Impulse Indicator Saturation Research Articles

Hybrid ARIMA-IIS Approach for Wheat Yield Forecasting: An Integrated Approach

This study explores the application of a hybrid approach, combining the Impulse Indicator Saturation (IIS) method with an ARIMA(x) model, to forecast wheat yield. The IIS method is employed to find potential impulse responses, which are then integrated into the ARIMA(x) framework. The IIS method captures the potential joint effects of the weather, climate and other inputs on the data generating process of the wheat yield time series. The performance of the hybrid ARIMA(x) model is compared with that of the standalone ARIMA model using various error metrics, including Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Deviation (MAD). Additionally, model selection criteria such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Schwarz Bayesian Information Criterion (SBIC), and Hannan-Quinn Criterion (HQ) are used to identify the optimal model for forecasting. The training data of wheat yield from 1948-2018 was used to fit both the ARIMA and ARIMA(x) models, while the remaining observations until 2023 are used for model validation. The results of the study reveal that the hybrid ARIMA(x) model exhibits superior forecasting ability, demonstrating lower error metrics compared to the standalone ARIMA model. Notably, the ex-ante forecasts for the 2023-24 period predict a wheat production of 29.916 million tons using the ARIMA(x) model and 29.656 million tons using the ARIMA (2,1,2) model. These findings underscore the efficacy of the hybrid approach in enhancing production forecasting accuracy, thereby serving as a valuable basis for early warning systems to address potential demand and supply gaps in wheat production.

The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models

This study investigates the effectiveness of sparse regression models with their diverse specifications and the impulse indicator saturation (IIS) method in forecasting crude oil prices and their returns in China. The empirical findings showed that the IIS approach and sparse regression models are more effective than the benchmark of the no-change model. The IIS approach consistently outperformed the competing models and produced reliable results in terms of both ROS2 statistics and success ratio, as it is invariant to window size and time horizon. The results suggest that sparse regression models exhibit a notable decline in accuracy compared to the IIS approach over the long horizon. However, the selection of window size in the rolling window method plays a crucial role. Meanwhile, the sparse regression models are quite sensitive to window size; with a long horizon forecast and a small window size, the accuracy of the results is far less than with a broader window size. In contrast, the IIS method provides quite accurate forecasts regardless of window size and time horizon. The elastic smoothly clipped absolute deviation (E-SCAD) and elastic minimax concave penalty (E-MCP) remained more successful across the sparse models, possibly as a result of the high correlation among the set of predictors.

Testing for coefficient distortion due to outliers with an application to the economic impacts of climate change

Outlying observations can bias regression estimates, requiring the use of outlier-robust estimators. Comparing robust estimates to those obtained using ordinary least squares (OLS) is a common robustness check, however, such comparisons have been mostly informal due to the lack of available tests. Here we introduce a formal test for coefficient distortion due to outliers in regression models. Our proposed test is based on the difference between OLS and robust estimates obtained using a class of Huber-skip M-type estimators (such as Impulse Indicator Saturation or Robustified Least Squares). We show that our distortion test has an asymptotic chi-squared distribution by establishing the asymptotics of the corresponding Huber-skip M-estimators using an empirical process Central Limit Theorem recently developed in the literature. The test is valid for cross-sectional, as well as panel, and stationary or deterministically-trending time series models. To improve finite sample performance and to alleviate concerns on distributional assumptions, we explore several bootstrap testing schemes. We apply our outlier distortion test to estimates of the macro-economic impacts of climate change allowing for adaptation.

Cointegration Analysis of Triple Deficits: An Indicator Saturation Approach

The objective of this study to examine the econometric technique of the triple deficit’s hypothesis in the existence of multiple structural breaks. Triple deficit theory is a put forward by expanding the twin deficit theory, to recognize the private deficit together with the trade deficit and budget deficit and to investigate the relationship among these under the Keynesian approach. The annual time series data has been used from 1975 to 2020. This study has applied econometric techniques that captured the impact of multiple structural breaks in the series. In this study we used the saturation approach (impulse indicator saturation and step indicator saturation) for multiple structural breaks and equilibrium correction model (EqCM) for cointegration analysis. Results concluded that in the static equilibrium correction model there has been a positive relationship between fiscal-deficit, private deficit, and trade-deficit in the existence of multiple breakpoints. In the dynamic equilibrium correction model, there exists a positive relationship both in (short-term and long-term) between budget-deficit, private deficit, and trade deficit in the presence of three-step indicators. Therefore, to control the deficits government should make such type of policies that mitigates the effects of structural breaks, and this will atomically reduce the adverse effects of shocks to the trade sector and financial sector.

Who Is Successful in Foreign Exchange Margin Trading? New Survey Evidence from Japan

We use a 2018 survey of FX margin traders in Japan to investigate which key factors influence their trading performance: socio-demographic and economic situation, investment strategy and trading behaviour, and/or financial literacy. We study this question using general-to-specific modelling and impulse-indicator saturation. First, the data show that variables from all three groups are significant predictors of traders’ performance. Second, we find that older traders and those without a specific trading strategy demonstrate lower performance. Performance is higher for those who trade greater amounts, rely more on fundamental analysis, and report having profitable FX trade skills. Third, respondents’ subjectively stated claim of having FX trade skills is based on a more advanced understanding of FX trading and a reliance on professional advice. Neither objective financial knowledge nor over/underconfidence play a noteworthy role in the performance of margin traders.

Cross-Sectional Analysis of Impulse Indicator Saturation Method for Outlier Detection Estimated via Regularization Techniques with Application of COVID-19 Data.

Impulse indicator saturation is a popular method for outlier detection in time series modeling, which outperforms the least trimmed squares (LTS), M-estimator, and MM-estimator. However, using the IIS method for outlier detection in cross-sectional analysis has remained unexplored. In this paper, we probe the feasibility of the IIS method for cross-sectional data. Meanwhile, we are interested in forecasting performance and covariate selection in the presence of outliers. IIS method uses Autometrics techniques to estimate the covariates and outlier as the number of covariates P > n observations. Besides Autometrics, regularization techniques are a well-known method for covariate selection and forecasting in high-dimensional analysis. However, the efficiency of regularization techniques for the IIS method has remained unexplored. For this purpose, we explore the efficiency of regularization techniques for out-of-sample forecast in the presence of outliers with 6 and 4 standard deviations (SD) and orthogonal covariates. The simulation results indicate that SCAD and MCP outperform in forecasting and covariate selection with 4 SD (20% and 5% outliers) compared to Autometrics. However, LASSO and AdaLASSO select more covariates than SCAD and MCP and possess higher RMSE. Overall, regularization techniques possess the least RMSE than Autometrics, as Autometrics possesses the least average gauge at the cost of the least average potency. We use COVID-19 cross-sectional data collected from 1 July 2021 to 30 September 2021 for real data analysis. The SCAD and MCP select CRP level, gender, and other comorbidities as an important predictor of hospital stay with the least out-of-sample RMSE of 7.45 and 7.50, respectively.

Impact of COVID-19, Political, and Financial Events on the Performance of Commercial Banking Sector

This paper employs a structural empirical model to gauge the possible effects of COVID-19, political and financial events on the returns and volatility of commercial banks. It observes that insured and run-prone uninsured depositors choose between differentiated commercial banks, which appears to be significantly impacted from the present pandemic, especially for the case of Pakistan’s commercial banking sector. The estimated volatility series for commercial banks is measured through the GARCH model, which explains the current financial and political distress for the case of shocks from COVID-19. We calibrate by Impulse Indicator Saturation (IIS) to detect the structural breaks formed by these events in the returns and volatility series of commercial banks. It is observed that the calibrated model possesses almost all financial events that have had a prominent impact on the returns and volatility series whereas two out of eighteen political events are unimpacted.

Role of Islamic Banking during COVID-19 on Political and Financial Events: Application of Impulse Indicator Saturation

This paper attempts to detect the unavoidable impacts of COVID-19 on geopolitical and financial events related to Islamic banking and the finance sector in Pakistan. It considers only those major events that triggered imbalances in the equity prices of selected Islamic banks. Employed here is the GARCH model, used to predict the volatility series using daily data from January 2007 to July 2020. The Impulse Indicator Saturation (IIS) helps to identify the structural breaks due to COVID-19, as well as the effects of political and financial events on the returns and volatility series of Islamic banks. The results indicate that all the events due to COVID-19 are significant. While 19 out of 21 political and financial events impacted the returns and volatility series, there were only 2 political events out of 18 that showed no significant effect on the returns and the volatility series. The state’s and Islamic banks’ policymakers can use these results to build an effective and sustainable financial policy regarding Islamic finance and the banking sector.

Outliers detection in state-space model using indicator saturation approach

Structural changes that occur due to outliers may reduce the accuracy of an estimated time series model, shifting the mean distribution and causing forecast failure. This study used general-to-specific approach to detect outliers via indicator saturation approach in the local level model framework. Focusing on impulse indicator saturation, performance recorded by the suggested approach was evaluated using Monte Carlo simulations. To tackle the issue of higher number of regressors compared to the number of observations, this research utilized the split-half approach algorithm. We found that the impulse indicator saturation performance relies heavily on the size of outlier, location of outlier and number of splits in the series examined. Detection of outliers using sequential and non-sequential algorithms is the most crucial issue in this study. The sequential searching algorithm was able to outperform the non-sequential searching algorithm in eliminating the non-significant indicators based on potency and gauge. The outliers captured using impulse indicator saturation in financial times stock exchange (FTSE) United States of America (USA) shariah index correspond to the financial crisis in 2008-2009.

Evidence of air quality data misreporting in China: An impulse indicator saturation model comparison of local government-reported and U.S. embassy-reported PM2.5 concentrations (2015-2017).

This paper analyzes hourly PM2.5 measurements from government-controlled and U.S. embassy-controlled monitoring stations in five Chinese cities between January 2015 and June 2017. We compare the two datasets with an impulse indicator saturation technique that identifies hours when the relation between Chinese and U.S. reported data diverges in a statistically significant fashion. These temporary divergences, or impulses, are 1) More frequent than expected by random chance; 2) More positive than expected by random chance; and 3) More likely to occur during hours when air pollution concentrations are high. In other words, relative to U.S.-controlled monitoring stations, government-controlled stations systematically under-report pollution levels when local air quality is poor. These results contrast with the findings of other recent studies, which argue that Chinese air quality data misreporting ended after a series of policy reforms beginning in 2012. Our findings provide evidence that local government misreporting did not end after 2012, but instead continued in a different manner. These results suggest that Chinese air quality data, while still useful, should not be taken entirely at face value.

Testing for Coefficient Distortion due to Outliers with an Application to the Economic Impacts of Climate Change

Outlying observations can bias regression estimates, requiring the use of robust estimators. Comparing robust estimates to those obtained using OLS is a common robustness check, however, such comparisons have been mostly informal due to the lack of available tests. Here we introduce a formal test for coefficient distortion due to outliers in regression models. Our proposed test is based on the difference between OLS and robust estimates obtained using a class of Huber-skip M-type estimators (such as Impulse Indicator Saturation or Robustified Least Squares). Establishing asymptotics of the corresponding Huber-skip M-estimators using an empirical process CLT recently developed by Berenguer-Rico et al. (2019), we show that our distortion test has an asymptotic chi-squared distribution, and is valid for cross-sectional as well as panel and time series models. To improve finite sample performance and to alleviate concerns on distributional assumptions, we further introduce and explore three bootstrap testing schemes. We apply our outlier distortion test to estimates of the macro-economic impacts of climate change allowing for adaptation. We find that OLS estimates are significantly different to those obtained using a robust estimator and provide evidence of income-driven adaptation. Projecting the resulting damage curve to the end of the century shows that outlier-robust estimates dampen projected GDP losses and reduce the estimated marginal impacts of additional warming under adaptation.

Detection of Outliers in the Volatility of Malaysia Shariah Compliant Index Return: The Impulse Indicator Saturation Approach

Financial time series data often affected by various unexpected events which known as the outliers. The aim of this study is to detect the outliers in high frequency data using Impulse Indicator Saturation approach (IIS).Monte Carlo simulations illustrate the ability of IIS to detect outliers by using data with various simulation settings. For empirical application, we have chosen the Malaysia Shariah compliant index which is the FBM EMAS Shariah (FBMS) index. The result of this study discovered the presence of 47 outliers which related to several global events such as global financial crisis (2008 & 2009), the falling of stock market (2011), the United States debt-ceiling crisis (2013) and the declination of international crude oil prices (2014). Keywords: outliers; volatility; stock indices; IIS

Event Study and Impulse Indicator Saturation Analysis to Assess Reaction of Terrorist and Political Events: Evidence from Oil and Gas Sector of Pakistan

The objective of this study is twofold, first, to assess the impact ofterrorist attacks and political events on returns and volatility oil and gassector of Karachi Stock Exchange from the period of 2004 to 2014.Second, to compare the results of these events applying event studymethodology, event dummy analysis and impulse indicator saturation.Results indicate that the oil and gas sector reacts on the occurrence ofterrorism and political events and the results of two methodologiesevent study and event dummy analysis are almost similar. However,impulse indicator saturation is able to provide better results incomparison to event study and event dummy analysis because as itcaptures all breaks and co-breaks within a sample period,moreover it clearly helps in defining rebounding period of the market.
 JEL Classification Codes: G12, G14

Outlier Detection in Local Level Model: Impulse Indicator Saturation Approach

The existence of outliers in financial time series may affect the estimation of economic indicators. Detection of outliers in structural time series framework by using indicator saturation approach has become our main interest in this study. The reference model used is local level model. We apply Monte Carlo simulations to assess the performance of impulse indicator saturation for detecting additive outliers in the reference model. It is found that the significance level, α = 0.001 (tiny) outperformed the other target size in detecting various size of additive outliers. Further, we apply the impulse indicator saturation to detection of outliers in FTSE Bursa Malaysia Emas (FBMEMAS) index. We discover that there were 14 outliers identified corresponding to several economic and financial events.

Estimation of Effects of Recent Macroprudential Policies in a Sample of Advanced Open Economies

We used a time-series cross-section dataset to test several hypotheses pertaining to the role of macroprudential policy instruments in the management of the financial cycle in advanced open economies. The short-run effects are most significant for caps on loan to value and income (LTV and LTI) and risk weights (RW). The long-run coefficients of credit growth with respect to the indicators of amortisation requirements (Amort) and RW are also significant. The estimation results when house price growth is the dependent variable are consistent with these results. Our findings do not support that Basel III type countercyclical buffer (CCyB) has affected credit growth, and we suggest that the variable is mainly a control in our dataset. In that interpretation, it is interesting that the estimated coefficients of the other instruments are robust with respect to exclusion of CCyB from the empirical models. The main results are also robust to controls in the form of impulse indicator saturation (IIS), which we employed as a novel estimation method for macro panels.

Testing for news and noise in non-stationary time series subject to multiple historical revisions

This paper focuses on testing non-stationary real-time data for forecastability, i.e., whether data revisions reduce noise or are news, by putting data releases in vector-error correction forms. To deal with historical revisions which affect the whole vintage of time series due to redefinitions, methodological innovations etc., we employ the recently developed impulse indicator saturation approach, which involves potentially adding an indicator dummy for each observation to the model. We illustrate our procedures with the U.S. real GNP/GDP series of the Federal Reserve Bank of Philadelphia and find that revisions to this series neither reduce noise nor can be considered as news.

Testing for News and Noise in Non-Stationary Time Series Subject to Multiple Historical Revisions

This paper focuses on testing non-stationary real-time data for forecastability, i.e., whether data revisions reduce noise or are news, by putting data releases in vector-error correction forms. To deal with historical revisions which affect the whole vintage of time series due to redefinitions, methodological innovations etc., we employ the recently developed impulse indicator saturation approach, which involves potentially adding an indicator dummy for each observation to the model. We illustrate our procedures with the U.S. real GNP/GDP series of the Federal Reserve Bank of Philadelphia and find that revisions to this series neither reduce noise nor can be considered as news.

Recent Developments in Macro-Econometric Modeling: Theory and Applications

Developments in macro-econometrics have been evolving since the aftermath of the Second World War.[...]

Interpreting estimates of forecast bias

This paper resolves differences in results and interpretation between Ericsson’s (2017) and Gamber and Liebner’s (2017) assessments of forecasts of U.S. gross federal debt. As Gamber and Liebner (2017) discuss, heteroscedasticity could explain the empirical results in Ericsson (2017). However, the combined evidence in Ericsson (2017) and Gamber and Liebner (2017) supports the interpretation that these forecasts have significant time-varying biases. Both Ericsson (2017) and Gamber and Liebner (2017) advocate using impulse indicator saturation in empirical modeling.

How biased are U.S. government forecasts of the federal debt?

Government debt and its forecasts attracted considerable attention during the recent financial crisis. The current paper analyzes potential biases in different U.S. government agencies’ one-year-ahead forecasts of U.S. gross federal debt over 1984–2012. Standard tests typically fail to detect biases in these forecasts. However, impulse indicator saturation (IIS) detects economically large and highly significant time-varying biases, particularly at turning points in the business cycle. These biases do not appear to be politically related. IIS defines a generic procedure for examining forecast properties; it explains why standard tests fail to detect bias; and it provides a mechanism for potentially improving forecasts.