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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.

Oil market VAR models have become the standard tool for understanding the evolution of the real price of oil and its impact on the macro economy. As this literature has expanded at a rapid pace, it has become increasingly difficult for mainstream economists to understand the differences between alternative oil market models, let alone the basis for the sometimes divergent conclusions reached in the literature. The purpose of this survey is to provide a guide to this literature. Our focus is on the econometric foundations of the analysis of oil market models with special attention to the identifying assumptions and methods of inference.

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.

This chapter analyzes the exchange rate pass-through (ERPT) into different prices for 12 euro area (EA) countries. We provide new up-to-date estimates of ERPT by paying attention to either the time-series properties of data and variables endogeneity. Using VECM framework, we examine the pass-through at different stages along the distribution chain, that is, import prices, producer prices, and consumer prices. When carrying out impulse response functions analysis, we find a higher pass-through to import prices with a complete pass-through (after one year) detected for roughly half of EA countries. These estimates are relatively large compared to single-equation literature. We denote that the magnitude of the pass-through of exchange rate shocks declines along the distribution chain of pricing, with the modest effect recorded for consumer prices. When assessing for the determinant of cross-country differences in the ERPT, we find that inflation level, inflation volatility, and exchange rate persistence are the main macroeconomic factors influencing the pass-through almost along the pricing chain. Thereafter, we have tested for the decline of the response of consumer prices across EA countries. According to multivariate time-series Chow test, the stability of ERPT coefficients was rejected, and the impulse responses of consumer prices over 1990–2010 provide an evidence of general decline in rates of pass-through in most of the EA countries. Finally, using the historical decompositions, our results reveal that external factors, that is, exchange rate and import prices shocks, have had important inflationary impacts on inflation since 1999 compared to the pre-EMU period.

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 estimate the impact of changes in macro-economic conditions going forward, focusing on a change in interest policy, with regard to office letting and investment markets.

For this analysis, the authors constructed two vector-autoregressive models, measuring the response of office rents and capital values in Germany to economic impulses. The authors isolated effects of unique exogenous positive shocks (such as economic growth or interest leaps) on the basis of impulse-response functions in order to understand the complex dynamic interdependence between several economic factors and office performance changes.

The authors initially find a moderately positive development of both office performance components even although supposing an increase in interest level. In terms of capital values, the authors find that they do not drop before 1.5 years after the interest impulse and the negative effect peaks after approximately nine quarters. Furthermore, the reaction to a change in GDP is significantly lower than a reaction to the interest rate, but impulses in other macro-economic factors provoke stronger reactions. Finally, the authors find that a positive interest shock leads to a comparably robust development and economic sustainability in office rents throughout a consideration horizon of 24 quarters.

Estimations are based on observations from a time period containing two rather extraordinary market phases. As they included bubble growth and the low-interest environment, the authors find that certain patterns in both phases neutralize each other when looking at the total time frame. The authors constructed sub-samples to compensate for this. However, the research does not provide to what extent the measured impulse-responses stay forecast-proof, if the market moves into a phase of short-term normalization.

This paper provides insights into estimated impulse-response patterns on a hypothetical sudden increase of several macro-economic determinants. On this basis, the probable reaction to an increase in, for example, the interest rate level can be approximated. Also, the paper provides a fundamental understanding of the economic sustainability of German office properties in terms of their value and rent performance in the case of exogenous shocks.

This paper contains the first vector-autoregressive, impulse-response analysis of office markets in Germany in the context of several macro-economic drivers, including the interest level. It delivers insights into market reaction patterns on the basis of simulated one standard deviation shocks in all included variables.

A state space representation of a linearized DSGE model implies a VAR in terms of observable variables. The model is said be non-invertible if there exists no linear rotation of the VAR innovations which can recover the economic shocks. Non-invertibility arises when the observed variables fail to perfectly reveal the state variables of the model. The imperfect observation of the state drives a wedge between the VAR innovations and the deep shocks, potentially invalidating conclusions drawn from structural impulse response analysis in the VAR. The principal contribution of this chapter is to show that non-invertibility should not be thought of as an “either/or” proposition – even when a model has a non-invertibility, the wedge between VAR innovations and economic shocks may be small, and structural VARs may nonetheless perform reliably. As an increasingly popular example, so-called “news shocks” generate foresight about changes in future fundamentals – such as productivity, taxes, or government spending – and lead to an unassailable missing state variable problem and hence non-invertible VAR representations. Simulation evidence from a medium scale DSGE model augmented with news shocks about future productivity reveals that structural VAR methods often perform well in practice, in spite of a known non-invertibility. Impulse responses obtained from VARs closely correspond to the theoretical responses from the model, and the estimated VAR responses are successful in discriminating between alternative, nested specifications of the underlying DSGE model. Since the non-invertibility problem is, at its core, one of missing information, conditioning on more information, for example through factor augmented VARs, is shown to either ameliorate or eliminate invertibility problems altogether.

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 chapter studies the dynamic responses of the conditional quantiles and their applications in macroeconomics and finance. The authors build a multi-equation autoregressive conditional quantile model and propose a new construction of quantile impulse response functions (QIRFs). The tool set of QIRFs provides detailed distributional evolution of an outcome variable to economic shocks. The authors show the left tail of economic activity is the most responsive to monetary policy and financial shocks. The impacts of the shocks on Growth-at-Risk (the 5% quantile of economic activity) during the Global Financial Crisis are assessed. The authors also examine how the economy responds to a hypothetical financial distress scenario.

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.