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Author:Berkowitz, Jeremy 

Working Paper
On identification of continuous time stochastic processes
In this note we delineate conditions under which continuous time stochastic processes can be identified from discrete data. The identification problem is approached in a novel way. The distribution of the observed stochastic process is expressed as the underlying true distribution, f, transformed by some operator, T. Using a generalization of the Taylor series expansion, the transformed function T f can often be expressed as a linear combination of the original function f. By combining the information across a large number of such transformations, the original measurable function of interest can be recovered.
AUTHORS: Berkowitz, Jeremy
DATE: 2000

Working Paper
Long-horizon exchange rate predictability?
Several authors have recently investigated the predictability of exchange rates by fitting a sequence of long-horizon error-correction regressions. We show that such a procedure gives rise to spurious evidence of predictive power. A simulation study demonstrates that even when using this technique on two independent series, estimates and diagnostic statistics suggest a high degree of predictability of the dependent variable. We apply a simple modification of the long-horizon regression due to Jegadeesh (1991), which may provide more accurate inferences for researchers interested in comparing short and long-run predictability of U.S. dollar exchange rates.
AUTHORS: Giorgianni, Lorenzo; Berkowitz, Jeremy
DATE: 1996

Working Paper
Bankruptcy exemptions and the market for mortgage loans
The recent explosion in personal bankruptcy filings has motivated research into whether credit markets are being adversely affected by generous legal provisions. Empirically, this question is examined by comparing credit conditions and bankruptcy exemptions across states. We note that the literature has focused on aggregate household credit, making no distinction between secured and unsecured credit. We argue that such aggregation obscures important differences in forms of credit. Most significantly, property exemptions do not prevent the home mortgage lender from foreclosing on the home if not fully repaid.
AUTHORS: Berkowitz, Jeremy; Hines, Richard
DATE: 1998

Working Paper
Generalized spectral estimation
This paper provides a framework for estimating parameters in a wide class of dynamic rational expectations models. The framework recognizes that RE models are often meant to match the data only in limited ways. In particular, interest may focus on a subset of frequencies. This paper designs a frequency domain version of GMM. The estimator has several advantages over traditional GMM. Aside from allowing band-restricted estimation, it does not require making arbitrary instrument or weighting matrix choices. The framework also includes least squares, maximum likelihood, and band restricted maximum likelihood as special cases.
AUTHORS: Berkowitz, Jeremy
DATE: 1996

Working Paper
Generalized Spectral Estimation
This paper provides a famework for estimating parameters in a wide class of dynamic rational expectations models. The framework recognizes that RE models are often meant to match the data only in limited ways. In particular, interest may focus on a subset of frequencies. This paper designs a frequency domain version of GMM. The estimator has several advantages over traditional GMM. Aside from allowing band-restricted estimation, it does not require making arbitrary instrument or weighting matrix choices. The framework also includes least squares, maximum likelihood, and band restricted maximum likelihood as special cases.
AUTHORS: Berkowitz, Jeremy

Working Paper
Dealer polling in the presence of possibly noisy reporting
The value of a vast array of financial assets are functions of rates or prices determined in OTC, interbank, or other off-exchange markets. In order to price such derivative assets, underlying rate and price indexes are routinely sampled and estimated. To guard against misreporting, whether unintentional or for market manipulation, many standard contracts utilize a technique known as trimmed-means. This paper points out that this polling problem falls within the statistical framework of robust estimation. Intuitive criteria for choosing among robust valuation procedures are discussed. In particular, the approach taken is to minimize the worst-case scenario arising from a false report. The finite sample performance of the procedures that qualify, the trimmed-mean and the Huber-estimator, are examined in a set of simulation experiments.
AUTHORS: Berkowitz, Jeremy
DATE: 1998

Working Paper
Recent developments in bootstrapping time series
In recent years, several new parametric and nonparametric bootstrap methods have been proposed for time series data. Which of these methods should applied researchers use? We provide evidence that for many applications in time series econometrics parametric methods are more accurate, and we identify directions for future research on improving nonparametric methods. We explicitly address the important, but often neglected issue of model selection in bootstrapping. In particular, we emphasize the advantages of the AIC over other lag order selection criteria and the need to account for lag order uncertainty in resampling. We also show that the block size plays an important role in determining the success of the block bootstrap, and we propose a data-based block size selection procedure.
AUTHORS: Berkowitz, Jeremy; Kilian, Lutz
DATE: 1996

Working Paper
Evaluating the forecasts of risk models
The forecast evaluation literature has traditionally focused on methods for assessing point-forecasts. However, in the context of risk models, interest centers on more than just a single point of the forecast distribution. For example, value-at-risk (VaR) models, which are currently in extremely wide, use form interval forecasts. Many other important financial calculations also involve estimates not summarized by a point-forecast. Although some techniques are currently available for assessing interval and density forecasts, none are suitable for sample sizes typically available. This paper suggests a new approach to evaluating such forecasts. It requires evaluation of the entire forecast distribution, rather than a value-at-risk quantity. The information content of forecast distributions combined with ex post loss realizations is enough to construct a powerful test even with sample sizes as small as 100.
AUTHORS: Berkowitz, Jeremy
DATE: 1999

Working Paper
On the finite-sample accuracy of nonparametric resampling algorithms for economic time series
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.
AUTHORS: Berkowitz, Jeremy; Biegean, Ionel; Kilian, Lutz
DATE: 1999

Working Paper
Recent Developments in Bootstrapping Time Series
In recent years, several new parametric and nonparametric bootstrap methods have been proposed for time series data. Which of these methods should applied researchers use? We provide evidence that for many applications in time series econometrics parametric methods are more accurate, and we identify directions for future research on improving nonparametric methods. We explicitly address the important, but often neglected issue of model selection in bootstrapping. In particular, we emphasize the advantages of the AIC over other lag order selection criteria and the need to account for lag order uncertainty in resampling. We also show that the block size plays an important role in determining the success of the block bootstrap, and we propose a data-based block size selection procedure.
AUTHORS: Berkowitz, Jeremy; Kilian, Lutz

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