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This article investigates the robustness of impulse response estimators to near unit roots and near cointegration in vector autoregressive (VAR) models. We compare estimators based on VAR specifications determined by pretests for unit roots and cointegration as well as unrestricted VAR specifications in levels. Our main finding is that the impulse response estimators obtained from the levels specification tend to be most robust when the magnitude of the roots is not known. The pretest specification works well only when the restrictions imposed by the model are satisfied. Its performance deteriorates even for small deviations from the exact unit root for one or more model variables. We illustrate the practical relevance of our results through simulation examples and an empirical application.

Social media marketing has become a powerful strategic tool for many brands, but scholarly research in this domain is still in its infancy. This study aims to examine the effects of social media marketing activities on consumer online impulse buying intentions via brand resonance and emotional responses by incorporating the direct and moderating effects of social network proneness toward fashion retail brands.

By using snowball sampling, this study recruited 441 netizens (who were using fashion retail brands) and obtained their responses through an online survey. Structural equation modeling was applied to 394 responses for analysis.

The findings discovered that social media marketing activities significantly influenced brand resonance, consumer emotional responses and online impulse buying intentions. Likewise, brand resonance and emotional responses were positively associated with online impulse buying intentions and acted as decisive mediators. Social network proneness’s direct and moderating effects significantly increased consumer online impulse-buying intentions toward fashion retail brands.

This study provides recommendations to retail managers for creating and executing brand positioning, segmenting and targeting strategies to enhance consumers’ intentions for engaging in online impulsive purchases for fashion brands.

This original research contributes to the branding literature and stimulus–organism–response theory by focusing on social media marketing activities, brand resonance, emotional responses, social network proneness and consumer online impulse buying intentions toward fashion retail brands.

This paper examines correlations of the underlying structural shocks and the degree of synchronization in the impulse responses of output, inflation and trade to a one standard deviation shock to non-oil commodities price index and exchange rates within the West African Monetary Zone (WAMZ) countries from 1990q1 to 2020q1.

This paper uses the structural vector autoregressive model to isolate the underlying structural shocks and compares them with the West African Monetary Union (WAEMU) countries.

Findings from the study suggest that correlations of underlying structural shocks are more profound in the WAEMU than in the WAMZ. Impulse responses of output to price and exchange rate shocks are more symmetric in the WAEMU than in the WAMZ. However, impulse responses of inflation to price and exchange rate shocks are symmetric in the WAMZ than in the WAEMU and responses of trade in both sub-groups are not uniform.

The paper concludes that the WAMZ does not constitute an Optimum Currency Area concerning the correlations of the structural shocks and output. However, it has achieved convergence in inflation and there are adequate adjustment mechanisms to shocks in the WAMZ than in the WAEMU. Therefore, the WAMZ may not suffer from joining the monetary union. Thus, economic Community of West African States may take steps to roll out the monetary union.

The paper examines correlations of the underlying structural shocks, impulse responses of output and inflation to shocks to commodities price and exchange rates in the WAMZ and compares them with the WAEMU.

This study examines the dynamic responses of five different daily energy prices to a pulse shock affecting the daily price of oil.

Daily data for energy prices from the Federal Reserve Economic Data (FRED) database for January 7, 1997, through February 8, 2021, are analyzed. A bivariate structural vector error correction model and generalized autoregressive conditionally heteroscedastic model are combined and extended by adding the volatility of the growth rate of daily oil prices as an explanatory variable for the growth rates of energy prices. This model is estimated and used to generate impulse responses for energy prices.

The empirical results show that the levels of the daily energy prices examined have unit roots, are integrated of order one, are cointegrated, and generally revert slowly to their long-term equilibrium relationships with the price of oil. The growth rates for the daily energy prices have autoregressive conditional heteroscedasticity, generally are positively related to the volatility of daily oil prices, respond quickly to a pulse shock to daily oil prices, and have cumulative responses that last at least one month.

This paper allows for simultaneous estimation of extended bivariate structural vector error correction and generalized autoregressive conditionally heteroscedastic models that include the volatility of oil as an explanatory variable and uses these models to generated cumulative impulse responses for the growth rates of daily energy prices to oil price shocks.

We examine the informational roles of trades and time between trades in the domestic and overseas US Treasury markets. A vector autoregressive model is employed to assess the information content of trades and time duration between trades. We find significant impacts of trades and time duration between trades on price changes. Larger trade size induces greater price revision and return volatility, and higher trading intensity is associated with a greater price impact of trades, a faster price adjustment to new information and higher volatility. Higher informed trading and lower liquidity contribute to larger bid–ask spreads off the regular daytime trading period.

The purpose of this paper is to examine the dynamic relationships among investment, earnings and dividends for US firms. The sample period is 1950-2006.

The authors use a firm-level vector auto-regression (VAR) framework to examine the firm-level dynamics among investment, earnings and dividends. The firm-level VAR yields Granger causality results, impulse response functions, and variance decompositions characterizing the dynamics of these three variables at the firm level.

For the average firm in the sample, Miller and Modigliani dividend policy irrelevance is not supported, even in the long run; the shocks to dividends do have long-run consequences for investment and vice versa. Dividend changes are an ineffective signal of future earnings in both the short and long-term. The cost of an increased dividend is on average an immediate decrease of $3 in investment for every dollar increase in dividends and the effect is persistent up to six years after the increase in dividends.

The firm-level VAR used in the study requires that sample firms have long histories of investment, earnings and dividend data. The study addresses the interaction between dividends and investment and therefore necessitates examining dividend-paying firms. By the nature of the research question, the sample firms will not be representative in all respects to the universe of firms. The most striking difference between the sample and the universe of firms is firm size. As such, the study's conclusions are most applicable to larger, stable, dividend-paying firms. The study is also limited to dividend payout. Alternative payout policies, such as share repurchases, are not considered in this work.

In theory, increases in dividends can signal higher future earnings; however, the evidence does not support this hypothesis. When capital markets are constrained or incomplete, increases in dividends come at a cost to investment. Firms should consider alternative methods of signaling future earnings that have less of an impact on investment. Investors should carefully evaluate the possible impact of an increase in dividends on investment and future earnings growth.

This study is the first to examine the dynamics of earnings, dividends and investment at a firm level and over such a long sample period. By including the dynamics of earnings, the authors emphasize the potential opportunity costs that increasing dividends has on investment when capital markets are imperfect. The dynamic system also allows the authors to consider long-run effects as well as immediate responses to system shocks.

This study first calculates a profit rate for China’s economy over the period 1952–2014; the rate shows a downward trend in the long term but also exhibits cyclical fluctuations. Then, structural vector autoregressive models are used to examine the Chinese economic structure and, thanks to impulse response functions, the role of the profit rate in investment, capital accumulation, and GDP growth rates. Then, based on a priori constraints relative to this structure, the study tests whether these assumptions are verified over the period studied in the context of the transformations of China. The impulse response functions are further examined by using Bayesian analysis. Finally, the authors conclude that the period from 1952 to 2014 should be divided into several sub-periods with distinct structural characteristics.

The purpose of this paper is to investigate the impact of a consumer confidence shock on GDP and different types of consumer spending during a slack state as well as a non-slack state of an economy.

The authors use the US quarterly data from 1960Q1 to 2014Q4 and apply Jorda’s (2005) local projection method to compute the impulse responses of macroeconomic variables to a consumer confidence shock. The local projection method allows us to include non-linearities in the response function.

In general, the response of output, following a consumer confidence shock, is similar in slack and non-slack states and indicate that an unfavorable confidence shock is contractionary. However, the intensity and duration of impact of a confidence shock on different components of spending are state dependent. Overall, a negative confidence shock appears to have a stronger impact on non-slack time than on a slack time.

Policy makers should be careful about undertaking a policy action that may affect consumer confidence adversely, particularly during an economic good time. An adverse confidence shock can trigger a downfall in a well-functioning economy and the dampening effect may last for several quarters before the economy rebounds.

US economy is subject to fluctuations; however, the literature on the impact of confidence shock in different economic states is limited. The incremental contribution of this paper is that it investigates how the consumers respond to the confidence shock in a state-dependent model. Furthermore, the authors use a more robust and alternative estimation method that tackles any non-linear problems.

Private residential construction spending (PRRESCON) is an important indicator for assessing housing supply/demand and economic strength. Currently, there are no comprehensive studies on PRRESCON forecasting. This study aims to address the gap in knowledge by conducting a comprehensive exploration of indicators for PRRESCON using time series methods.

Granger causality test trials were conducted between PRRESCON and all of its potential indicators before the vector autoregression model was implemented. Extensive effort was exerted toward model interpretation in the form of impulse–response functions.

Impulse–response functions indicated that the escalation of labor supply, material/construction costs and issued building permits at any given time consistently had a positive impact on PRRESCON 10–11 months later, with a 95% confidence interval. Conversely, the unemployment rate and housing value escalations at any given time were found to have a negative impact on PRRESCON 10–11 months later in more than 95% of the instances. Furthermore, material/construction cost escalations at any given time were shown to have a negative impact on PRRESCON 7 months later in more than 95% of the instances.

Current forecasting literature on construction spending focuses exclusively on the parameter’s relationship with gross domestic product and the architectural billing index. This study reveals many additional indicators, many of which are directly related to the implementation of housing development projects. The paper is also the first in the body of forecasting literature, to the best of the authors’ knowledge, to conduct impulse–response analysis on residential construction spending.

The purpose of this paper is to propose a fast, accurate and efficient algorithm for assessment of transient behavior of grounding grids buried in horizontal multilayered earth model considering soil ionization effect.

The purpose of this paper is to develop a numerical simulation method to calculate the lightning impulse response of the grounding grid buried in a horizontal multilayered earth model. The mathematical model about the hybrid method based on PI basic function belonging to time domain is proposed in the paper; the mode can precisely calculate the lightning current distribution and lightning impulse response to grounding grids buried in horizontal multilayered soil model considering soil ionization effect. To increase computing efficiency, quasi-static complex image method (QSCIM) and its time-domain Green’s function closed form are introduced in the model.

The hybrid model is rather stable, with the respect to the number of elements used and with excellent convergence rate. In addition, because this mathematical model belongs to the time domain algorithm, it is very powerful for the simulation of soil ionization caused by high amplitude lightning current.

To increase computing efficiency, QSCIM and its time domain Green's function closed form are introduced in the model.

The mathematical model about the hybrid method based on PI basic function can precisely calculate the lightning current distribution and lightning impulse response to grounding grids buried in horizontal multilayered soil model considering the soil ionization effect.

Considering the soil ionization effect, the simulation calculation of lightning impulse response of substation grounding grid buried in the actual horizontal multilayered earth can effectively support the scientific and efficient design of lightning protection performance of substation grounding grid.

The hybrid model in time domain is originally developed by the authors and used to precisely calculate the lightning current distribution and lightning impulse response to grounding grids buried in horizontal multilayered soil model considering soil ionization effect. It is simple and very efficient and can easily be extended to arbitrary grounding configurations.