The non-continuous direction vector I test

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Testing the Nominal-to-Real Transformation.

Nominal-to-real data transformations are routinely used in empirical work. A common example is the transformation of nominal money and prices to real money and the rate of inflation. This paper establishes the necessary and sufficient condition for a transformation to reduce the order of integration of an I(2) vector process while retaining the cointegrating relations among the variables. A particular direction in which the condition potentially fails is often treated by assumption in applied work. In this case, the transformed process satisfies a well-specified vector equilibrium model, yet I(1) inference and interpretation based on the real transformed system is invalidated. An easy-to-implement sequential test of the transformation based on I(1) cointegration methods is suggested. It demonstrates good size and power properties in a small-scale simulation experiment. An empirical example illustrates the need to test the nominal-to-real transformation.cointegration; stochastic trend; I(2); price homogeneity; Monto Carlo experiment

Neutral interstellar hydrogen in the inner heliosphere under the influence of wavelength-dependent solar radiation pressure

With the plethora of detailed results from heliospheric missions and at the advent of the first mission dedicated IBEX, we have entered the era of precision heliospheric studies. Interpretation of these data require precision modeling, with second-order effects quantitatively taken into account. We study the influence of the non-flat shape of the solar Ly-alpha line on the distribution of neutral interstellar H in the inner heliosphere. Based on available data, we (i) construct a model of evolution for the solar Ly-alpha line profile with solar activity, (ii) modify an existing test-particle code used to calculate the distribution of neutral interstellar H in the inner heliosphere so that it takes the dependence of radiation pressure on radial velocity into account, and (iii) compare the results of the old and new version. Discrepancies between the classical and Doppler models appear between ~5 and ~3 AU and increase towards the Sun from a few percent to a factor of 1.5 at 1 AU. The classical model overestimates the density everywhere except for a ~60-degr cone around the downwind direction, where a density deficit appears. The magnitude of the discrepancies appreciably depends on the phase of the solar cycle, but only weakly on the parameters of the gas at the termination shock. For in situ measurements of neutral atoms performed at ~1 AU, the Doppler correction will need to be taken into account, because the modifications include both the magnitude and direction of the local flux by a few km/s and degrees, respectively, which, when unaccounted for, would introduce an error of a few km/s and degrees in determination of the magnitude and direction of the bulk velocity vector at the termination shock.Comment: 10 pages, 13 figures, accepted by A&

Vector Autoregressive Models for Multivariate Time Series Analysis; Macroeconomic Indicators in Ghana

This study investigated the relationship, the percentage contribution of endogenous shocks and the direction of causality between real gross domestic product, exchange rate, foreign direct investment and unemployment rate in Ghana. It employed the multivariate Johansen co-integration test via vector auto-regressive model and the vector error correction model, to examine both long-run and short-run dynamic relationships respectively, between the selected macroeconomic variables for the period 1991-2016. The dynamic interactions between the variables were studied with Granger causality tests, impulse response functions, and forecast error variance decompositions. Augmented Dickey-Fuller (ADF) test indicated that all the variables were stationary after their first differencing, thus variables are integrated of order one, I (1). The diagnostic tests on the model residuals revealed that the models were adequate, valid and stable. The Trace test statistic of the Johansen cointegration test indicated one cointegrating relationship indicating long run relationship among the variables. Granger Causality analysis indicated a uni–directional causal relationship between real GDP and FDI. It also showed that FDI Granger-causes all of the other variables. The results revealed the positive effect and sensitivity of the FDI variable in determining the activities pertaining to real GDP, exchange rate, and unemployment rate and vice versa in the Ghanaian economy. Keywords: Vector Autoregression Model (VAR), Multivariate Time Series, Macroeconomic Variables, Cointegration, Granger causality, Impulse response function, Forecast error variance decomposition,

Capsule Network based Contrastive Learning of Unsupervised Visual Representations

Capsule Networks have shown tremendous advancement in the past decade, outperforming the traditional CNNs in various task due to it's equivariant properties. With the use of vector I/O which provides information of both magnitude and direction of an object or it's part, there lies an enormous possibility of using Capsule Networks in unsupervised learning environment for visual representation tasks such as multi class image classification. In this paper, we propose Contrastive Capsule (CoCa) Model which is a Siamese style Capsule Network using Contrastive loss with our novel architecture, training and testing algorithm. We evaluate the model on unsupervised image classification CIFAR-10 dataset and achieve a top-1 test accuracy of 70.50% and top-5 test accuracy of 98.10%. Due to our efficient architecture our model has 31 times less parameters and 71 times less FLOPs than the current SOTA in both supervised and unsupervised learning

The Impact of Economic Growth on CO2 Emissions and Energy Consumption -In the Gulf Cooperation Council Countries-

학위논문(석사)--서울대학교 대학원 :환경대학원 환경계획학과,2019. 8. Hong, Jong Ho.The research aims to test the Environmental Kuznets Curve (EKC) hypothesis and analyses the causality relationship between carbon (CO2) emissions, energy consumption, and Gross Domestic Product (GDP) per capita in the case of Gulf Cooperation Council – GCC – countries using the time series data for the period 1990–2014. Using the Vector Error Correction Model (VECM), it indicates that the EKC hypothesis does not hold in five of the six countries, and the inverted U-shaped curve was identified only in the UAE, regarding the direction of causality with Granger Causality and Vector Auto-Regressive (VAR) tests. It appears to be a unidirectional causality going from economic growth represented in GDP per capita to energy consumption. Such results suggest that reducing energy consumption and controlling CO2 emissions policies could be adopted in the GCC economies without much concern about its effects on economic growth.Abstract ................................................................................... i Chapter 1. Introduction ........................................................... 1 1.1. Purpose of Research ................................................................... 5 1.2. Structure of Research ................................................................. 6 Chapter 2. Current Status of GCC Countries............................ 7 2.1. Economic Growth ............................................................................. 7 2.2. Energy Consumption ........................................................................ 9 2.3. CO2 Emissions ...............................................................................10 Chapter 3. Literature Review ............................................... 13 3.1 Studies related to the EKC hypothesis.........................................13 3.2. Studies related to energy consumption and economic growth ..16 3.2.1. Single country studies on economic growth- energy consumption nexus..................................................................................................17 3.2.2. Multi-country studies on economic growth–energy consumption nexus..................................................................................................19 3.3. Studies related to the three variables .........................................21 3.4. Studies related to the GCC countries case .................................22 Chapter 4. Data Sources and Model...................................... 25 4.1. Data Sources..................................................................................25 4.2. Theoretical Context of the Model................................................30 Chapter 5. Methodology....................................................... 31 5.1. Cross-Sectional Test.....................................................................31 5.2. Unit Root Test ...............................................................................32 5.3. Causality Tests..............................................................................32 5.3.1. Granger Causality Test............................................................33 5.3.2. Vector Auto-Regressive (VAR) test........................................33 5.4. Vector Error Correction Model....................................................34 Chapter 6. Empirical Results ................................................ 35 6.1. Finding from CSD Test .................................................................35 6.2. Finding from Unit Root Test.........................................................35 6.3. Finding from Causality Tests .......................................................37 6.4. Findings from EKC ........................................................................38 6.4.1. EKC hypothesis not confirmed:................................................39 6.4.2. EKC hypothesis confirmed:......................................................40 6.4.3. EKC hypothesis not found:.......................................................40 Chapter 7. Policy Implications.............................................. 42 7.1. Over the literature.........................................................................43 7.2. Related to the causality test.........................................................44 7.2.1. Residential Sectors ..................................................................45 7.2.2. Commercial Sector...................................................................47 7.2.3. Industrial Sector.......................................................................48 7.2.4. Transportation Sector..............................................................49 7.3. Related to EKC test.......................................................................49 7.3.1. UAE Case.................................................................................50 7.3.2. Recommendations .................................................................... 54 Chapter 8. Conclusion.......................................................... 60 References.......................................................................... 62 Appendix............................................................................. 68 Appendix (I): Literature View..............................................................68 EKC's Literature Summery ................................................................68 Literature studying the EG- EC nexus .............................................. 69 Literature studying GCC countries ....................................................70 Literature studying the three variables .............................................71 Appendix (II): Data for GCCs Current Situation ................................72 Total final consumption of energy (Mtoe)..........................................72 CO2 emissions (million tonnes ) by sector in 2016.............................72 CO2 emissions (million tonnes ) 1971- 2016.....................................73 Electricity generation in Gigawatt hours (GWh ) 1973- 2016...........74 Total primary energy supply (Mtoe)..................................................75 Appendix (III): Data for econometric analysis....................................76 Bahrain ............................................................................................... 76 Kuwait ................................................................................................ 77 Qatar ..................................................................................................78 Oman .................................................................................................. 79 Saudi Arabia.......................................................................................80 UAE .................................................................................................... 81Maste