scholarly journals Comment on "Polynomial cointegration tests of anthropogenic impact on global warming" by Beenstock et al. (2012) – some hazards in econometric modelling of climate change

2013 ◽  
Vol 4 (2) ◽  
pp. 375-384 ◽  
Author(s):  
F. Pretis ◽  
D. F. Hendry
Keyword(s):  
Time Series ◽  
Anthropogenic Impact ◽  
Time Series Data ◽  
Series Data ◽  
Climate Data ◽  
Econometric Modelling ◽  
Empirical Modelling ◽  
Cointegration Tests ◽  
Polynomial Cointegration ◽  
Made In

Abstract. We outline six important hazards that can be encountered in econometric modelling of time-series data, and apply that analysis to demonstrate errors in the empirical modelling of climate data in Beenstock et al. (2012). We show that the claim made in Beenstock et al. (2012) as to the different degrees of integrability of CO2 and temperature is incorrect. In particular, the level of integration is not constant and not intrinsic to the process. Further, we illustrate that the measure of anthropogenic forcing in Beenstock et al. (2012), a constructed "anthropogenic anomaly", is not appropriate regardless of the time-series properties of the data.

Producing Daily Landsat Snow Cover Time-Series Data

2021 ◽  
Author(s):  
Fatemeh Zakeri ◽  
Gregoire Mariethoz
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Snow Cover ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Swiss Alps ◽  
Series Data ◽  
Landsat Data ◽  
Climate Data ◽  
Data Set

<p>Snow cover maps are critical for hydrological studies as well as climate change impacts assessment. Remote sensing plays a vital role in providing snow cover information. However, acquisition limitations such as clouds, shadows, or revisiting time limit accessing daily complete snow cover maps obtained from remote sensing. This study explores the generation of synthetic daily Landsat time-series data focusing on snow cover using available Landsat data and climate data for 2020 in the Western Swiss Alps (Switzerland). <br>Landsat surface reflectance is predicted using all available Landsat imagery from 1984 to2020 and ERA5 reanalysis precipitation and air temperature daily data in this study. For a given day where there is no Landsat data, the proposed procedure computes a similarity metric to find a set of days having a similar climatic pattern and for which satellite data is available. These best match images constitute possible snow cover scenarios on the target day and can be used as stochastic input to impact models. <br>Visual comparison and quantitative assessment are used to evaluate the accuracy of the generated images. In both accuracy assessments, some real Landsat data are omitted from the searching data set, and synthetic images are compared visually with real Landsat images. In the quantitative evaluation, the RSME between the real and artificial images is computed in a cross-validation fashion. Both accuracy procedures demonstrate that the combination of Landsat and climate data can predict Landsat's daily reflectance focusing on snow cover.</p>

Growth Rate of Pulses in Eastern Uttar Pradesh a Zone-wise Analysis

2021 ◽  
Vol 66 (3) ◽  
Author(s):  
Ekta Pandey
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Time Series Data ◽  
Uttar Pradesh ◽  
Series Data ◽  
The North ◽  
North Eastern ◽  
Eastern Uttar Pradesh ◽  
Negative Growth ◽  
Made In

Attempts are made in this paper to investigate the trend of pulses in Eastern Uttar Pradesh, as well as their instability and non-linear model. This time series data on pulses pertains to the period 1980-1981 to 2014-15 and includes information on the area, production, and productivity of pulses. Pulses have had negative growth in terms of area, production, and productivity in all three zones of Eastern Uttar Pradesh, namely, the North Eastern plain zone, the Eastern plain zone, and the Vindhyan zone. Since 1980-81, there has been a rise in the area and output of pulses in the Vindhyan zone, as seen by the percentage change. The Eastern plain zone has the most stable pulse crop in terms of instability

scholarly journals Identification of Variations in the Climatic Conditions of the Lerma-Chapala-Santiago Watershed by Comparative Analysis of Time Series

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Luis Ricardo Manzano-Solís ◽  
Miguel A. Gómez-Albores ◽  
Carlos Díaz-Delgado ◽  
Carlos Alberto Mastachi-Loza ◽  
Raymundo Ordoñez-Sierra ◽  
...  
Keyword(s):  
Time Series ◽  
Comparative Analysis ◽  
Time Series Data ◽  
Summer Rainfall ◽  
Kappa Statistic ◽  
Climatic Conditions ◽  
Series Data ◽  
Summer Drought ◽  
Climate Data ◽  
Climate Maps

The current study presents a method for automating the Köppen–Garcia climate classification using a GIS module. This method was then applied in a case study of the Lerma-Chapala-Santiago watershed to compare time series data on climate from 1960 to 1989, 1981 to 2010, and 1960 to 2010. The kappa statistic indicated that the climate classifications of the generated model had a perfect degree of agreement with those of a prior nonautomated study. The climate data from the period 1960 to 2010 were used to create a climate map for the watershed. Overall, the dominant climates were dry, semiarid, temperate, and semiwarm temperate with a summer rainfall pattern. A comparative analysis of climate behavior between 1960 and 1989 and between 1981 and 2010 showed changes in temperature and extreme temperatures over 13.6% and 9.9%, respectively, of the watershed; the presence or absence of mid-summer drought also changed over 0.8% of the watershed. The module developed herein can be used to classify climates across all of Mexico, and data of varying spatial resolution and coverage can be inputted to the module. Finally, this module can be used to automate the creation of climate maps or to update climate maps at diverse spatial-temporal scales.

scholarly journals Moving forward with time series analysis

2017 ◽  
Vol 4 (4) ◽  
pp. 205316801773223
Author(s):  
Peter K. Enns ◽  
Nathan J. Kelly ◽  
Takaaki Masaki ◽  
Patrick C. Wohlfarth
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Error Correction ◽  
Time Series Data ◽  
Error Correction Model ◽  
Series Data ◽  
Correction Model ◽  
Standard Procedures ◽  
Cointegration Tests ◽  
General Error

In a recent Research and Politics article, we showed that for many types of time series data, concerns about spurious relationships can be overcome by following standard procedures associated with cointegration tests and the general error correction model (GECM). Matthew Lebo and Patrick Kraft (LK) incorrectly argue that our recommended approach will lead researchers to identify false (i.e., spurious) relationships. In this article, we show how LK’s response is incorrect or misleading in multiple ways. Most importantly, when we correct their simulations, their results reinforce our previous findings, highlighting the utility of the GECM when estimated and interpreted correctly.

scholarly journals Rising Unemployment in Nigeria-Public Debt to the Rescue?

2020 ◽  
Vol 3 (2) ◽  
pp. 280-290
Author(s):  
Jude Chukwunyere Iwuoha
Keyword(s):  
Time Series ◽  
Unemployment Rate ◽  
Public Debt ◽  
Inverse Relationship ◽  
Time Series Data ◽  
Series Data ◽  
Critical Infrastructures ◽  
Cointegration Tests ◽  
Weak Growth ◽  
Stationarity Test

Among the macroeconomic challenges facing Nigeria as a country are weak growth of the economy, ever increasing unemployment rate, and increasing inequality occasioned by increasing poverty. In trying to mitigate these challenges, the Nigeria government usually run aborrowing. In all these, the unemployment rate keep rising year-on-year. In this study, we tried to find out whether borrowing will come to the rescue in reducing unemployment in Nigeria, using time series data from 1981 - 2019. Employing the VECM model, we carried out the stationarity and cointegration tests respectively. While the stationarity test confirmed all variables being stationary at I(1), existence of cointegration was also confirmed indicating a relationship between public debt and unemployment which turned out to be an inverse relationship. A high value of ECM was recorded. It was found that unemployment granger causes government debt and debt servicing. The overall result shows that public debt have rendered little or no assistance in combating unemployment in Nigeria. While we do not discourage government from borrowing for the provision of critical infrastructures, corruption should be put in check so as to allow the amount of borrowing be reflected on the infrastructures available, as public debt also have some adverse effects on the economy.

scholarly journals Comment on "Polynomial cointegration tests of anthropogenic impact on global warming" by Beenstock et al. (2012) – Some fallacies in econometric modelling of climate change

2013 ◽  
Vol 4 (1) ◽  
pp. 219-233
Author(s):  
D. F. Hendry ◽  
F. Pretis
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Statistical Analysis ◽  
Anthropogenic Impact ◽  
Econometric Modelling ◽  
Cointegration Tests ◽  
Polynomial Cointegration

Abstract. We demonstrate major flaws in the statistical analysis of Beenstock et al. (2012), discrediting their initial claims as to the different degrees of integrability of CO2 and temperature.

Stationarity and Cointegration Tests of the Ohlson Model

2000 ◽  
Vol 15 (2) ◽  
pp. 141-160 ◽  
Author(s):  
Daqing D. Qi ◽  
Y. Woody Wu ◽  
Bing Xiang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Empirical Studies ◽  
Market Value ◽  
Residual Income ◽  
Series Data ◽  
Book Value ◽  
Cointegration Tests ◽  
Significant Difference ◽  
Out Of Sample

This paper investigates the time-series properties of the Ohlson (1995) model and examines their implications for empirical studies that use time-series data but do not explicitly account for such properties. Based on a sample of 95 firms with complete data from 1958 to 1994, we show that the null hypothesis that market value and book value are nonstationary cannot be rejected for most of the sample firms. More importantly, book value and residual income do not cointegrate with market value for 80 percent of the sample firms. We demonstrate the importance and relevance of the time-series properties of the model to OLS regressions by showing that the OLS out-of-sample forecasts of market value are significantly more accurate and less biased for the cointegrated firms than for the non-cointegrated firms. We also explore methods to improve the specification of OLS regressions based on the Ohlson (1995) model and suggest that scaling the variables with lagged market value can significantly alleviate the problem with nonstationarity of the unsealed time-series data. While the generality of our results is limited by the survivorship bias of our sample, we believe that our paper has some important implications for studies motivated by the Ohlson (1995) model. First, because market value and book value are nonstationary and book value and residual income do not cointegrate with market value for most firms, the other information variable has to be nonstationary so that a linear combination of the independent variables can cointegrate with market value. Second, direct tests of the Ohlson (1995) model through OLS regressions using time-series data are questionable because they are likely to be misspecified. This may partially explain the underestimation of market value widely documented by previous studies and the significant difference between parameters predicted by the Ohlson (1995) model and estimated from OLS regressions. Third, our results also suggest that scaling the data with lagged market value can mitigate the problems with nonstationarity. For studies using unsealed time-series data, a cointegration test should be conducted first and a sensitivity analysis based on the cointegrated sub-sample should be performed to examine whether the results based on the full sample are robust.

scholarly journals UNDERSTANDING INDONESIA’S MACROECONOMIC DATA: WHAT DO WE KNOW AND WHAT ARE THE IMPLICATIONS?

2018 ◽  
Vol 21 (2) ◽  
pp. 229-264 ◽  
Author(s):  
Susan Sunila Sharma
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Structural Breaks ◽  
Time Series Data ◽  
Unit Roots ◽  
Series Data ◽  
Econometric Modelling ◽  
Shock Response ◽  
Macroeconomic Time Series ◽  
Root Properties

Unit root properties of macroeconomic data are important for both econometric modelling specifications and policy making. The form of variables (whether they are a unit root process) helps determine the correct econometric modelling. Equally, the form of variables helps explain how they react to shocks (both internal and external). Macroeconomic time-series data are often at the forefront of shock analysis and econometric modelling. There is a growing emphasis on research on Indonesia using time-series data; yet, there is limited understanding of data characteristics and shock response of these data. Using an extensive dataset comprising 33 macroeconomic time-series variables, we provide an informative empirical analysis of unit root properties of data. We find that regardless of data frequencies the empirical evidence of unit roots is mixed, some series respond quickly to shocks others do take time, and almost every macroeconomic data suffers from structural breaks. We draw implications of these findings.

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