scholarly journals The Macroeconomic Forecasting Performance of Autoregressive Models with Alternative Specifications of Time-Varying Volatility

2012 ◽  
Author(s):  
Todd E. Clark ◽  
Francesco Ravazzolo
Keyword(s):  
Autoregressive Models ◽  
Time Varying ◽  
Macroeconomic Forecasting ◽  
Forecasting Performance

scholarly journals Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Serdar Neslihanoglu
Keyword(s):  
State Space Model ◽  
Additive Model ◽  
Market Model ◽  
Time Varying ◽  
Daily Data ◽  
Forecasting Performance ◽  
Modeling And Forecasting ◽  
Crypto Currency ◽  
Beta Risk ◽  
The Relationship

AbstractThis research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. Two extensions are offered to compare the performance of the linear specification of the market model (LMM), which allows for the measurement of the cryptocurrency price beta risk. The first is the generalized additive model, which permits flexibility in the rigid shape of the linearity of the LMM. The second is the time-varying linearity specification of the LMM (Tv-LMM), which is based on the state space model form via the Kalman filter, allowing for the measurement of the time-varying beta risk of the cryptocurrency price. The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization, using the Crypto Currency Index 30 (CCI30) as a market proxy and 1-day and 7-day forward predictions. Such a comparison of cryptocurrency prices has yet to be undertaken in the literature. The empirical findings favor the Tv-LMM, which outperforms the others in terms of modeling and forecasting performance. This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear, especially during the COVID-19 period.

Time-Varying Linear Autoregressive Models for Segmentation

2007 ◽  
Author(s):  
Charles Florin ◽  
Nikos Paragios ◽  
Gareth Funka-Lea ◽  
James Williams
Keyword(s):  
Autoregressive Models ◽  
Time Varying

On Computing Jeffrey’s Divergence Between Time-Varying Autoregressive Models

2015 ◽  
Vol 22 (7) ◽  
pp. 915-919 ◽  
Author(s):  
Clement Magnant ◽  
Audrey Giremus ◽  
Eric Grivel
Keyword(s):  
Autoregressive Models ◽  
Time Varying

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 Count time series models for road traffic accidents in Tanzania Mainland

1970 ◽  
Vol 47 (2) ◽  
pp. 495-506
Author(s):  
Amina S Msengwa ◽  
Florence D Ngari
Keyword(s):  
Traffic Accidents ◽  
Negative Binomial ◽  
Road Traffic ◽  
Information Criterion ◽  
Dar Es Salaam ◽  
Autoregressive Models ◽  
Ratio Test ◽  
Time Varying ◽  
Road Traffic Accidents ◽  
Related Factors

A pairwise analysis was conducted to assess the trends and factors associated with road traffic accidents in Tanzania. The Poisson and Negative Binomial Autoregressive Models were used to extend log linear functions by accounting time-varying components. A total of 85,514 road traffic accidents in Tanzania mainland that occurred from 2012 to 2017 were extracted from Tanzania Police Office records. Eleven factors were grouped into a human, vehicle, physical/environmental and pedestrian-related factors. The Likelihood ratio test, Akaike Information Criterion, Bayesian Information Criterion and residual ACF plots were used to evaluate the performance of the models in Dar es Salaam and other combined regions. The trend analysis indicated a declining pattern in all factors and human-related factors appeared higher than the other three factors. The highest number of road traffic accidents was observed in Dar es Salaam Region compared to other combined regions. The models, including its past values and time-varying factors, were in favour-of other models. In both, Dar es Salaam and other combined regions, non-linear pattern and Negative Binomial Autoregressive Models fitted the data well. The implementation of collective actions in recent years seems positive on road traffic accidents. Nevertheless, more emphasis is needed to monitor trends on the number of accidents and related fatalities. Keywords: Road Traffic Accidents, Poisson, Negative binomial, Autoregressive Models, Tanzania.

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