scholarly journals The Discovery of Long-Run Causal Order: A Preliminary Investigation

Econometrics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 31
Author(s):  
Kevin D. Hoover
Keyword(s):  
Time Series ◽  
Causal Structure ◽  
Preliminary Investigation ◽  
Causal Modeling ◽  
Causal Order ◽  
Long Run ◽  
Weak Exogeneity

The relation between causal structure and cointegration and long-run weak exogeneity is explored using some ideas drawn from the literature on graphical causal modeling. It is assumed that the fundamental source of trending behavior is transmitted from exogenous (and typically latent) trending variables to a set of causally ordered variables that would not themselves display nonstationary behavior if the nonstationary exogenous causes were absent. The possibility of inferring the long-run causal structure among a set of time-series variables from an exhaustive examination of weak exogeneity in irreducibly cointegrated subsets of variables is explored and illustrated.

scholarly journals Erratum: Hoover, K.D. 2020. The Discovery of Long-Run Causal Order: A Preliminary Investigation. Econometrics 8: 31

Econometrics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Kevin D. Hoover
Keyword(s):  
Preliminary Investigation ◽  
Causal Order ◽  
Long Run

The author would like to make the following correction to the article by Kevin D [...]

scholarly journals Time Series Modelling of The Volume of Overland Timber Export in The Bolgatanga Municipality

2020 ◽  
pp. 124-138
Author(s):  
Henry M. Kpamma ◽  
Silverius K. Bruku ◽  
John A. Awaab
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Preliminary Investigation ◽  
Arima Model ◽  
Estimation Method ◽  
Long Run ◽  
Time Series Modelling ◽  
The Future ◽  
Equilibrium Relationships ◽  
The Right

Aims/ Objectives: This research was carried out with the intention of using time series to model the volume of overland timber exported within Bolgatanga municipalityPlace and Duration of Study: Study of the time series was based on a historical data of the volume of timber exported for twenty consecutive years, from 1999 to 2019 within Bolgatanga municipality.Methodology: The three-stage iterative modeling approach for Box Jenkins was used to match an ARIMA model and to forecast both the amount of timber export and the confiscated lumber. ARIMA method incorporates a cycle of autoregressive and a moving average. The three-stage iterative modeling technique of Box Jenkins which were used are model recognition, parameter estimation and/or diagnostic checks were also made. Results: From the preliminary investigation, the study showed that the amount of timber exported in municipality is skewed to the right, suggesting that much of the amount of timber exported is below the average. This, together with the high volatility in the volume of timber exported, indicates that the amount of timber exported within the municipalities during the twenty-year period was low. The plots from the trends also showed robust variations in the volume of timber exported indicating that timber exporters do not have better grips with the concepts and applications of export technology, hence the erratic nature of the volume of timber exported over the period. The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported.The analysis further indicated that there will be a further decrease in the amount of timber export from the five years projection into the future. Over the last two decades the Bayesian approach to VAR has gained ground. For a future report, this estimation method will be followed to examine the ”long-run equilibrium relationships” between timber export volumes and climate change.Conclusion: The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported. There will be a further decrease in the amount of timber export from the five years projection into the future.

scholarly journals Cyclic quantum causal models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jonathan Barrett ◽  
Robin Lorenz ◽  
Ognyan Oreshkov
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Quantum Correlations ◽  
Quantum Systems ◽  
Causal Models ◽  
Causal Order ◽  
Quantum Processes ◽  
Causal Explanations ◽  
Causal Structures

AbstractCausal reasoning is essential to science, yet quantum theory challenges it. Quantum correlations violating Bell inequalities defy satisfactory causal explanations within the framework of classical causal models. What is more, a theory encompassing quantum systems and gravity is expected to allow causally nonseparable processes featuring operations in indefinite causal order, defying that events be causally ordered at all. The first challenge has been addressed through the recent development of intrinsically quantum causal models, allowing causal explanations of quantum processes – provided they admit a definite causal order, i.e. have an acyclic causal structure. This work addresses causally nonseparable processes and offers a causal perspective on them through extending quantum causal models to cyclic causal structures. Among other applications of the approach, it is shown that all unitarily extendible bipartite processes are causally separable and that for unitary processes, causal nonseparability and cyclicity of their causal structure are equivalent.

LEARNING DYNAMICS AND NONLINEAR MISSPECIFICATION IN AN ARTIFICIAL FINANCIAL MARKET

2009 ◽  
Vol 13 (5) ◽  
pp. 625-655 ◽  
Author(s):  
Christophre Georges ◽  
John C. Wallace
Keyword(s):  
Time Series ◽  
Financial Market ◽  
Consistency Condition ◽  
Nonlinear Structure ◽  
State Variable ◽  
Bounded Memory ◽  
Long Run ◽  
Finite Memory ◽  
Aggregate Dynamics ◽  
True Structure

In this paper, we explore the consequence of learning to forecast in a very simple environment. Agents have bounded memory and incorrectly believe that there is nonlinear structure underlying the aggregate time series dynamics. Under social learning with finite memory, agents may be unable to learn the true structure of the economy and rather may chase spurious trends, destabilizing the actual aggregate dynamics. We explore the degree to which agents' forecasts are drawn toward a minimal state variable learning equilibrium as well as a weaker long-run consistency condition.

Forecasting Long-Run Coal Price in China: A Shifting Trend Time-Series Approach

2010 ◽  
Vol 14 (3) ◽  
pp. 499-519 ◽  
Author(s):  
Baomin Dong ◽  
Xuefeng Li ◽  
Boqiang Lin
Keyword(s):  
Time Series ◽  
Coal Price ◽  
Long Run ◽  
Time Series Approach

The Impact of Terms of Trade on the Export Structure of Manufactured Products in Different Economic Developing Status

2021 ◽  
Vol 1 (1) ◽  
pp. 59-67
Author(s):  
Kai-Ting Huang ◽  
Keyword(s):  
Time Series ◽  
Terms Of Trade ◽  
Development Status ◽  
Structural Time Series ◽  
Export Structure ◽  
Primary Products ◽  
Long Run ◽  
Deterministic Trend ◽  
Manufactured Products ◽  
The Impact

The Prebisch-Singer Hypothesis states that in structural time series analysis, the terms of trade between primary products and manufacturers have a negative deterministic trend. Many researchers argued that the deterioration in trade is the type of country in which the products are exported, regardless of whether the types of products exported by such countries are primary or manufactured products. This paper employs a development-differentiated model to analyze the correlation between various terms of trade and the export proportion of manufactured products on different economies of development status. In the long run, stable co-integration relations exist between terms of trade and the export proportion of manufactured products for development status. Furthermore, the increased proportion of manufactured products exports is the Granger casualty for the worse terms of trade for several economies of development status. The results demonstrated that changing the terms of trade is significantly influenced by structured changes in the export proportion of manufactured products for the development status of economies.

scholarly journals Causes for Deforestation in Indonesia: Corruption and Palm Tree Plantation

2015 ◽  
Vol 11 (27) ◽  
pp. 120
Author(s):  
Osama Eldeeb ◽  
Petr Prochazka ◽  
Mansoor Maitah
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Forest Cover ◽  
Palm Tree ◽  
Test Statistics ◽  
Crude Palm Oil ◽  
Tree Plantation ◽  
Long Run ◽  
Corruption Perception ◽  
Forest Cover Loss

<p>Indonesian biodiversity is threatened by massive deforestation. In this research paper, claims that deforestation in Indonesia is caused by corruption and supported by crude palm oil production is verified using time series analysis. Using Engel Granger cointegration test, three time series of data, specifically corruption perception index, rate of deforestation and price of crude palm oil are inspected for a long-run relationship. Test statistics suggests that there is no long-run relationship among these variables. Authors provide several explanations for this result. For example, corruption in Indonesia, as measured by CPI is still very high. This may mean that forest cover loss is possible even though there is a positive change in corruption level. According to the results, crude palm oil price has also no effect upon forest cover loss. This is likely due to very low shut-down price of crude palm oil for which production is still economical.</p>

scholarly journals Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

2019 ◽  
Vol 3 (2) ◽  
pp. 274-306 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Joseph D. Ramsey ◽  
Kun Zhang ◽  
Madelyn R. K. Glymour ◽  
Biwei Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Permutation Test ◽  
Causal Structure ◽  
Multiple Model ◽  
New Methods ◽  
Test Models ◽  
Non Gaussian ◽  
True Structure ◽  
Better Than

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).

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