Sayyed Mohammad Hoseini; Ramin Khochiany
Abstract
One of the most challenging issues in forecasting economic variables is the lack of sufficient data or the missing data in time series. In this paper, the time series of the GDP growth rate from 1980 to 2019 for 18 Middle East and North African countries is modeled via a generalized network ...
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One of the most challenging issues in forecasting economic variables is the lack of sufficient data or the missing data in time series. In this paper, the time series of the GDP growth rate from 1980 to 2019 for 18 Middle East and North African countries is modeled via a generalized network autoregressive model. Of the total observations, 13.42% were missed. In the proposed model, a random network is applied to the data for which nodes represent countries or related time series. Then, an autoregressive model of each node was constructed based on all the data of its multi-stage neighboring nodes. Some parameters of the model may depend on the node (local model) or can be considered the same for all network nodes (global model). The missing data are modeled by changing the weights of the network edges. Finally, the time series was predicted based on the constructed model. Because the network structure affects the model and ultimately the forecast, and on the other hand it is difficult to examine all possible networks, ten thousand un-directional random networks and 16 models including 8 local models and 8 global models on each network are considered. Out of 160,000 models, the network and the model with the least prediction error are selected as the best network and model that are used for the main prediction. The lowest in-sample predictive error was obtained at a local network, which has 64 edges and the number of corresponding model parameters is 4. Finally, the model is compared with the classical models such as AR and VAR. The results indicate the superiority of the proposed method in significantly reducing the prediction error over the AR and VAR models.
Ramin Khochiani; Younes Nademi
Abstract
The purpose of this paper is to revisit the relationship between inflation and output gap by using wavelet coherence approach. This approach attempts to combine the classical time series analysis with frequency domain analysis, and presents the advantages of assessing the co-movement of two series in ...
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The purpose of this paper is to revisit the relationship between inflation and output gap by using wavelet coherence approach. This approach attempts to combine the classical time series analysis with frequency domain analysis, and presents the advantages of assessing the co-movement of two series in the context of both time and frequency dimentions. Using continuous wavelet transform approach, the relationship between inflation and output gap, by considering GDP with oil sector and without oil sector, was studied by annual and quarterly data from 1959 to 2016 in Iran. The results showed that in the long run, the relashionship between inflation and output gap is positive. This result confirms existence of a Phillips curve with negative slope in the long run. However, the relationship between the two variables in the short term and also for the period before Islamic Revolution reflects a Phillips curve with positive slope. Friedman noted this type of curve for high inflation economies for a period of several years in 1977 in his Nobel Prize lecture. This result could have been very important in testing Phillips curve theory in Iranian economy.
