scholarly journals Checking for model consistency in optimal fingerprinting: a comment

2021 ◽  
Author(s):  
Ross McKitrick
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Critical Condition ◽  
Null Hypothesis ◽  
Generalized Least Squares ◽  
Variance Estimator ◽  
Specification Test ◽  
Consistency Test ◽  
Model Consistency ◽  
Optimal Fingerprinting

AbstractAllen and Tett (1999, herein AT99) introduced a Generalized Least Squares (GLS) regression methodology for decomposing patterns of climate change for attribution purposes and proposed the “Residual Consistency Test” (RCT) to check the GLS specification. Their methodology has been widely used and highly influential ever since, in part because subsequent authors have relied upon their claim that their GLS model satisfies the conditions of the Gauss-Markov (GM) Theorem, thereby yielding unbiased and efficient estimators. But AT99 stated the GM Theorem incorrectly, omitting a critical condition altogether, their GLS method cannot satisfy the GM conditions, and their variance estimator is inconsistent by construction. Additionally, they did not formally state the null hypothesis of the RCT nor identify which of the GM conditions it tests, nor did they prove its distribution and critical values, rendering it uninformative as a specification test. The continuing influence of AT99 two decades later means these issues should be corrected. I identify 6 conditions needing to be shown for the AT99 method to be valid.

Determination of Estimators with Minimum Asymptotic Covariance Matrices

1993 ◽  
Vol 9 (4) ◽  
pp. 633-648 ◽  
Author(s):  
Charles E. Bates ◽  
Halbert White
Keyword(s):  
Least Squares ◽  
Rate Of Convergence ◽  
Asymptotic Variance ◽  
Generalized Least Squares ◽  
Covariance Matrices ◽  
Variance Estimator ◽  
Objective Functions ◽  
Generalized Least Squares Estimator ◽  
Asymptotic Covariance

We give a straightforward condition sufficient for determining the minimum asymptotic variance estimator in certain classes of estimators relevant to econometrics. These classes are relatively broad, as they include extremum estimation with smooth or nonsmooth objective functions; also, the rate of convergence to the asymptotic distribution is not required to be n−½. We present examples illustrating the content of our result. In particular, we apply our result to a class of weighted Huber estimators, and obtain, among other things, analogs of the generalized least-squares estimator for least Lp-estimation, 1 ≤ p < ∞.

scholarly journals Impacts of Climate Factors Influencing Rice Production in Bangladesh

2021 ◽  
pp. 43-52
Author(s):  
Mahmuda Akter ◽  
Md. Mizanur Rahman Sarker
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Climate Extremes ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Rice Production ◽  
Climate Data ◽  
Average Percentage ◽  
Climate Change Pattern ◽  
Rice Yields

This study aims to study the climate change pattern, assess the situation of climate change, finding the influences of climate change on the production of rice, estimating a model between climate change and rice production in Bangladesh. Ordinary Least Squares (OLS), Generalized Least Squares (GLS), Feasible Generalized Least Squares (FGLS) were used in this study to compare the results. This study included all 64 districts of Bangladesh with a time span from 2011 to 2018. It included panel data of the production of Aus rice, Aman rice, Boro rice as well as HYV of each rice (Aus, Aman, Boro) of 64 districts of Bangladesh for agricultural data, temperature, rainfall and humidity of 64 districts for climate data. This study estimates the stochastic production function formulated by Just and Pope (1978, 1979), which allows the effect of inputs on the mean yield to differ from that on yield variance. The results showed that increased climate variability, climate extremes; in particular, exacerbate risk on Rice production in Bangladesh. Rice yields are sensitive to rainfall extremes, with both deficient and surplus rainfall increasing variability. For 1% increase in annual total rainfall, Mean Yield will decrease by 0.139%, 0.141%, 0.132% in OLS, GLS and FGLS method respectively, if other variables remaining the same. For 1% increase in annual average percentage of humidity, Mean Yield increases by 1.352%, 1.340%, 1.362% in OLS, GLS and FGLS method respectively, if other variables remaining the same. for 1% increase in HYV area, Mean Yield increases by 0.831% in OLS, GLS and FGLS method, if other variables remaining the same. Additionally, climate inputs, non-climate input, high yielding variety seeds are found to increase average yield.

scholarly journals A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions

2021 ◽  
Author(s):  
Simone Persiano ◽  
Jose Luis Salinas ◽  
Jery Russell Stedinger ◽  
William H. Farmer ◽  
David Lun ◽  
...  
Keyword(s):  
Least Squares ◽  
Generalized Least Squares ◽  
Least Squares Regression

scholarly journals A Bayesian Climate Change Detection and Attribution Assessment

2005 ◽  
Vol 18 (13) ◽  
pp. 2429-2440 ◽  
Author(s):  
Terry C. K. Lee ◽  
Francis W. Zwiers ◽  
Gabriele C. Hegerl ◽  
Xuebin Zhang ◽  
Min Tsao
Keyword(s):  
Climate Change ◽  
Strong Evidence ◽  
Climate Model ◽  
Consistency Test ◽  
Change Question ◽  
Detection And Attribution ◽  
Complex Process ◽  
Detection Approach ◽  
Limited Length ◽  
Observational Record

Abstract A Bayesian analysis of the evidence for human-induced climate change in global surface temperature observations is described. The analysis uses the standard optimal detection approach and explicitly incorporates prior knowledge about uncertainty and the influence of humans on the climate. This knowledge is expressed through prior distributions that are noncommittal on the climate change question. Evidence for detection and attribution is assessed probabilistically using clearly defined criteria. Detection requires that there is high likelihood that a given climate-model-simulated response to historical changes in greenhouse gas concentration and sulphate aerosol loading has been identified in observations. Attribution entails a more complex process that involves both the elimination of other plausible explanations of change and an assessment of the likelihood that the climate-model-simulated response to historical forcing changes is correct. The Bayesian formalism used in this study deals with this latter aspect of attribution in a more satisfactory way than the standard attribution consistency test. Very strong evidence is found to support the detection of an anthropogenic influence on the climate of the twentieth century. However, the evidence from the Bayesian attribution assessment is not as strong, possibly due to the limited length of the available observational record or sources of external forcing on the climate system that have not been accounted for in this study. It is estimated that strong evidence from a Bayesian attribution assessment using a relatively stringent attribution criterion may be available by 2020.

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