Monte Carlo study

5. On the finite-sample accuracy of nonparametric resampling algorithms for economic time series

6. On the finite-sample accuracy of nonparametric resampling algorithms for economic time series

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