scholarly journals Asymmetric and nonlinear inter-relations of US stock indices

2018 ◽  
Vol 14 (1) ◽  
pp. 78-129 ◽  
Author(s):  
Dimitrios Vortelinos ◽  
Konstantinos Gkillas (Gillas) ◽  
Costas Syriopoulos ◽  
Argyro Svingou
Keyword(s):  
Debt Crisis ◽  
Forecast Error ◽  
Error Variance ◽  
Asset Markets ◽  
Correction Model ◽  
Content Type ◽  
Stock Indices ◽  
The Us ◽  
Forecast Error Variance ◽  
Causality Model

Purpose The purpose of this paper is to examine the inter-relations among the US stock indices. Design/methodology/approach Data of nine US stock indices spanning a period of sixteen years (2000-2015) are employed for this purpose. Asymmetries are examined via an error correction model. Non-linear inter-relations are researched via Breitung’s nonlinear cointegration, a M-G nonlinear causality model, shocks to the forecast error variance, a shock spillover index and an asymmetric VAR-GARCH (VAR-ABEKK) approach. Findings The inter-relations are significant. The results are robust across all types of inter-relations. They are highest in the Lehman Brothers sub-period. Higher stability after the EU debt crisis, enhances independence and growth for the US stock indices. Originality/value To the best of the knowledge, this is the first study to examine the inter-relations of US stock indices. Most studies on inter-relations concentrate on the portfolio analysis to reveal diversification benefits among various asset markets internationally. Hence this study contributes to this literature on the inter-relations of a specific asset market (stock), and in a specific nation (USA). The evident inter-relations support the notion of diversification benefits in the US stock markets.

Testing cointegration between workers' remittances and human capital formation in Sri Lanka

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahamed Lebbe Mohamed Aslam ◽  
Selliah Sivarajasingham
Keyword(s):  
Human Capital ◽  
Sri Lanka ◽  
Function Analysis ◽  
Forecast Error ◽  
Error Variance ◽  
Capital Formation ◽  
Human Capital Formation ◽  
Causality Test ◽  
Content Type ◽  
Forecast Error Variance

PurposeThis study investigates the long-run relationship between workers' remittances and human capital formation in Sri Lanka by using the macro-level time series data during the period of 1975–2020.Design/methodology/approachIn this study, the augmented Dickey–Fuller (ADF) and Philips–Perron (PP) unit root tests, the autoregressive distributed lag (ARDL) bounds cointegration technique, the Granger causality test, the forecast error variance decomposition technique and impulse response function analysis were employed as the analytical techniques.FindingsIn accordance with the results of unit root tests, the variables used in this study are mixed order. Results of cointegration confirm that workers' remittances in Sri Lanka have both long-run and short-run beneficial relationship with human capital formation. The Granger causality test results indicate that there is a two-way causal relationship between workers' remittances and human capital formation. The results of forecast error variance decomposition expose that innovation of workers' remittances contributes to the forecast error variance in human capital in bell shape. Further, the empirical evidence of impulse response function analysis reveals that a positive standard deviation shock to workers' remittances has an immediate significant positive impact on human capital formation in Sri Lanka for a period of up to ten years.Practical implicationsThis research provides insights into the workers' remittances in human capital formation in Sri Lanka. The findings of this study provides evidence that workers' remittances help to produce human capital formation.Originality/valueBy using the ARDL Bounds cointegration and other techniques in Sri Lanka, this study fills an important gap in academic literature.

scholarly journals Tfp, News, and “Sentiments”: the International Transmission of Business Cycles

2018 ◽  
Vol 18 (1) ◽  
pp. 302-341 ◽  
Author(s):  
Andrei A Levchenko ◽  
Nitya Pandalai-Nayar
Keyword(s):  
United States ◽  
Business Cycle ◽  
Forecast Error ◽  
Error Variance ◽  
The United States ◽  
International Transmission ◽  
The Us ◽  
Forecast Error Variance ◽  
Short Run ◽  
Positive Shock

Abstract We propose a novel identification scheme for a nontechnology business cycle shock, which we label “sentiment”. This is a shock orthogonal to identified surprise and news TFP shocks that maximize the short-run forecast error variance of an expectational variable, alternatively a GDP forecast or a consumer confidence index. We then estimate the international transmission of three identified shocks—surprise TFP, news of future TFP, and sentiment—from the United States to Canada. The US sentiment shock produces a business cycle in the United States, with output, hours, and consumption rising following a positive shock, and accounts for the bulk of the US short-run business cycle fluctuations. The sentiment shock also has a significant impact on Canadian macroaggregates. In the short run, it is more important than either the surprise or the news TFP shocks in generating business cycle comovement between the United States and Canada, accounting for over 40% of the forecast error variance of Canadian GDP and over one-third of Canadian hours, imports, and exports. The news shock is responsible for some comovement at 5–10 years, and surprise TFP innovations do not generate synchronization. We provide a simple theoretical framework to illustrate how the US sentiment shocks can transmit to Canada.

The time-varying and volatile macroeconomic effects of immigration

2021 ◽  
pp. 1-21
Author(s):  
Huachen Li
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Time Varying ◽  
Vector Autoregressions ◽  
Hours Worked ◽  
Supply Shock ◽  
The Us ◽  
Forecast Error Variance ◽  
The Business Cycle ◽  
The Impact

Abstract This paper studies the impact of immigration on the US macroeconomy. I identify structural vector autoregressions (SVARs) with time-varying parameters (TVPs) and stochastic volatility (SV) using a novel set of restrictions. The TVP-SV-SVARs are estimated on a quarterly sample including average labor productivity (ALP), hours worked, immigration, consumption, and term spread from 1953 to 2017. An immigration supply shock increases domestic ALP and hours worked over the business cycle horizons. Movements in immigration are explained by its own shock and to a lesser extent by the productivity and news shocks. IRFs driven by these shocks vary over the sample, especially around changes in immigration policy such as the Immigration Act of 1990. In contrast, the forecast error variance decompositions exhibit little change over the sample. Immigration plays an important role in the US macroeconomy.

Smart advertising in prescription only medication; aligning it with prescriber’s or consumer’s behavior

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mesay Moges Menebo
Keyword(s):  
Forecast Error ◽  
Unit Root Test ◽  
Empirical Support ◽  
Error Variance ◽  
Model Specification ◽  
Dynamic Interactions ◽  
Carryover Effect ◽  
Content Type ◽  
Forecast Error Variance ◽  
Market Leader

Purpose This study has four objectives. First is to investigate and compare the immediate and carryover effects of four pharmaceutical marketing tools (prescriber detailing, medical events, journal ads and direct-to-consumer advertising [DTCA]) on sales. Based on the effect comparisons, the second objective is to determine whether advertising tools that are more compatible with prescriber’s behavior have superior impact on sales. Third is to examine empirical support for the argument that advertising directly to consumers, as a market follower versus leader, has a backfiring effect. Finally, this paper aims to assess the magnitude of variance in sales as a function of each advertising tool. Design/methodology/approach Data on unit sales and spending (on DTCA, journal ads, events and detailing) ranging 84 months are obtained for six prescription-only cholesterol-reducing brands. First, linearity is checked. Second, evolution versus stationarity is tested by applying the unit-root test. Third, potential endogeneity among variables is assessed with granger causality. Fourth, vector autoregressive model (VAR) that accounts for endogeneity and dynamic interactions is specified. Intercept, seasons and market share are added into the model specification as exogenous variables. Fifth, VAR with akaike selected lags and generalized impulse response are conducted. Finally, sales variance is decomposed with forecast error variance decomposition and Cholesky ordering. Findings A 10% increase on detailing or journal ads spending brought an immediate (one month) negative effect on sales in a market leader, whereas that same increase is insignificant in a market follower. A 10% increase on DTCA (vs detailing) spending led to a negative (vs positive) carryover effect for the market follower, giving empirical support to the backfiring effect of DTCA and partial evidentiary support suggested about prescriber friendly advertising. However, DTCA induces a larger short term and longer carryover effect in a market leader, with seven times more effect on sales than what detailing does. In addition, it explains 50% of the variation in sales. Originality/value The model applied captures extensive dynamics; hence, findings are robust. The analysis considered comparison in terms of prescriber friendly (vs not) advertising tools and brand market status and thus can make managers rethink strategy of advertising budget allocations. This study also introduced a new look onto DTCA and hence challenges the traditional thought held on consumer advertising response.

scholarly journals Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Syed Jawad Hussain Shahzad ◽  
Elie Bouri ◽  
Ladislav Kristoufek ◽  
Tareq Saeed
Keyword(s):  
Stock Market ◽  
Network Structure ◽  
Forecast Error ◽  
Common Factor ◽  
Error Variance ◽  
Stock Market Index ◽  
Vector Autoregressive ◽  
The Us ◽  
Market State ◽  
Forecast Error Variance

AbstractThe aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak. To this end, we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns. Notably, we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network. The results show that the network structure and spillovers differ considerably with respect to the market state. During stable times, the network shows a nice sectoral clustering structure which, however, changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure. The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated. The sectoral topology thus has not collapsed into a unified market during the pandemic.

The influence of economic policy uncertainty and geopolitical risk on US citizens overseas air passenger travel by regional destination

2020 ◽  
pp. 135481662098119
Author(s):  
James E Payne ◽  
Nicholas Apergis
Keyword(s):  
Economic Policy ◽  
Forecast Error ◽  
Error Variance ◽  
Variance Decomposition ◽  
Air Travel ◽  
Policy Uncertainty ◽  
Economic Policy Uncertainty ◽  
Passenger Travel ◽  
Forecast Error Variance

This research note extends the literature on the role of economic policy uncertainty and geopolitical risk on US citizens overseas air travel through the examination of the forecast error variance decomposition of total overseas air travel and by regional destination. Our empirical findings indicate that across regional destinations, US economic policy uncertainty explains more of the forecast error variance of US overseas air travel, followed by geopolitical risk with global economic policy uncertainty explaining a much smaller percentage of the forecast error variance.

scholarly journals Predicting Water Availability in Water Bodies under the Influence of Precipitation and Water Management Actions Using VAR/VECM/LSTM

Climate ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 144
Author(s):  
Harleen Kaur ◽  
Mohammad Afshar Alam ◽  
Saleha Mariyam ◽  
Bhavya Alankar ◽  
Ritu Chauhan ◽  
...  
Keyword(s):  
Water Management ◽  
Short Term Memory ◽  
Forecast Error ◽  
Error Variance ◽  
Water Levels ◽  
Water Bodies ◽  
Economic Activities ◽  
Management Actions ◽  
Forecast Error Variance ◽  
Water Treatments

Recently, awareness about the significance of water management has risen as population growth and global warming increase, and economic activities and land use continue to stress our water resources. In addition, global water sustenance efforts are crippled by capital-intensive water treatments and water reclamation projects. In this paper, a study of water bodies to predict the amount of water in each water body using identifiable unique features and to assess the behavior of these features on others in the event of shock was undertaken. A comparative study, using a parametric model, was conducted among Vector Autoregression (VAR), the Vector Error Correction Model (VECM), and the Long Short-Term Memory (LSTM) model for determining the change in water level and water flow of water bodies. Besides, orthogonalized impulse responses (OIR) and forecast error variance decompositions (FEVD) explaining the evolution of water levels and flow rates, the study shows the significance of VAR/VECM models over LSTM. It was found that on some water bodies, the VAR model gave reliable results. In contrast, water bodies such as water springs gave mixed results of VAR/VECM.

scholarly journals Oil Prices and the U.S. Dollar Exchange Rate: Evidence from the Monetary Model

2018 ◽  
Vol 10 (4) ◽  
pp. 17
Author(s):  
Moayad H. Al Rasasi
Keyword(s):  
Exchange Rate ◽  
Forecast Error ◽  
Negative Relationship ◽  
Impulse Response Function ◽  
Decomposition Analysis ◽  
Oil Prices ◽  
Error Variance ◽  
Monetary Model ◽  
Forecast Error Variance ◽  
Positive Shock

This paper analyzes how changes in global oil prices affect the US dollar (USD) exchange rate based on the monetary model of exchange rate. We find evidence indicating a negative relationship between oil prices and the USD exchange rate against 12 currencies. Specifically, the analysis of the impulse response function shows that the depreciation rate of the USD exchange rate ranges between 0.002 and 0.018 percentage points as a result of a one-standard deviation positive shock to the real price of crude oil. In the same vein, the forecast error variance decomposition analysis reveals that variation in the USD exchange rate is largely attributable to changes in the price of oil rather than monetary fundamentals. In last, the out-of-sample forecast exercise indicates that oil prices enhance the predictability power of the monetary model of exchange rate.

scholarly journals KAJIAN AKTIVITAS EKONOMI LUAR NEGERI INDONESIA TERHADAP PERTUMBUHAN EKONOMI INDONESIA PERIODE 1998-2014

2017 ◽  
Vol 9 (2) ◽  
pp. 119
Author(s):  
Ryan Hawari ◽  
Fitri Kartiasih
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Exchange Rate ◽  
Direct Investment ◽  
Forecast Error ◽  
Foreign Debt ◽  
Trade Openness ◽  
Impulse Response Function ◽  
Error Variance ◽  
Forecast Error Variance

Indonesia is a developing country which adopts an “open economic”. That caused Indonesia economic is strongly influenced by factors that come from outside of Indonesia. External factors in this research is referred to foreign debt, foreign direct investment, trade openness and exchange rate of rupiah with USD. The analytical method in this research used Vector Error Correction Model (VECM) which will focused on Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD). Based on result of IRF, exchange rate had a positive effect to economic growth, while foreign debt, foreign direct investment and trade openness had a negative effect to economic growth. Based on result of FEVD, shock on economic growth in Indonesia affected by economic growth itself (43.21%), followed by foreign debt (26.30%), trade openness (14.16%), foreign direct investment (8.29%) and exchange rate (8.04%) Keywords: economic growth, trade openness, VECM, IRF, FEVD

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