Bias Correction Technique Research Articles

Does Self-Control Training Improve Self-Control? A Meta-Analysis

Self-control is positively associated with a host of beneficial outcomes. Therefore, psychological interventions that reliably improve self-control are of great societal value. A prominent idea suggests that training self-control by repeatedly overriding dominant responses should lead to broad improvements in self-control over time. Here, we conducted a random-effects meta-analysis based on robust variance estimation of the published and unpublished literature on self-control training effects. Results based on 33 studies and 158 effect sizes revealed a small-to-medium effect of g = 0.30, confidence interval (CI95) [0.17, 0.42]. Moderator analyses found that training effects tended to be larger for (a) self-control stamina rather than strength, (b) studies with inactive compared to active control groups, (c) males than females, and (d) when proponents of the strength model of self-control were (co)authors of a study. Bias-correction techniques suggested the presence of small-study effects and/or publication bias and arrived at smaller effect size estimates (range: gcorrected = .13 to .24). The mechanisms underlying the effect are poorly understood. There is not enough evidence to conclude that the repeated control of dominant responses is the critical element driving training effects.

Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean

In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981–2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter.

Quantification of Flood Damage under Potential Climate Change Impacts in Central Vietnam

AbstractThe quantification of economic losses due to floods becomes necessary in flood management. In this context, the study aims to assess insights into flood damage under potential impacts of climate change in central Vietnam. The HadGEM3–RA Regional Climate Model (RCM) under Representative Concentration Pathways (RCP) 4.5 and 8.5 climate change scenarios was used in this study. A bias correction technique for adjusting RCM simulations was applied to correct the climate input data for systematic statistical deviations from observed data. In addition, a coupling of the HEC‐HMS and HEC‐RAS hydrodynamic models and the flood damage reduction analysis model (HEC‐FDA) was applied to assess the level of flood hazard and damage existing in central Vietnam. The findings showed that the weather in central Vietnam will be warmer in the future with the average temperature increasing from 0.2 to 0.8°C, and annual rainfall will increase from 3.5 to 6.8%. The water level and runoff will also increase from 0.1 to 0.3 m and 10–30%, respectively. Flood damage was estimated to be US$18.8–20.7 million from the 2020s to the 2080s. Finally, the findings obtained are useful for the decision‐making process in flood management and economic development in Vietnam. Copyright © 2017 John Wiley & Sons, Ltd.

Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

Abstract. In this study, a regional coupled climate–chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting – Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006–2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2 m temperature (T2) in this study (with a mean bias of −0.6 °C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2. 5 in winter (with a normalized mean bias (NMB) of 6.4 % in 2013) and O3 in summer (with an NMB of 18.2 % in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate–chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in east Asia (e.g., SWDOWN and T2 decreased by 21.8 W m−2 and 0.45 °C, respectively, and most pollutant concentrations increased by 4.8–9.5 % in January over China's major cities) were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled models over east Asia for regional climate and air quality modeling and to study the important climate–chemistry interactions. This work established a baseline for WRF-CMAQ simulations for a future period under the RCP4.5 climate scenario, which will be presented in a future paper.

Bias correction of climate models for hydrological modelling – are simple methods still useful?

ABSTRACTThe spatial climatic characteristics of the Himalayas are complex and a challenge for regional climate models (RCMs). There is no doubt that some form of correction before any application of RCM simulations is a must. In recent years, simple bias correction techniques have been overshadowed by more popular and complex bias correction techniques. In this study an attempt is made to compare the performance of a simple and of a comparatively complex correction technique for hydrological analysis at a monthly resolution in the Kaligandaki River Basin of Nepal. The research workflow consists of bias correction of temperature and precipitation using a simple technique (linear scaling) and a comparatively complex one (quantile mapping). The performance at monthly resolution is evaluated against observed meteorological data while a combined evaluation is made via hydrological model response analysis. The wetter and colder RCM estimates were significantly improved after bias correction. The hydrological modelling response also shows the importance of the bias correction of the RCMs. However, no significant difference was observed between the outputs of linear scaling and quantile mapping which exhibited almost identical performances. Hence, this study has a novel conclusion that a simple method, such as linear scaling, is sufficient for hydrological analysis at monthly resolution.

Combining the ensemble mean and bias correction approaches to reduce the uncertainty in hillslope-scale soil moisture simulation

The ROSETTA model has routinely been applied to predict the soil hydraulic properties for simulating the water flow at the hillslope scale. However, the uncertainties in water flow simulations are substantial due to the soil heterogeneity and ROSETTA model structure. In order to reduce these uncertainties, this study used the HYDRUS-2D and ensemble mean to simulate soil moisture based on the outputs of all candidate models. In addition, the bias correction techniques (including linear bias correction (LBC) and cumulative distribution function (CDF) matching) were also applied to improve the prediction of soil moisture. A total of 320days of observed soil moisture data at two depths (10 and 30cm) in the upper and lower slope positions were adopted to evaluate the performances of different bias correction methods results showed that the uncertainty in hillslope-scale soil moisture simulation due to the ROSETTA model structure was more important than that due to the soil heterogeneity. The CDF matching-based nonlinear bias correction approach was generally better than the LBC in reducing the uncertainty in soil moisture simulation. Combining the ensemble mean and CDF matching was a viable approach to improve the accuracy of the numerical model for simulating the hillslope-scale soil moisture variations.

EFFECT OF CLIMATE CHANGE ON GROUNDWATER AND SALTS STORAGE IN EL SHEIKH ZUWAID-RAFAH, NORTH SINAI, EGYPT

Identifying and quantifying future climate impacts on water resources has major economic and societal implications, rendering such studies extremely important for water planners. In this research, we integrate the outputs of the general global models GDFL-ESM2M (the suitable one of the three studied models; CNRM-CM5, GFDL-ESM2M and EC-EARTH) downscaled by regional climate model (RCA4) under representative concentration pathways RCPs scenarios 8.5, into two hydrological models to provide approximations of climate change impacts on water quantity and quality in El Sheikh Zuwaid-Rafah quaternary aquifer system which is the main source of water in the area. The projected changes of the rising scenario between the periods 1980-2005 and 2050-2075 from the three studied GCMs predicted an increase in the mean of July temperature between 4.2 - 4.8 K (kelvin degree), whereas January temperature was estimated to increase between 1.8 - 5.4 K (kelvin degree). The cumulative annual precipitation was estimated to decrease by a range varied between 30% to 51%. A Simple Bias correction (SBC) technique was implemented to correct the simulated monthly precipitation series of RCA4 (GDFL-ESM2M) over Sinai Peninsula. A three dimensional finite difference flow model (Processing MODFLOW -PM5), and the implemented solute transport model (MT3D) were used to predict the groundwater and the total dissolved solids storage. Results from running the raising scenario RCP 8.5 estimated that by the hydrological year 2074 groundwater recharge would be decreased by 2.73 M.m3, leading with the yearly exploitation to decrease groundwater storage to 11.83 M.m3 and decrease in dissolve salt storage 33.1 M. Kg.

Dealing with small sample bias in post-crisis samples

In this paper, we demonstrate that using finite sample correction bootstrapping techniques is advisable in samples that cover less than two complete business cycles, even when high-frequency data seemingly provide a sufficient number of observations to overcome the small sample bias. This is particularly relevant in the current research environment. Because the recent financial crisis is considered as a structural break, research on current problems is often conducted using post-crisis data. That is, the available samples cover only a few years of data, often spanning only one business cycle or even less. We provide ample simulation-based evidence that samples of daily or monthly dynamic data covering periods of this magnitude are prone to a fairly substantial bias. Moreover, we are able to show that standard bootstrap-based bias correction techniques still work in those cases.

The accuracy of self-reported pregnancy-related weight: a systematic review.

Self-reported maternal weight is error-prone, and the context of pregnancy may impact error distributions. This systematic review summarizes error in self-reported weight across pregnancy and assesses implications for bias in associations between pregnancy-related weight and birth outcomes. We searched PubMed and Google Scholar through November 2015 for peer-reviewed articles reporting accuracy of self-reported, pregnancy-related weight at four time points: prepregnancy, delivery, over gestation and postpartum. Included studies compared maternal self-report to anthropometric measurement or medical report of weights. Sixty-two studies met inclusion criteria. We extracted data on magnitude of error and misclassification. We assessed impact of reporting error on bias in associations between pregnancy-related weight and birth outcomes. Women underreported prepregnancy (PPW: -2.94 to -0.29kg) and delivery weight (DW: -1.28 to 0.07kg), and over-reported gestational weight gain (GWG: 0.33 to 3kg). Magnitude of error was small, ranged widely, and varied by prepregnancy weight class and race/ethnicity. Misclassification was moderate (PPW: 0-48.3%; DW: 39.0-49.0%; GWG: 16.7-59.1%), and overestimated some estimates of population prevalence. However, reporting error did not largely bias associations between pregnancy-related weight and birth outcomes. Although measured weight is preferable, self-report is a cost-effective and practical measurement approach. Future researchers should develop bias correction techniques for self-reported pregnancy-related weight.

Precipitation change assessment over upper Bagmati river basin using regional bias corrected GCM data

Climate change impacts on precipitation pattern have been assessed over upper Bagmati river basin, Nepal. General circulation model (GCM) projections, which are widely used to assess climate change impacts, consist of significant biases due to oversimplification of the global climate system. Bias in daily GCM precipitation was corrected at grid cell scale using regional quantile-based bias correction technique. In this technique, bias correction pattern is established by comparing GCM grid data with nearby observation station data. Concepts of homogeneous precipitation regions were used to transfer statistical characteristics of nearby observation stations to ungauged cells. Calibration and validation of the regional bias correction technique was performed over 1979-2003. Comparative precipitation change assessments were carried out at annual, monthly and daily time scales considering bias corrected precipitation data of 1979-2003 and 2075-2099 periods respectively.

Precipitation change assessment over upper Bagmati river basin using regional bias corrected GCM data

Climate change impacts on precipitation pattern have been assessed over upper Bagmati river basin, Nepal. General circulation model (GCM) projections, which are widely used to assess climate change impacts, consist of significant biases due to oversimplification of the global climate system. Bias in daily GCM precipitation was corrected at grid cell scale using regional quantile-based bias correction technique. In this technique, bias correction pattern is established by comparing GCM grid data with nearby observation station data. Concepts of homogeneous precipitation regions were used to transfer statistical characteristics of nearby observation stations to ungauged cells. Calibration and validation of the regional bias correction technique was performed over 1979-2003. Comparative precipitation change assessments were carried out at annual, monthly and daily time scales considering bias corrected precipitation data of 1979-2003 and 2075-2099 periods respectively.

Evaluation of bias correction methods for wave modeling output

Models that seek to predict environmental variables invariably demonstrate bias when compared to observations. Bias correction (BC) techniques are common in the climate and hydrological modeling communities, but have seen fewer applications to the field of wave modeling. In particular there has been no investigation as to which BC methodology performs best for wave modeling. This paper introduces and compares a subset of BC methods with the goal of clarifying a “best practice” methodology for application of BC in studies of wave-related processes. Specific focus is paid to comparing parametric vs. empirical methods as well as univariate vs. bivariate methods. The techniques are tested on global WAVEWATCH III historic and future period datasets with comparison to buoy observations at multiple locations. Both wave height and period are considered in order to investigate BC effects on inter-variable correlation. Results show that all methods perform uniformly in terms of correcting statistical moments for individual variables with the exception of a copula based method underperforming for wave period. When comparing parametric and empirical methods, no difference is found. Between bivariate and univariate methods, results show that bivariate methods greatly improve inter-variable correlations. Of the bivariate methods tested the copula based method is found to be not as effective at correcting correlation while a “shuffling” method is unable to handle changes in correlation from historic to future periods. In summary, this study demonstrates that BC methods are effective when applied to wave model data and that it is essential to employ methods that consider dependence between variables.

On bias correction in drought frequency analysis based on climate models

Assessment of future drought characteristics based on climate models is difficult as climate models usually have bias in simulating precipitation frequency and intensity. In this study, we examine the significance of bias correction in the context of drought frequency and scenario analysis using output from climate models. In particular, we use three bias correction techniques with different emphases and complexities to investigate how they affect the results of drought frequency and severity based on climate models. The characteristics of drought are investigated using regional climate model (RCM) output from the North American Regional Climate Change Assessment Program (NARCCAP). The Standardized Precipitation Index (SPI) is used to compare and forecast drought characteristics at different timescales. Systematic biases in the RCM precipitation output are corrected against the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data and the bias-corrected RCM historical simulations. Preserving mean and standard deviation of NARR precipitation is essential in drought frequency analysis. The results demonstrate that bias correction significantly decreases the RCM errors in reproducing drought frequency derived from the NARR data. Different timescales of input precipitation in the bias corrections show similar results. The relative changes in drought frequency in future scenario compared to historical scenario are similar whether both scenarios are bias corrected or both are not bias corrected.

Statistical bias correction method applied on CMIP5 datasets over the Indian region during the summer monsoon season for climate change applications

This study makes use of temperature and precipitation from CMIP5 climate model output for climate change application studies over the Indian region during the summer monsoon season (JJAS). Bias correction of temperature and precipitation from CMIP5 GCM simulation results with respect to observation is discussed in detail. The non-linear statistical bias correction is a suitable bias correction method for climate change data because it is simple and does not add up artificial uncertainties to the impact assessment of climate change scenarios for climate change application studies (agricultural production changes) in the future. The simple statistical bias correction uses observational constraints on the GCM baseline, and the projected results are scaled with respect to the changing magnitude in future scenarios, varying from one model to the other. Two types of bias correction techniques are shown here: (1) a simple bias correction using a percentile-based quantile-mapping algorithm and (2) a simple but improved bias correction method, a cumulative distribution function (CDF; Weibull distribution function)-based quantile-mapping algorithm. This study shows that the percentile-based quantile mapping method gives results similar to the CDF (Weibull)-based quantile mapping method, and both the methods are comparable. The bias correction is applied on temperature and precipitation variables for present climate and future projected data to make use of it in a simple statistical model to understand the future changes in crop production over the Indian region during the summer monsoon season. In total, 12 CMIP5 models are used for Historical (1901–2005), RCP4.5 (2005–2100), and RCP8.5 (2005–2100) scenarios. The climate index from each CMIP5 model and the observed agricultural yield index over the Indian region are used in a regression model to project the changes in the agricultural yield over India from RCP4.5 and RCP8.5 scenarios. The results revealed a better convergence of model projections in the bias corrected data compared to the uncorrected data. The study can be extended to localized regional domains aimed at understanding the changes in the agricultural productivity in the future with an agro-economy or a simple statistical model. The statistical model indicated that the total food grain yield is going to increase over the Indian region in the future, the increase in the total food grain yield is approximately 50 kg/ ha for the RCP4.5 scenario from 2001 until the end of 2100, and the increase in the total food grain yield is approximately 90 kg/ha for the RCP8.5 scenario from 2001 until the end of 2100. There are many studies using bias correction techniques, but this study applies the bias correction technique to future climate scenario data from CMIP5 models and applied it to crop statistics to find future crop yield changes over the Indian region.

Daily climate data for the Amhara region in Northwestern Ethiopia

ABSTRACTMany spatial vegetative physiological and environmental impact studies demand consistent as well as fine resolution daily meteorological data, which is often unavailable. This problem is evident when studies and applications focus at the regional and local scale. Meteorological stations cannot provide the needed data due to their irregular locations and several associated shortcomings such as missing data. Climate data from global sources like the National Centers for Environmental Prediction (NCEP) and WorldClim are provided at a coarse resolution which makes them inappropriate to use directly at regional and local scales. In order to circumvent this problem, we downscaled daily WorldClim and NCEP global climate grids to produce weather data sets using a delta method to (approximately 1 × 1 km) spatial resolution for the Amhara region of northwestern Ethiopia. The downscaled data set includes daily precipitation, and minimum and maximum temperature from 1979 to 2010. The drizzle effect in the downscaled data set was first eliminated by using a rain threshold that removed values of <1 mm per day. We compared the downscaled values with our calibration data from 56 meteorological stations in the Amhara region. The comparison between the downscaled and the calibrated data from weather stations showed biases in the downscaled values. A delta‐change bias correction technique on a 10‐day average basis was used to correct the biases associated with the downscaled data. Simple additive and multiplicative bias correction methods were used for temperature and precipitation, respectively. We then validated the corrected downscaled daily weather data using ten independent weather stations from the region. We found that the downscaling and the bias correction methods have improved the NCEP values. The validation exhibited no bias. The full data set can be accessed freely under the following link: ftp://palantir.boku.ac.at/Public/ClimateDataEthiopia/.

Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

A bias corrected WRF mesoscale fire weather dataset for Victoria, Australia 1972-2012

Climatology data of fire weather across the landscape can provide science-based evidence for informing strategic decisions to ameliorate the impacts (at times extreme) of bushfires on community socio-economic wellbeing and to sustain ecosystem health and functions. A long-term climatology requires spatial and temporal data that are consistent to represent the landscape in sufficient detail to be useful for fire weather studies and management purposes. To address this inhomogeneity problem for analyses of a variety of fire weather interests and to provide a dataset for management decision-support, a homogeneous 41-year (1972-2012), hourly interval, 4 km gridded climate dataset for Victoria has been generated using a combination of mesoscale modelling, global reanalysis data, surface observations, and historic observed rainfall analyses. Hourly near-surface forecast fields were combined with Drought Factor (DF) fields calculated from the Australian Water Availability Project (AWAP) rainfall analyses to generate fields of hourly fire danger indices for each hour of the 41-year period. A quantile mapping (QM) bias correction technique utilizing available observations during 1996-2012 was used to ameliorate any model biases in wind speed, temperature and relative humidity. Extensive evaluation was undertaken including both quantitative and case study qualitative assessments. The final dataset includes 4-km surface hourly temperature, relative humidity, wind speed, wind direction, Forest Fire Danger Index (FFDI), and daily DF and Keetch-Byram Drought Index (KBDI), and a 32-level full three-dimensional volume atmosphere.

Multivariate Bias Correction of Climate Model Output: Matching Marginal Distributions and Intervariable Dependence Structure

Abstract Univariate bias correction algorithms, such as quantile mapping, are used to address systematic biases in climate model output. Intervariable dependence structure (e.g., between different quantities like temperature and precipitation or between sites) is typically ignored, which can have an impact on subsequent calculations that depend on multiple climate variables. A novel multivariate bias correction (MBC) algorithm is introduced as a multidimensional analog of univariate quantile mapping. Two variants are presented. MBCp and MBCr respectively correct Pearson correlation and Spearman rank correlation dependence structure, with marginal distributions in both constrained to match observed distributions via quantile mapping. MBC is demonstrated on two case studies: 1) bivariate bias correction of monthly temperature and precipitation output from a large ensemble of climate models and 2) multivariate correction of vertical humidity and wind profiles, including subsequent calculation of vertically integrated water vapor transport and detection of atmospheric rivers. The energy distance is recommended as an omnibus measure of performance for model selection. As expected, substantial improvements in performance relative to quantile mapping are found in each case. For reference, characteristics of the MBC algorithm are compared against existing bivariate and multivariate bias correction techniques. MBC performs competitively and fills a role as a flexible, general purpose multivariate bias correction algorithm.

Practical Bias Correction in Aerial Surveys of Large Mammals: Validation of Hybrid Double-Observer with Sightability Method against Known Abundance of Feral Horse (Equus caballus) Populations

Reliably estimating wildlife abundance is fundamental to effective management. Aerial surveys are one of the only spatially robust tools for estimating large mammal populations, but statistical sampling methods are required to address detection biases that affect accuracy and precision of the estimates. Although various methods for correcting aerial survey bias are employed on large mammal species around the world, these have rarely been rigorously validated. Several populations of feral horses (Equus caballus) in the western United States have been intensively studied, resulting in identification of all unique individuals. This provided a rare opportunity to test aerial survey bias correction on populations of known abundance. We hypothesized that a hybrid method combining simultaneous double-observer and sightability bias correction techniques would accurately estimate abundance. We validated this integrated technique on populations of known size and also on a pair of surveys before and after a known number was removed. Our analysis identified several covariates across the surveys that explained and corrected biases in the estimates. All six tests on known populations produced estimates with deviations from the known value ranging from -8.5% to +13.7% and <0.7 standard errors. Precision varied widely, from 6.1% CV to 25.0% CV. In contrast, the pair of surveys conducted around a known management removal produced an estimated change in population between the surveys that was significantly larger than the known reduction. Although the deviation between was only 9.1%, the precision estimate (CV = 1.6%) may have been artificially low. It was apparent that use of a helicopter in those surveys perturbed the horses, introducing detection error and heterogeneity in a manner that could not be corrected by our statistical models. Our results validate the hybrid method, highlight its potentially broad applicability, identify some limitations, and provide insight and guidance for improving survey designs.

Is It Possible to Beat the Random Walk Model in Exchange Rate Forecasting? More Evidence for Brazilian Case.

Abstract The seminal study of Meese et al. (1983) on exchange rate forecastability had a great impact on the international finance literature. The authors showed that exchange rate forecasts based on structural models are worse than a naive random walk. This result is known as the Meese--Rogoff (MR) puzzle. Although the validity of this result has been checked for many currencies, studies for the Brazilian currency are not common. In 1999, Brazil adopted the dirty floating exchange rate regime. Rossi (2013) ran an extensive study on the MR puzzle but did not analyse Brazilian data. Our goal is to run a “pseudo real-time experiment” to investigate whether forecasts based on econometric models that use the fundamentals suggested by the exchange rate monetary theory of the 80s can beat the random model for the case of the Brazilian currency. Our work has three main differences with respect to Rossi (2013). We use a bias correction technique and forecast combination in an attempt to improve the forecast accuracy of our projections. We also combine the random walk projections with the projections of the structural models to investigate if it is possible to further improve the accuracy of the random walk forecasts. However, our results are quite in line with Rossi (2013). We show that it is not difficult to beat the forecasts generated by the random walk with drift using Brazilian data, but that it is quite difficult to beat the random walk without drift. Our results suggest that it is advisable to use the random walk without drift, not only the random walk with drift, as a benchmark in exercises that claim the MR result is not valid.