Multivariate Vector Autoregression Research Articles

Analysing the geographical impact on short-term solar irradiance forecasting using univariate and multivariate time series models

ABSTRACT Sustainable development is significantly hampered by air pollution brought on by greenhouse gas emissions. One of the primary contributors to air pollution is coal, which is used in thermal power plants. Substituting power plants from combustible sources to renewable energy sources can reduce air pollution to an extent. Solar energy is one of the most readily accessible renewable energy sources. High Global Horizontal Irradiance (GHI) is advantageous for deploying solar power plants, allowing maximum electricity production. However, the spatiotemporal factors affecting GHI are not analyzed, which can improve the forecasting accuracy. The proposed work analyzes the geographical impact of solar irradiation using statistical time-series models called ARIMA (Auto Regression Integrated Moving Average) and VAR (Vector Auto Regression) using four datasets from two different locations in India. The statistical time-series models can analyze the impact of the features and can show how the effect improves the performance of the multivariate model compared to the univariate model. The study aims to find the influence of the geographical factors of Global Horizontal Irradiance (GHI) based on the location chosen to build a solar power plant. The effectiveness of the univariate and multivariate models in forecasting solar irradiance is also discussed. The RMSE (root mean square error) analysis shows that the multivariate model VAR outperforms the univariate model in predicting solar irradiance. There is a 1–25% variance in the RMSE values between the ARIMA and VAR models at different locations and a 1–19% variance in MAE (Mean Absolute Error) value, while a 94–98% variance in MAPE (mean absolute percentage error) value. A deep analysis of factors that affect the precision of global horizontal irradiance (GHI) and the causal impact of variables is also analyzed using different variable impact tests. This variable selection is achieved by a feature selection algorithm called Maximum Relevance Minimum Redundancy (MRMR).

Identifying Central Negative Thoughts Using Experience Sampling and Network Analysis

PurposeNetwork analysis has promised to inform clinical practice about what should be prioritised in treating different psychological disorders. However, the pure phenomenological approach adopted by network analysis did not help advance this goal considerably. We propose a theoretical approach based on the cognitive model of psychopathology.MethodWe used a multivariate vector-autoregression model to analyse the networks of anxious symptoms, depressive symptoms, and negative automatic thoughts. In a preliminary study, we identified the most common negative thoughts and then monitored them alongside symptoms of anxiety and depression in a sample of undergraduate students three times per day for three weeks.ResultsResults revealed that negative thoughts have a high bridge outdegree in the temporal network (predict the occurrence of symptoms), while symptoms have a high bridge outdegree (are predicted by thoughts). Thoughts related to self-criticism, like “There is something wrong with me”, were the most central for both anxiety and depression and could be considered priority targets for cognitive interventions.ConclusionsAdopting a theoretical approach has proven helpful in providing concrete targets for therapy instead of just identifying central symptoms, as it is typically done in network studies. Future network studies could also consider adopting an approach based on a psychotherapeutic theory about the aetiology of psychopathology.

Physical Activity and Nighttime Sleep in Adolescents.

Previous studies show contradictory results regarding the bidirectional association between physical activity and nighttime sleep. The objective of the present study was to add knowledge to these possible relationships using autoregressive models. 214 adolescents (117 boys and 97 girls), with a mean age of 13.31 years agreed to participate. The study variables were measured with accelerometers for 7 full days over three consecutive years. The mlVAR package was used to compute estimates from multivariate vector autoregression models. The 5-delay models showed a better fit. Autoregressive effects were observed in sleep onset, sleep offset and sedentary behavior, which could explain the relationships found in previous studies between physical activity and sleep. Sleep onset, total sleep time, and sleep efficiency had direct effects on sedentary behavior. Moderate-to-vigorous physical activity was not related to any of the sleep variables. The hypothesis that there are bidirectional/reciprocal relationships between physical activity and sleep cannot be accepted.

Does globalization drive long-run inequality within OECD countries? A guide to policy making

We analyse around four decades of annual time-series data revisiting the long-run relationship between globalization and income inequality for 24 OECD member countries across different geographical regions, applying the Yamamoto-Kurozumi multivariate vector autoregression (VAR) framework. We observe that rapid globalization is not the key cause of rising long-run intra country inequality. This result is obtained by controlling for growth, terms of trade, minimum wage legislation, and unionization and found robust by further controlling education. Most of the countries in our study with a long-run relationship reveal the robust reverse causal impact of rising globalization on reducing inequality. Our impulse response breakdown across various sub-components of globalization suggests that economic globalization is not a primary contributor to long-run inequality for developed industrialized countries. Our framework guides future research to concentrate more on country-specific relationships, with policy guidance tailored for each country based on their level of economic development and institutional quality.

Ricardian equivalence or twin deficits hypothesis? Evidence from Serbia

This study investigates empirically whether higher budget deficits worsen the current account balance in Serbia, which is in line with the twin deficit hypothesis.This prediction is very different from Ricardian equivalence theory, which implies that a decrease in public savings is always anticipated via increased private savings. Hence, budget deficits have no impact on the current account balance. Based on quarterly data for the period between 2005 and 2020 and using a multivariate vector autoregression (VAR) model and a short-run structural VAR model, this paper confirms the twin deficit hypothesis in Serbia. More precisely, a 1 percentage point increase in the budget deficit (as a percentage of GDP) generates a 0.31 percentage point increase in the current account deficit (as a percentage of GDP). Moreover, the result is also confirmed using alternative estimation techniques (GMM and OLS method). According to these results, macroeconomic policymakers in Serbia should resort to policies that encourage fiscal consolidation to rectify or at least mitigate deterioration of the current account balance.

Using Econometric Models to Manage the Price Risk of Cocoa Beans: A Case from India

This study aims at developing econometric models to manage the price risk of Dry and Wet Cocoa beans with the help of ARIMA (Autoregressive Integrated Moving Average) and VAR (Vector Auto Regressive). The monthly price of Cocoa beans is collected for the period starting from April 2009 to March 2020 from the office of CAMPCO Limited, Mangalore, and the ICE Cocoa futures price from the website of investing.com. The augmented dickey fuller test is used to test the stationarity of the series. The ACF and PACF correlograms are used to identify the tentative ARIMA model. Akaike information criterion (AIC) and Schwarz criterion (SBIC), Sigma square, and adjusted R2 are used to decide on the optional AR and MA terms for the models. Durbin–Watson statistics and correlograms of the residuals are used to decide on the model’s goodness of fit. Identified optimal models were ARIMA (1, 1, 0) for the Dry Cocoa beans price series and ARIMA (1, 1, 2) for the Wet Cocoa beans price series. The multivariate VAR (1) model found that the US and London Cocoa futures prices traded on the ICE platform will influence the price of Dry Cocoa in India. This study will be helpful to forecast the price of Cocoa beans to manage the price risk, precisely for Cocoa traders, Chocolate manufacturers, Cocoa growers, and the government for planning and decision-making purposes.

Confidence is Key: A Study on Consumer Confidence and its Potential in the Recovering Philippine Economy

The Coronavirus Disease 2019 (COVID-19) Pandemic has caused major damage to the Philippine economy, resulting in a drastic -17.0% drop in aggregate output since 2019 and a downturned economy that could be felt by 2021. The impact of the pandemic could be felt at a household level, as 1.8 million Filipino families report to have become newly poor within the last 1 to 4 years, in addition to 17.6 million pre-existing families below the poverty threshold in 2018. The Philippine citizens and their government are hoping for economic recovery soon. This study provides an in-depth multivariate analysis of the role of selected monetary indicators and labor and commerce indicators in the consumer confidence of the general Philippine population using multi-variate regression and vector autoregression analyses. It also establishes a causal relationship of the latter to HFCE and GDP using the Granger causality test to determine which indicators affect general consumption the most in the context of recovery from the COVID Pandemic. The paper has successfully confirmed the significance of consumer confidence in the fluctuations of the Philippine economy in the short- and long-run, as well as systematically illustrates the relationship of consumer confidence with other macroeconomic variables. As the model shows, consumer confidence is heavily influenced by the country’s economic stability and the individual’s level of income, purchasing power, access to financial instruments, and ability to borrow.

Spillovers of U.S. monetary policy uncertainty on inflation targeting emerging economies

We investigate for spillovers from monetary policy uncertainty in the United States to the policy rates of seven inflation targeting emerging economies — Brazil, Chile, Colombia, Indonesia, Mexico, Poland, and South Africa. We use monthly data, with the start of the sample period being dictated by the start of the inflation targeting regime, and a multivariate GARCH-in-Mean vector autoregression (VAR), controlling for the traditional Taylor rule type variables. We also use a multivariate structural VAR and a different measure of U.S. monetary policy uncertainty, achieving identification by a combination of short-run and long-run restrictions. Our evidence shows that U.S. monetary policy uncertainty, irrespective of how it is measured, has negative effects on the macroeconomic and financial fundamentals of emerging economies.

Analysis of the impact of electric vehicle policy on supply and demand of U.S. states bioethanol market

This study aims to construct a quantitative model of the dynamic influence of electric vehicle sales in the United States on bioethanol consumption and bioethanol production. The final research results have shown that there is a long-term equilibrium relationship in our multivariate vector autoregression (VAR) model between bioethanol consumption and bioethanol production, gasoline price, corn price, and electric vehicle sales. It can be seen from the impulse response analysis that ethanol demand will strongly respond to one standard deviation of electric vehicle sales, and our new model fits the real data of ethanol supply and demand deviation well. Based on this newly long-term equilibrium relationship, we have constructed a policy simulation model to describe the quantitative relationship between the changes in the electric vehicle policy of each state and the temporal and spatial pattern of the national bioethanol resource allocation among US states. It can help and serve as a reference tool for policy makers to conduct quantitative policy evaluation.

An Empirical Relationship Between Real Effective Exchange Rate Volatility and Current Account Balances in Kenya

Deficits in the current account balances have been a common phenomenon in Kenya. Some scholars have attributed this trend to the volatility of the Kenyan shilling. Previous literatures on this issue have generated mixed results on the relationship between Kenya’s real effective exchange rate (REER) volatility and current account balances, and as such this study sought to resolve this controversy. This study investigated and mapped out the profile of Kenya’s REER volatility for the period 1972 to 2015 and interrogated its impact on current account balances (CAB) to fill the gap. The study was guided by Mundell-Fleming-Dornbusch model to measure REER volatility and the elasticity approach to assess the relationship between REER volatility and CAB. The study employed GARCH technique to measure REER volatility and Vector Autoregressive (VAR) model to examine the impact of REER on Current Account Balances. The study adopted Quantitative Research Design.  It relied on secondary data sourced from World Bank and International Monetary Fund. Unit root tests reported stationarity when variables were first differenced, while Cointegration test results based on Johansen’s maximum-likelihood procedure indicated that there is no evidence of cointegration among variables under consideration. The results of GARCH revealed that REER was indeed volatile (t-statistic 32.90 > 1.96). The results of multivariate VAR model analysis indicated that REER (-1) volatility impacted positively and insignificantly on CAB, while REER(-2) had a negative and insignificant effect on CAB for the sampled period. Findings of this study will help in the formulation of fiscal and monetary policies to address macro-economic shocks associated with REER volatility in the Kenyan economy. In light of these results, there is need to apply appropriate macroeconomic policy mix in the short run to mitigate the cyclical and short-term shocks that arise from REER volatility and correct the country’s unsustainable external imbalances in the long run.  

Data-driven models for predicting microbial water quality in the drinking water source using E. coli monitoring and hydrometeorological data

Rapid changes in microbial water quality in surface waters pose challenges for production of safe drinking water. If not treated to an acceptable level, microbial pathogens present in the drinking water can result in severe consequences for public health. The aim of this paper was to evaluate the suitability of data-driven models of different complexity for predicting the concentrations of E. coli in the river Göta älv at the water intake of the drinking water treatment plant in Gothenburg, Sweden. The objectives were to (i) assess how the complexity of the model affects the model performance; and (ii) identify relevant factors and assess their effect as predictors of E. coli levels. To forecast E. coli levels one day ahead, the data on laboratory measurements of E. coli and total coliforms, Colifast measurements of E. coli, water temperature, turbidity, precipitation, and water flow were used. The baseline approaches included Exponential Smoothing and ARIMA (Autoregressive Integrated Moving Average), which are commonly used univariate methods, and a naive baseline that used the previous observed value as its next prediction. Also, models common in the machine learning domain were included: LASSO (Least Absolute Shrinkage and Selection Operator) Regression and Random Forest, and a tool for optimising machine learning pipelines – TPOT (Tree-based Pipeline Optimization Tool). Also, a multivariate autoregressive model VAR (Vector Autoregression) was included. The models that included multiple predictors performed better than univariate models. Random Forest and TPOT resulted in higher performance but showed a tendency of overfitting. Water temperature, microbial concentrations upstream and at the water intake, and precipitation upstream were shown to be important predictors. Data-driven modelling enables water producers to interpret the measurements in the context of what concentrations can be expected based on the recent historic data, and thus identify unexplained deviations warranting further investigation of their origin.

Predicting total electron content in ionosphere using vector autoregression model during geomagnetic storm

Abstract The ionospheric total electron content (TEC) severely impacts the positional accuracy of a single frequency Global Positioning System (GPS) receiver at the equatorial latitudes. The ionosphere causes a frequency-dependent group delay in the GPS-ranging signals, which reduces the receiver’s accuracy. Further, the variations in TEC due to various space weather phenomena make the ionosphere’s behaviour nonhomogeneous and complex. Hence, developing an accurate forecast model that can track the dynamic behaviour of the ionosphere remains a challenge. However, advances in emerging data-driven algorithms have been found helpful in tracking non-stationary behavior in TEC. These models help forecast the delays in advance. The multivariate Vector Autoregression model (VAR) predicts the Ionospheric TEC in the proposed model. The prediction model uses input data compiled in real-time from the lag values of incoming TEC data and features extracted from TEC. The TEC is predicted in real-time and tested for different prediction intervals. The metrics – Mean Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used for testing and validating the accuracy of the model statistically. Testing the predicted output accuracy is also done with the dynamic time warping (DTW) algorithm by comparing it with the actual value obtained from the dual-frequency receiver. The model is tested for storm days of the year 2015 for Bangalore and Hyderabad stations and found to be reliable and accurate. A prediction interval of twenty-minute shows the highest accuracy with an error within 10 TECU for all the storm days.

Volatility transmission across international markets amid COVID 19 pandemic

PurposeThis paper aims to investigate the volatility transmission across stocks, gold and crude oil markets before and during the novel coronavirus (COVID-19) crisis.Design/methodology/approachA multivariate vector autoregression (VAR)-Baba, Engle, Kraft and Kroner generalized autoregressive conditional heteroskedasticity model (BEKK-GARCH) is used to assess volatility transmission across the examined markets. The sample is divided as follows. The first period ranging from 02/01/2019 to 10/03/2020 defines the pre-COVID-19 crisis. The second period is from 11/03/2020 to 05/10/2020, representing the COVID-19 crisis period. Then, a robustness test is used using exponential GARCH models after including an exogenous variable capturing the growth of COVID-19 confirmed death cases worldwide with the aim to test the accuracy of the VAR-BEKK-GARCH estimated results.FindingsResults indicate that the interconnectedness among the examined market has been intensified during the COVID-19 crisis, proving the lack of hedging opportunities. It is also found that stocks and Gold markets lead the crude oil market especially during the COVID-19 crisis, which explains the freefall of the crude oil price during the health crisis. Similarly, results show that Gold is most likely to act as a diversifier rather than a hedging tool during the current health crisis.Originality/valueAlthough the recent studies in the field focused on analyzing the relationships between different markets during the first quarter of 2020, this study considers a larger data set with the aim to assess the volatility transmission across the examined international markets Amid the COVID-19 crisis, while it shows the most significant impact on various financial markets compared to other diseases.

Electronic Corporate Tax, Aggregate Federally Collected Tax Revenues and Economic Growth: A Multivariate VAR Approach

There is the dearth of empirical evidence on the relationship between corporate tax, aggregate federally collected tax revenues and economic growth, predominantly in the post-electronic tax era. This study seeks to assess whether corporate taxes affects aggregate federally collected tax revenues and economic growth using quarterly time-series data as extracted from the official websites of the Federal Inland Revenue Service, National Bureau of Statistics (NBS) and Central Bank of Nigeria (CBN) Statistical Bulletin covering during the period 2015q1-2020q1. The Multivariate Vector Auto Regression result revealed that corporate taxes positively and significantly affect aggregate federally collected tax revenue; company income tax (CIT) and economic growth (RGDP) were statistically significant while petroleum profit tax (PPT) is statistically insignificant to economic growth (RGDP). In view of this result, it is recommended that the Nigerian government should diversify measures aimed at jettisoning leakages in the administration of corporate tax, particularly petroleum profit and company income taxes since they contribute significantly to the revenue base of the country. Moreover, more measures that are stringent should be put in place to revitalize and fully automate the tax systems for improved revenue generation. The study is limited to the post-electronic tax system payment in Nigeria which was occasioned by tremendous improvements in the tax administration system in the Country.

Yanrui Wu Comments on Financial Spillover in Emerging Asia: A Tale of Three Crises

Yanrui Wu, University of Western Australia Business School: This paper examines and compares three major crises, namely, the 2008–09 global financial crisis (GFC), the 2013 “taper tantrum” (TT), and the ongoing COVID-19 pandemic (C-19) and their potential spillover effects. The topic is interesting and timely given the current outbreak of COVID-19 in the world. The paper thus potentially adds to the literature and contributes to the understanding of the economic impacts of a major crisis or pandemic in the world.The paper is essentially an empirical exercise involving standard time-series econometric methods, that is, the GARCH BEKK process and the multivariate vector autoregression (M-VAR). The authors would most probably be aware that there are competing, more advanced techniques available. It would thus be helpful if these new developments are at least acknowledged in the paper. Otherwise, the chosen research methods in the paper are appropriate and acceptable.In the paper, the authors considered five asset types in ten countries (United States, Japan, and eight emerging Asian economies). They concluded that among the three crises, the COVID-19 pandemic has generated the largest spillover effect and the United States is a net transmitter of spillovers, particularly in bonds and equity markets. The detailed results are presented in Figures 1–3 of the paper. However, interpretation of these figures is largely based on virtual observation. It would be more convincing if the authors can conduct some statistical tests of statements like being “the largest transmitter” or “the largest receiver.” The three charts are based on 1 percent level of significance. Are the conclusions the same if a 5 percent level of significance is considered for instance?The authors also showed that TT was a main trigger of financial volatility in emerging Asian economies, particularly the bond markets. Although COVID-19 and its effects are still being assessed by economists, there must already be a large body of studies on the impacts of the GFC and TT. It would be instrumental if the findings in this paper could be compared with those in the existing literature, which I feel is underdocumented in this paper.The last part of the empirical exercise is the examination of impulse response functions (IRF). The authors concluded that “IRF shows that in general, the average period of transmission of spillover was around three to five days.” This is a key finding that is highlighted in several places (the abstract, conclusion, and so on). But it is difficult for readers to digest this, as relevant evidence is not presented in the paper perhaps due to space limitations. This is unfortunate.Finally, in the paper, the authors commented on policy implications, which I like. The message to emerging Asian economies and policymakers is noticeably clear that these economies should establish a concrete system of financial cooperation or ensure that the existing system truly functions during a crisis.

Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms

PurposeThe main purpose of this study is to forecast inflation rates in the case of the Turkish economy with shrinkage methods of machine learning algorithms.Design/methodology/approachThis paper compares the predictive ability of a set of machine learning techniques (ridge, lasso, ada lasso and elastic net) and a group of benchmark specifications (autoregressive integrated moving average (ARIMA) and multivariate vector autoregression (VAR) models) on the extensive dataset.FindingsResults suggest that shrinkage methods perform better for variable selection. It is also seen that lasso and elastic net algorithms outperform conventional econometric methods in the case of Turkish inflation. These algorithms choose the energy production variables, construction-sector measure, reel effective exchange rate and money market indicators as the most relevant variables for inflation forecasting.Originality/valueTurkish economy that is a typical emerging country has experienced two digit and high volatile inflation regime starting with the year 2017. This study contributes to the literature by introducing the machine learning techniques to forecast inflation in the Turkish economy. The study also compares the relative performance of machine learning techniques and different conventional methods to predict inflation in the Turkish economy and provide the empirical methodology offering the best predictive performance among their counterparts.

Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend

The surveillance and forecast of newly confirmed cases are important to mobilize medical resources and facilitate policymaking during a public health emergency. Digital surveillance using data available online has increasingly become a trend with the advancement of the Internet. In this study, we assessed the predictive value of multiple online medical behavioral data, including online medical consultation (OMC), online medical appointment (OMA), and online medical search (OMS) for the regional outbreak of coronavirus disease 2019 in Shenzhen, China during January 1, 2020 to March 5, 2020. Multivariate vector autoregression models were used for the prediction. The results identified a novel predictor, OMC, which can forecast the disease trend up to 2 days ahead of the official reports of confirmed cases from the local health department. OMS data had relatively weaker predictive power than OMC in our model, and OMA data failed to predict the confirmed cases. This study highlights the importance of OMC data and has implication in providing evidence-based guidelines for local authorities to evaluate risks and allocate resources during the pandemic.

Introducing uncertainty in a large scale agricultural economic model: A methodological overview

The analysis of uncertainty in large-scale agricultural economic models has gained attention from a policymakers and researchers’ viewpoint. The different methodologies available vary depending on data availability and the nature of the variables subject to analysis, which in turn influences the outcomes. When evaluating the results of previously applied partial stochastic methodologies to partial equilibrium models, underperformance and generation of biases have been observed. This paper evaluates different stochastic methods for introducing yield and macroeconomic uncertainty in a large-scale agricultural economic model and proposes a new methodology for partial uncertainty analysis consisting of a combination of parametric and non-parametric estimators chosen to minimize the statistical prediction error and distributional assumptions. Results suggest that the best methodologies are those relaxing distributional assumptions and allowing for a better representation of historical variability. For uncertainty extraction, the cubic polynomial (for yields) and the multivariate vector auto-regression (for macroeconomic variables) methods perform best. For uncertainty simulation, tests favor semi-parametric methods against parametric approaches. These methods are applied to the ex-ante analysis of global agricultural commodity markets and supplement the traditional deterministic analysis with a statistical representation of stochastic uncertainty.

THE ASYMMETRIC EFFECTS OF FLUCTUATIONS IN OIL PRICES ON THE PERFORMANCE OF THE LIBYAN ECONOMY: A VAR APPROACH

This paper examines the presence of asymmetry in the response of the Libyan economy to fluctuations in oil prices, subsequent to discovery of oil in the country. Three Vector Autoregressive (VAR) models are illustrated and estimated along with a multivariate rolling VAR approach. All sectors are found to react asymmetrically to shocks in oil prices over the 1962-2012 period. The magnitude of the adverse effect of the negative oil shocks on the manufacturing and agriculture sector appears to outweigh the positive effect of the positive oil shocks. The services sector, on the other hand, is able to overcome the shocks of the oil prices, due to absence of external competition. In addition, the results of the Multivariate rolling VAR highlight the existence of structural changes in the relationship between sectors of the Libyan economy and oil prices. The essay recommends implementing fiscal policy reform to de-link the real sector from fluctuations in oil prices. It also advises promoting the development of the financial sector in order for it to contribute in the diversification of the economy.

Public debt and fiscal sustainability: the cyclically adjusted balance in the case of Lebanon

ABSTRACT Lebanon’s high debt-to-GDP ratio and persistent overall budget deficit expose the entire financial system to vulnerability. Unless the country generates enough revenue to meet its gross financing needs, the prospect of borrowing more remains strong. Given the complexity of the Lebanese economy and debt structure, the formulation of fiscal and monetary policy is difficult and costly. Hence, policymakers are continuously confronted with difficult choices and must select the choice that is the least destructive. The choice between short-term economic stability and medium-term fiscal sustainability is a clear example of the dilemmas that confront policymakers. In addition, procyclicality and its repercussions on economic growth and the lack of responses to fiscal needs are matters of significant importance for public debt and fiscal sustainability. Hence, this study provides an in-depth analysis of the difficulties facing the Lebanese economy as it tries to sustain its public debt and the overall fiscal deficit. The study calculates the cyclically adjusted balance to analyze the appropriateness of fiscal policy and to assess the role of the automatic stabilizers. Additionally, the study employs a multivariate vector autoregression to forecast the variables in the simple debt model and to identify the impulses needed from various endogenous shocks. The derived results demonstrate that fiscal policy is predominately procyclical, that policymakers risked short-term economic stability for the sake of medium-term fiscal sustainability, and that unless the government adjusts its fiscal behavior, triggers automatic stabilizers, and negotiates a better deal with resident lenders, the economy will further deteriorate.