scholarly journals Hidden Error Variance Theory. Part II: An Instrument That Reveals Hidden Error Variance Distributions from Ensemble Forecasts and Observations

2013 ◽  
Vol 141 (5) ◽  
pp. 1469-1483 ◽  
Author(s):  
Craig H. Bishop ◽  
Elizabeth A. Satterfield ◽  
Kevin T. Shanley
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
True Error ◽  
Ensemble Forecasts ◽  
Optimal Weights ◽  
Long Time ◽  
Forecast Error Variance ◽  
Hidden Parameters ◽  
Variance Parameters ◽  
Using Data

Abstract In Part I of this study, a model of the distribution of true error variances given an ensemble variance is shown to be defined by six parameters that also determine the optimal weights for the static and flow-dependent parts of hybrid error variance models. Two of the six parameters (the climatological mean of forecast error variance and the climatological minimum of ensemble variance) are straightforward to estimate. The other four parameters are (i) the variance of the climatological distribution of the true conditional error variances, (ii) the climatological minimum of the true conditional error variance, (iii) the relative variance of the distribution of ensemble variances given a true conditional error variance, and (iv) the parameter that defines the mean response of the ensemble variances to changes in the true error variance. These parameters are hidden because they are defined in terms of condition-dependent forecast error variance, which is unobservable if the condition is not sufficiently repeatable. Here, a set of equations that enable these hidden parameters to be accurately estimated from a long time series of (observation minus forecast, ensemble variance) data pairs is presented. The accuracy of the equations is demonstrated in tests using data from long data assimilation cycles with differing model error variance parameters as well as synthetically generated data. This newfound ability to estimate these hidden parameters provides new tools for assessing the quality of ensemble forecasts, tuning hybrid error variance models, and postprocessing ensemble forecasts.

scholarly journals Regional Ensemble Forecasts Using the Ensemble Transform Technique

2009 ◽  
Vol 137 (1) ◽  
pp. 288-298 ◽  
Author(s):  
Craig H. Bishop ◽  
Teddy R. Holt ◽  
Jason Nachamkin ◽  
Sue Chen ◽  
Justin G. McLay ◽  
...  
Keyword(s):  
Forecast Error ◽  
Initial Perturbation ◽  
Error Variance ◽  
Precipitation Forecast ◽  
Initial Time ◽  
Naval Research ◽  
Ensemble Forecasts ◽  
Breeding Technique ◽  
Forecast Error Variance ◽  
Ensemble Transform

Abstract A computationally inexpensive ensemble transform (ET) method for generating high-resolution initial perturbations for regional ensemble forecasts is introduced. The method provides initial perturbations that (i) have an initial variance consistent with the best available estimates of initial condition error variance, (ii) are dynamically conditioned by a process similar to that used in the breeding technique, (iii) add to zero at the initial time, (iv) are quasi-orthogonal and equally likely, and (v) partially respect mesoscale balance constraints by ensuring that each initial perturbation is a linear sum of forecast perturbations from the preceding forecast. The technique is tested using estimates of analysis error variance from the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) and the Navy’s regional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) over a 3-week period during the summer of 2005. Lateral boundary conditions are provided by a global ET ensemble. The tests show that the ET regional ensemble has a skillful mean and a useful spread–skill relationship in mass, momentum, and precipitation variables. Diagnostics indicate that ensemble variance was close to, but probably a little less than, the forecast error variance for wind and temperature variables, while precipitation ensemble variance was significantly smaller than precipitation forecast error variance.

The influence of economic policy uncertainty and geopolitical risk on US citizens overseas air passenger travel by regional destination

2020 ◽  
pp. 135481662098119
Author(s):  
James E Payne ◽  
Nicholas Apergis
Keyword(s):  
Economic Policy ◽  
Forecast Error ◽  
Error Variance ◽  
Variance Decomposition ◽  
Air Travel ◽  
Policy Uncertainty ◽  
Economic Policy Uncertainty ◽  
Passenger Travel ◽  
Forecast Error Variance

This research note extends the literature on the role of economic policy uncertainty and geopolitical risk on US citizens overseas air travel through the examination of the forecast error variance decomposition of total overseas air travel and by regional destination. Our empirical findings indicate that across regional destinations, US economic policy uncertainty explains more of the forecast error variance of US overseas air travel, followed by geopolitical risk with global economic policy uncertainty explaining a much smaller percentage of the forecast error variance.

scholarly journals Predicting Water Availability in Water Bodies under the Influence of Precipitation and Water Management Actions Using VAR/VECM/LSTM

Climate ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 144
Author(s):  
Harleen Kaur ◽  
Mohammad Afshar Alam ◽  
Saleha Mariyam ◽  
Bhavya Alankar ◽  
Ritu Chauhan ◽  
...  
Keyword(s):  
Water Management ◽  
Short Term Memory ◽  
Forecast Error ◽  
Error Variance ◽  
Water Levels ◽  
Water Bodies ◽  
Economic Activities ◽  
Management Actions ◽  
Forecast Error Variance ◽  
Water Treatments

Recently, awareness about the significance of water management has risen as population growth and global warming increase, and economic activities and land use continue to stress our water resources. In addition, global water sustenance efforts are crippled by capital-intensive water treatments and water reclamation projects. In this paper, a study of water bodies to predict the amount of water in each water body using identifiable unique features and to assess the behavior of these features on others in the event of shock was undertaken. A comparative study, using a parametric model, was conducted among Vector Autoregression (VAR), the Vector Error Correction Model (VECM), and the Long Short-Term Memory (LSTM) model for determining the change in water level and water flow of water bodies. Besides, orthogonalized impulse responses (OIR) and forecast error variance decompositions (FEVD) explaining the evolution of water levels and flow rates, the study shows the significance of VAR/VECM models over LSTM. It was found that on some water bodies, the VAR model gave reliable results. In contrast, water bodies such as water springs gave mixed results of VAR/VECM.

scholarly journals Oil Prices and the U.S. Dollar Exchange Rate: Evidence from the Monetary Model

2018 ◽  
Vol 10 (4) ◽  
pp. 17
Author(s):  
Moayad H. Al Rasasi
Keyword(s):  
Exchange Rate ◽  
Forecast Error ◽  
Negative Relationship ◽  
Impulse Response Function ◽  
Decomposition Analysis ◽  
Oil Prices ◽  
Error Variance ◽  
Monetary Model ◽  
Forecast Error Variance ◽  
Positive Shock

This paper analyzes how changes in global oil prices affect the US dollar (USD) exchange rate based on the monetary model of exchange rate. We find evidence indicating a negative relationship between oil prices and the USD exchange rate against 12 currencies. Specifically, the analysis of the impulse response function shows that the depreciation rate of the USD exchange rate ranges between 0.002 and 0.018 percentage points as a result of a one-standard deviation positive shock to the real price of crude oil. In the same vein, the forecast error variance decomposition analysis reveals that variation in the USD exchange rate is largely attributable to changes in the price of oil rather than monetary fundamentals. In last, the out-of-sample forecast exercise indicates that oil prices enhance the predictability power of the monetary model of exchange rate.

scholarly journals KAJIAN AKTIVITAS EKONOMI LUAR NEGERI INDONESIA TERHADAP PERTUMBUHAN EKONOMI INDONESIA PERIODE 1998-2014

2017 ◽  
Vol 9 (2) ◽  
pp. 119
Author(s):  
Ryan Hawari ◽  
Fitri Kartiasih
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Exchange Rate ◽  
Direct Investment ◽  
Forecast Error ◽  
Foreign Debt ◽  
Trade Openness ◽  
Impulse Response Function ◽  
Error Variance ◽  
Forecast Error Variance

Indonesia is a developing country which adopts an “open economic”. That caused Indonesia economic is strongly influenced by factors that come from outside of Indonesia. External factors in this research is referred to foreign debt, foreign direct investment, trade openness and exchange rate of rupiah with USD. The analytical method in this research used Vector Error Correction Model (VECM) which will focused on Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD). Based on result of IRF, exchange rate had a positive effect to economic growth, while foreign debt, foreign direct investment and trade openness had a negative effect to economic growth. Based on result of FEVD, shock on economic growth in Indonesia affected by economic growth itself (43.21%), followed by foreign debt (26.30%), trade openness (14.16%), foreign direct investment (8.29%) and exchange rate (8.04%) Keywords: economic growth, trade openness, VECM, IRF, FEVD

THE PRICE OF COCAINE AND THE COLOMBIAN PESO: AN EMPIRICAL INVESTIGATION

2019 ◽  
Vol 19 (03) ◽  
pp. 1950015
Author(s):  
ALEXI THOMPSON ◽  
YAYA SISSOKO
Keyword(s):  
Forecast Error ◽  
Control Policy ◽  
Error Variance ◽  
Decomposition Methods ◽  
Nominal Exchange Rate ◽  
Policy Implications ◽  
Vector Error Correction ◽  
National Drug ◽  
Forecast Error Variance ◽  
National Drug Control Policy

While the underground economy is not explicitly included in the measure of (GDP), the cocaine trade has been a major source of revenue for Colombia. Using quarterly cocaine prices from 1982 to 2007 published by the Office of National Drug Control Policy, this paper uses vector error correction and forecast error variance decomposition methods to look at the relationship between cocaine prices and the peso/$ nominal exchange rate. Our results indicate cocaine prices affect the value of the Colombian peso, which leads to some interesting policy implications.

scholarly journals Alternative Model Selection Using Forecast Error Variance Decompositions in Wholesale Chicken Markets

2009 ◽  
Vol 41 (1) ◽  
pp. 227-240 ◽  
Author(s):  
Andrew M. McKenzie ◽  
Harold L. Goodwin ◽  
Rita I. Carreira
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Autoregressive Models ◽  
Model Specification ◽  
Decomposition Approach ◽  
Vector Autoregressive ◽  
Forecasting Performance ◽  
Causality Tests ◽  
Forecast Error Variance ◽  
Variance Decompositions

Although Vector Autoregressive models are commonly used to forecast prices, specification of these models remains an issue. Questions that arise include choice of variables and lag length. This article examines the use of Forecast Error Variance Decompositions to guide the econometrician's model specification. Forecasting performance of Variance Autoregressive models, generated from Forecast Error Variance Decompositions, is analyzed within wholesale chicken markets. Results show that the Forecast Error Variance Decomposition approach has the potential to provide superior model selections to traditional Granger Causality tests.

scholarly journals Impacts of Sea Surface Temperature Uncertainty on the Western North Pacific Subtropical High (WNPSH) and Rainfall

2011 ◽  
Vol 26 (3) ◽  
pp. 371-387 ◽  
Author(s):  
Xiaodong Hong ◽  
Craig H. Bishop ◽  
Teddy Holt ◽  
Larry O’Neill
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Pacific ◽  
Western North Pacific ◽  
Forecast Error ◽  
Error Variance ◽  
Satellite Observations ◽  
Sea Surface ◽  
Subtropical High ◽  
Forecast Error Variance

Abstract This paper examines the sensitivity of short-term forecasts of the western North Pacific subtropical high (WNPSH) and rainfall to sea surface temperature (SST) uncertainty using the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). A comparison of analyzed SSTs with satellite observations of SST indicates that SST analysis errors are particularly pronounced on horizontal scales from 100 to 200 km, similar to the mesoscale eddy scales in the Kuroshio region. Since significant oceanic variations occur on these scales, it is of interest to examine the effects of representing this small-scale uncertainty with random, scale-dependent perturbations. An SST ensemble perturbation generation technique is used here that enables temporal and spatial correlations to be controlled and produces initial SST fields comparable to satellite observations. The atmospheric model develops large uncertainty in the Korea and Japan area due to the fluctuation in the horizontal pressure gradient caused by the location of the WNPSH. This, in turn, increases the variance of the low-level jet (LLJ) over southeast China, resulting in large differences in the moist transport flux from the tropical ocean and subsequent rainfall. Validation using bin-mean statistics shows that the ensemble forecast with the perturbed SST better distinguishes large forecast error variance from small forecast error variance. The results suggest that using the SST perturbation as a proxy for the ocean ensemble in a coupled atmosphere and ocean ensemble system is feasible and computationally efficient.

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