Graham Elliott
Biographic Data
| ID | 8879698 |
|---|---|
| NAME | Graham Elliott |
| GIVEN NAMES | Graham |
| FAMILY NAME | Elliott |
| SIGNATURE | ELLIOTT G |
| AFFILIATIONS | University of California San Diego |
| VERIFIED | No |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1996 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Combining Forecasts - On Why Averaging Beats Optimal Linear Weights
A continuing puzzle in constructing a point forecast by combining individual forecasts is that simple averaging often leads to a smaller out-of-sample mean square error compared to estimating optimal weights (the forecast combination puzzle). Most researchers have suggested that the puzzle is explained by estimation error in computing the weights, typically by estimation of a restricted least squares regression. For this explanation, gains from o…
The Power of Tests for Detecting p -Hacking
A flourishing empirical literature investigates the prevalence of p-hacking based on the distribution of p-values across studies. Interpreting results in this literature requires a careful understanding of the power of methods for detecting p-hacking. We theoretically study the implications of likely forms of p-hacking on the distribution of p-values to understand the power of tests for detecting it. Power can be low and depends crucially on the …
Forecasting Conditional Probabilities of Binary Outcomes under Misspecification
We consider constructing probability forecasts from a parametric binary choice model under a large family of loss functions (“scoring rules”). Scoring rules are weighted averages over the utilities that heterogeneous decision makers derive from a publicly announced forecast (Schervish, 1989). Using analytical and numerical examples, we illustrate howdifferent scoring rules yield asymptotically identical results if the model is correctly specified…
Handbook of economic forecasting
Optimal Power for Testing Potential Cointegrating Vectors With Known Parameters for Nonstationarity
Theory often specifies a particular cointegrating vector among integrated variables, and testing for a unit root in the known cointegrating vector is often required. Although it is common to simply use a univariate test for a unit root for this test, it is known that this does not take into account all available information. We show here that in such testing situations, a family of tests with optimality properties exists. We use this to character…
Estimating Restricted Cointegrating Vectors
This article suggests the use of simple minimum-distance methods to estimate restricted cointegrating vectors. The method directly employs minimum-distance methods on unrestricted cointegrating matrices estimated in the usual way to estimate restricted parameters that are linearly or nonlinearly related to the unrestricted cointegrating vector coefficients. The limiting distribution of the estimates and the usual test for the restrictions are der…
Efficient Tests for an Autoregressive Unit Root
This paper derives the asymptotic power envelope for tests of a unit autoregressive root for various trend specifications and stationary Gaussian autoregressive disturbances. A family of tests is proposed, members of which are asymptotically similar under a general 1(1) null (allowing nonnormality and general dependence) and which achieve the Gaussian power envelope. One of these tests, which is asymptotically point optimal at a power of 50%, is …
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Efficient Tests for an Autoregressive Unit Root
This paper derives the asymptotic power envelope for tests of a unit autoregressive root for various trend specifications and stationary Gaussian autoregressive disturbances. A family of tests is proposed, members of which are asymptotically similar under a general 1(1) null (allowing nonnormality and general dependence) and which achieve the Gaussian power envelope. One of these tests, which is asymptotically point optimal at a power of 50%, is …
Estimating Restricted Cointegrating Vectors
This article suggests the use of simple minimum-distance methods to estimate restricted cointegrating vectors. The method directly employs minimum-distance methods on unrestricted cointegrating matrices estimated in the usual way to estimate restricted parameters that are linearly or nonlinearly related to the unrestricted cointegrating vector coefficients. The limiting distribution of the estimates and the usual test for the restrictions are der…
Optimal Power for Testing Potential Cointegrating Vectors With Known Parameters for Nonstationarity
Theory often specifies a particular cointegrating vector among integrated variables, and testing for a unit root in the known cointegrating vector is often required. Although it is common to simply use a univariate test for a unit root for this test, it is known that this does not take into account all available information. We show here that in such testing situations, a family of tests with optimality properties exists. We use this to character…
Handbook of economic forecasting
Forecasting Conditional Probabilities of Binary Outcomes under Misspecification
We consider constructing probability forecasts from a parametric binary choice model under a large family of loss functions (“scoring rules”). Scoring rules are weighted averages over the utilities that heterogeneous decision makers derive from a publicly announced forecast (Schervish, 1989). Using analytical and numerical examples, we illustrate howdifferent scoring rules yield asymptotically identical results if the model is correctly specified…
The Power of Tests for Detecting p -Hacking
A flourishing empirical literature investigates the prevalence of p-hacking based on the distribution of p-values across studies. Interpreting results in this literature requires a careful understanding of the power of methods for detecting p-hacking. We theoretically study the implications of likely forms of p-hacking on the distribution of p-values to understand the power of tests for detecting it. Power can be low and depends crucially on the …
Combining Forecasts - On Why Averaging Beats Optimal Linear Weights
A continuing puzzle in constructing a point forecast by combining individual forecasts is that simple averaging often leads to a smaller out-of-sample mean square error compared to estimating optimal weights (the forecast combination puzzle). Most researchers have suggested that the puzzle is explained by estimation error in computing the weights, typically by estimation of a restricted least squares regression. For this explanation, gains from o…
Econometrics (4 works) · Mathematics (4 works) · Monetary Policy and Economic Impact (4 works) · Statistics (4 works) · Cointegration (3 works) · Financial Risk and Volatility Modeling (2 works) · Unit root (2 works) · Advanced Malware Detection Techniques (1 work) · Advanced Statistical Methods and Models (1 work) · Applied Mathematics (1 work)