MODELING AND PREDICTION CONCERNING TIME SERIES OF FLOOD/DROUGHT RUNS USING THE SELF-EXCITING THRESHOLD AUTOREGRESSIVE MODEL

When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic time series,results are often not sufficiently good because nonlinear variations in the time series.In this paper, a nonlinear self-exciting threshold autoregressive(SETAR)model is applied to modeling and predicting the time series of flood/drought runs in Beijing,which were derived from the graded historical flood/drought records in the last 511 years(1470—1980).The results show that the modeling and predicting with the SETAR model are much better than that of the AR model.The latter can predict the flood/drought runs with a length only less than two years,while the formal can predict more than three-year length runs.This may be due to the fact that the SETAR model can renew the model according to the run-turning points in the process of predic- tion,though the time series is nonstationary.

Impact of Exchange Rate Threshold Level on Stock Market Performance- Evidence from Ghana

The exchange rate plays a significant role in an economy and also the purpose of this study is to examine the impact of exchange rate threshold level on the capital market performance.The study used a Threshold Autoregressive model introduced by[24]and[12].The study used quarter-time series data for thirty years from 1990 to 2019.The capital market performance was measured by the value of shares traded;market turnover;market capitalization and all-shares index.However,the results unconcealed the subsequently estimated threshold level of exchange rate for every performance indicator:7.94%;25.33%;25.33%,and 7.80%respectively.In all,the threshold level of the exchange rate estimated was 8 and 25 percent.The findings suggest that a low rate is performance-enhancing.Additionally,the exchange rate above the threshold level is harmful to the capital market performance.The findings of this investigation may be helpful to the government of Ghana and policymakers as they decide on an exchange rate target to implement to avoid the prejudicious effects of high exchange rates whereas getting the growth advantages of the low exchange rate.The finding of the study shows that the exchange rate impacts the economy more than inflation however,not many works in the subject area have been done in Sub-Saharan Africa.Therefore,I suggest that more threshold studies ought to be meted out on the exchange rate in the other sectors of the economy to determine its impact on the economy.

Sustainability of Public Debt and Economic Growth in Cote d’Ivoire:is There a Threshold Effect?

The development of Ivorian public debt in recent years has raised concerns.Is its current level capable of boosting the economy or,on the contrary,being at the source of a recession?This paper analyzes the effect of the level of indebtedness on economic growth in Côte d’Ivoire using the Threshold Autoregressive(TAR)model over the period 1970-2018.The results obtained in the short run shed light on the no relationship between public debt and economic growth.In the long run,on the other hand,there is a bi-directional granger causality between public debt and the sustainability of economic growth.The non-linearity between the variables of interest has been studied and the results show the presence of a threshold effect:beyond 48.03 percent of GDP,any increase in public debt by 1%should reduce economic growth by 0.28%.Thus,the study questions the relevance of the criterion set by the WAEMU:public debt<70%of GDP.

Threshold autoregression models for forecasting El Nino events

-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies of the cold tongue water area in the eastern tropical Pacific Ocean is obtained. On the basis of the time series, an autoregression model, a self-exciting threshold autoregression model and an open loop autoregression model are developed respectively. The interannual variations are simulated by means of those models. The simulation results show that all the three models have made very good hindcasting for the nine El Nino events since 1951. In order to test the reliability of the open loop threshold model, extrapolated forecast was made for the period of Jan. 1986-Feb. 1987. It can be seen from the forecasting that the model could forecast well the beginning and strengthening stages of the recent El Nino event (1986-1987). Correlation coefficients of the estimations to observations are respectively 0. 84, 0. 88 and 0. 89. It is obvious that all the models work well and the open loop threshold one is the best. So the open loop threshold autoregression model is a useful tool for monitoring the SSTinterannual variation of the cold tongue water area in the Eastern Equatorial Pacific Ocean and for estimating the El Nino strength.

Analyzing short time series data from periodically fluctuating rodent populations by thresholdmodels: A nearest block bootstrap approach

The study of the rodent fluctuations of the North was initiated in its modern form with Elton's pioneering work.Many scientific studies have been designed to collect yearly rodent abundance data,but the resulting time series are generally subject to at least two "problems":being short and non-linear.We explore the use of the continuous threshold autoregressive(TAR) models for analyzing such data.In the simplest case,the continuous TAR models are additive autoregressive models,being piecewise linear in one lag,and linear in all other lags.The location of the slope change is called the threshold parameter.The continuous TAR models for rodent abundance data can be derived from a general prey-predator model under some simplifying assumptions.The lag in which the threshold is located sheds important insights on the structure of the prey-predator system.We propose to assess the uncertainty on the location of the threshold via a new bootstrap called the nearest block bootstrap(NBB) which combines the methods of moving block bootstrap and the nearest neighbor bootstrap.The NBB assumes an underlying finite-order time-homogeneous Markov process.Essentially,the NBB bootstraps blocks of random block sizes,with each block being drawn from a non-parametric estimate of the future distribution given the realized past bootstrap series.We illustrate the methods by simulations and on a particular rodent abundance time series from Kilpisjrvi,Northern Finland.

What Causes China's High Inflation? A Threshold Structural Vector Autoregression Analysis

China＇s astonishing economic growth implies a necessity to understand its inflation. The present paper employs threshold nonrecursive structural vector autoregression analysis to explore the asymmetric effects of macro-variables on inflation in low and high inflation regimes. The empirical evidence demonstrates, first, that the reactions of inflation to various shocks are inflation-regime-dependent and asymmetric. Second, monetary policy influences China ＂s high inflation and adjusting the domestic interest rate in China may be an effective way to control inflation in a high inflation regime, but not in a low inflation regime. In a high inflation regime, a high inflation rate may cause the macro-policy authorities to increase the domestic interest rate, in an attempt to stabilize high inflation. Third, contrary to expectations, the world oil price is not a strong cost-push factor in a low inflation regime. Oil price increases may increase inflation in a high inflation regime, but there is no such obvious effect in a low inflation regime. Finally, China ＂s nominal effective exchange rate influences inflation in both low and high inflation regimes. A nominal effeetive exchange rate appreciation might be effective in controlling domestic inflation in both regimes.

Spatiotemporal Evolution and Linkage Analysis of Mutton Price in Xinjiang

The mutton industry plays a pivotal role in the animal husbandry industry of Xinjiang Uygur Autonomous Region.To better monitor the volatility movement and risk warning of mutton price,the fluctuation characteristics of mutton prices and the future trend of prices are analyzed systematically.On the one hand,the Hodrick Prescott(HP)filter method is used to analyze the long-term trends and cyclical characteristics of mutton prices in Xinjiang and explored the spatial evolution characteristics of mutton price fluctuations in various regions of Xinjiang.On the other hand,the Threshold Auto-Regressive(TAR)model is used to analyze the linkage relationship between mutton prices and the prices of other livestock products.The empirical results show that 1)the overall volatility of mutton price in Xinjiang is high,showing a trend of rising first,then falling and then rising from the temporal perspective.2)At the regional level,the price in the south is higher than that of the north,showing a decreasing trend from south to north on the whole from the spatial viewpoint.3)From the linkage relationship perspective,mutton and beef are complementary in the short term,but they are substitutes each other in the long term.This paper explores the characteristics of mutton price fluctuations in Xinjiang from the single time series of mutton prices and the linkage with other livestock products,which provides a reliable basis for the monitoring and risk warning of mutton price fluctuation.