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Related Concept Videos

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first column of the Routh...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
First Derivative Test: Problem Solving01:25

First Derivative Test: Problem Solving

Imagine an asset price that crashes to a low point, rebounds sharply as bargain-hunters step in, and then gradually declines. Such behavior can be modeled with a smooth function whose turning points represent locally overvalued and undervalued regions. A convenient example that captures rebound followed by decay is:The high and low points of this curve are identified using the first derivative test, which determines where the function changes from increasing to decreasing or vice versa. To...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
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Related Experiment Video

Updated: May 9, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Truncated Product Methods for Panel Unit Root Tests.

Xuguang Sheng1, Jingyun Yang

  • 1Department of Economics, American University, Washington, DC 20016, USA.

Oxford Bulletin of Economics and Statistics
|July 23, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces novel panel unit root tests for analyzing forecast precision. Findings suggest professional forecasters may not adhere to optimal Bayesian updating methods.

Keywords:
Density ForecastP-valuePanel Unit RootSieve BootstrapTruncated Product Method

Related Experiment Videos

Last Updated: May 9, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Econometrics
  • Time Series Analysis
  • Statistical Inference

Background:

  • Panel unit root testing is crucial for analyzing economic data.
  • Existing methods may not fully capture cross-section dependence.
  • Assessing forecast accuracy and updating behavior is vital for economic modeling.

Purpose of the Study:

  • To propose two new panel unit root tests.
  • To address limitations in existing methods regarding p-value correlation and cross-section dependence.
  • To evaluate forecast precision in professional forecasters.

Main Methods:

  • Development of two novel panel unit root tests utilizing Zaykin et al. (2002)'s truncated product method.
  • The first test assumes constant correlation between p-values.
  • The second test employs sieve bootstrap for general cross-section dependence.

Main Results:

  • Monte Carlo simulations demonstrate good size and power for the proposed tests, even with large p-values.
  • Application to real GDP and inflation forecasts reveals suboptimal Bayesian updating by professional forecasters.
  • Evidence suggests potential inefficiencies in how forecasters adjust their precision.

Conclusions:

  • The new panel unit root tests are effective and powerful.
  • Professional forecasters' updating of forecast precision may deviate from optimal Bayesian principles.
  • Further research can explore the implications of these findings for economic forecasting and policy.