Estimation Of Impulse Response Function Research Articles

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels

Ridge regularized estimation of VAR models for inference

Ridge regression is a popular method for dense least squares regularization. In this article, ridge regression is studied in the context of VAR model estimation and inference. The implications of anisotropic penalization are discussed, and a comparison is made with Bayesian ridge‐type estimators. The asymptotic distribution and the properties of cross‐validation techniques are analyzed. Finally, the estimation of impulse response functions is evaluated with Monte Carlo simulations and ridge regression is compared with a number of similar and competing methods.

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels

The connectedness and risk spillovers between bitcoin spot and futures markets: evidence from intraday data.

This paper examines the dynamic relation between Bitcoin spot and futures markets during the Covid-19 pandemic. Using hourly data from 2020 combined with quantile impulse response analysis and predictability in the distribution test, we attempt to ascertain whether spot or futures markets lead in the price discovery process under a variety of market conditions. Granger predictability based on the left tail, the right tail, and the center of the distribution show bidirectional predictability between spot and futures markets suggesting significant feedback effects following normal and extreme gains/losses where neither market dominates in price discovery. Using a CAViaR model and the associated impulse response functions with estimates for dynamic tail dependence, we document spillovers between quantiles of spot and futures returns. Estimates of impulse response functions at various risk levels show the futures market has an edge in influencing the spot market and figures more prominently in the price discovery process.

Does R&D tax credit impact firm behaviour? Micro evidence for Portugal

AbstractIn this study, we use panel data to analyse the impact of an R&D tax credit on R&D personnel, particularly the impact on Ph.D. holders allocation, comparing low R&D intensity firms with medium-high and high R&D intensity firms. The results show that, in medium-high and high R&D intensity firms, the R&D tax credit had a significant impact on allocating Ph.D. holders in firms after 3 years of participation in the tax incentive scheme. We use a database covering 7,710 firms that performed R&D at least once in Portugal over the 23-year period 1995 to 2017, provided by the official business R&D survey data and a database of firms that applied for tax credit incentives at least once in the same period. Based on the estimation of impulse-response functions by local projections, we assess the impact of introducing the tax incentive scheme for corporate R&D in firms from different R&D intensity sectors.

A Comparison of Approaches to Select the Informativeness of Priors in BVARs

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.

Estimation of Impulse Response Functions When Shocks Are Observed at a Higher Frequency Than Outcome Variables

This article proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions in a setup where (i) the shock of interest is observed, (ii) the impact variable of interest is observed at a lower frequency (as a temporally aggregated or sequentially sampled variable), (iii) the data generating process (DGP) is given by a VAR model at the frequency of the shock, and (iv) the full set of relevant endogenous variables entering the DGP is unknown or unobserved. Consistency and asymptotic normality of the proposed MFDL estimators is established, and their small-sample performance is documented by a set of Monte Carlo experiments. The usefulness of MFDL estimator is then illustrated in three empirical applications: (i) the daily pass-through of shocks to crude oil prices observed at the daily frequency to U.S. gasoline consumer prices observed at the weekly frequency, (ii) the impact of shocks to global investors’ risk appetite on global capital flows, and (iii) the impact of monetary policy shocks on real activity.

Structural Damage Diagnosis-Oriented Impulse Response Function Estimation under Seismic Excitations.

Impulse response function (IRF) is an ideal structural damage index for the identification of structural damage associated with changes in modal properties. However, IRFs from multiple excitations applied at different degrees-of-freedoms jointly contribute to the dynamic response, and their estimation is often underdetermined. Although some efforts have been devoted to the estimation of IRF for a structure under single excitation, the case under multiple excitations has not been fully investigated yet. The estimation of IRF under multiple excitations is generally an ill-conditioned inverse problem such that an incorrect or non-feasible solution is common, preventing its application to damage detection. This work explores this problem by introducing dimensionality reduction transformation matrices relating two sets of IRFs of a structure with discussions on the performance of the non-unique transformation matrices. Then, the extraction of IRF via wavelet-based and Tikhonov regularization-based methods are compared. Finally, a numerical study with a truss structure is conducted to validate the estimation of the IRFs and to demonstrate their applicability for damage detection under seismic excitations. Both the damage locations and severity are accurately identified, indicating the proposed methodology can enable the IRFs estimation under multiple excitations for successful damage detection.

The effect of financial crises on air pollutant emissions: An assessment of the short vs. medium-term effects

This paper empirically investigates the impact of financial crises on air pollutant emissions (CO2, SO2, NOx and PM2.5). A panel data approach is used, including 419 financial crisis episodes in >150 countries over the period 1970–2014. The short- and medium-term effects of crises are estimated, using a GMM specification (for short-term) and the estimation of impulse response functions (for medium-term). Results show that in the short-term, as a consequence financial crises, emissions decrease for all gases except for PM2.5. In particular, emissions of CO2, SO2 and NOx decrease by 2.6, 1.8, and 1.7% respectively. However, in the medium-term, financial crises cause insignificant effect on emissions, or in some cases even lead to a 1–2% increase, cancelling out the initial benefit. Our analysis also shows that the effect of crises is larger in high income and upper-middle income countries. Moreover, recent crises had a larger short-term impact on air pollutants than crises in previous decades. Our results suggest that the beneficial impact of financial crises on air quality is short-lived. To preserve this beneficial impact in the long run and avert new negative post-crisis emission patterns and dynamics, policy responses to financial crises should encompass tighter environmental regulations and green investments.

‘Indian Stock Market Volatility’: A Study of Inter-linkages and Spillover Effects

The article attempts to examine interdependence between Indian stock market and other domestic financial markets, namely, foreign exchange market, bullion market, money market, and also Foreign Institutional Investor (FII) trade and foreign stock markets comprising one regional stock market represented by Nikkei of Japan and other stock market for the rest of the world represented by Standard & Poor’s (S&P) 500 of the USA. Attempts are also made to examine asymmetric volatility spillover, first, between the Indian stock market and other domestic financial markets and second, between the Indian stock market and global stock markets (represented by Nikkei and S&P 500) along with the foreign exchange market. To measure linear interdependence among multiple time series of financial markets multivariate Vector Autoregression (VAR) analysis, Granger causality test, impulse response function and variance decomposition techniques are used. For estima-ting the volatility spillover among the aforesaid markets Dynamic Conditional Correlation-Multivriate-Threshold Autoregressive Condi-tional Heteroscedastic (DCC-MV-TARCH) (1, 1) model is applied on daily data for a quite long period of time from 01 April 1996 to 31 March 2012. The results of multi­variate VAR analysis, Granger causality test, variance decomposition analysis and impulse response function estimation establish significant interdependence between domestic stock market and different other financial markets in India and abroad. The results of DCC-MV-TARCH (1, 1) model estimation further show signi- ficant asymmetric volatility spillover between the domestic stock market and the foreign exchange market and also from the domestic stock market to bullion market and changes in gross volume of FII trade. We also find (a) both way asymmetric volatility spillover between the domestic stock market and the Asian stock market and (b) its unidirectional movement from the world stock market to the domestic stock market. The results of the study may help market regulators in setting regulatory policies considering the inter-linkages and pattern of volatility spillovers across different financial markets. JEL Classification: G15, G17

Estimation of Impulse Response Functions When Shocks are Observed at a Higher Frequency than Outcome Variables

This paper proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions (IRFs) in a setup where (i) the shock of interest is observed, (ii) the impact variable of interest is observed at a lower frequency (as a temporally aggregated or sequentially sampled variable), (iii) the data-generating process (DGP) is given by a VAR model at the frequency of the shock, and (iv) the full set of relevant endogenous variables entering the DGP is unknown or unobserved. Consistency and asymptotic normality of the proposed MFDL estimators is established, and their small-sample performance is documented by a set of Monte Carlo experiments. The proposed approach is then applied to estimate the daily pass-through of changes in crude oil prices observed at a daily frequency to U.S. gasoline consumer prices observed at a weekly frequency. We find that the pass-through is fast, with about 28% of the crude oil price changes passed through to retail gasoline prices within five working days, and that the speed of the pass-through has increased over time.

Estimation of Impulse Response Functions When Shocks are Observed at a Higher Frequency than Outcome Variables

This paper proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions (IRFs) in a setup where (i) the shock of interest is observed, (ii) the impact variable of interest is observed at a lower frequency (as a temporally aggregated or sequentially sampled variable), (iii) the data generating process (DGP) is given by a VAR model at the frequency of the shock, and (iv) the full set of relevant endogenous variables entering the DGP is unknown or unobserved. Consistency and asymptotic normality of the proposed MFDL estimators is established, and their small-sample performance is documented by a set of Monte Carlo experiments. The proposed approach is then applied to estimate the daily pass-through of changes in crude oil prices observed at the daily frequency to U.S. gasoline consumer prices observed at the weekly frequency. We find that the pass-through is fast, with about 23% of the crude oil price changes passed through to retail gasoline prices within five working days, representing about 42% of the long-run pass-through. JEL Classification: C22

Estimation of impulse response functions in two-output systems

A single input–double output (SIDO) linear time-invariant (LTI) system is considered, whose impulse response function (IRF) is assumed to have one unknown component. The problem is to estimate this unknown component after observations of the second component. Both IRF’s components are supposed to be L2-integrable, and the estimation is made by cross-correlating the outputs, given that the input is a standard Wiener process on R. Weak asymptotic normality of appropriately centred estimators is proved.

Simultaneous confidence bands: Theory, implementation, and an application to SVARs

SummarySimultaneous confidence bands are versatile tools for visualizing estimation uncertainty for parameter vectors, such as impulse response functions. In linear models, it is known that that the sup‐t confidence band is narrower than commonly used alternatives—for example, Bonferroni and projection bands. We show that the same ranking applies asymptotically even in general nonlinear models, such as vector autoregressions (VARs). Moreover, we provide further justification for the sup‐t band by showing that it is the optimal default choice when the researcher does not know the audience's preferences. Complementing existing plug‐in and bootstrap implementations, we propose a computationally convenient Bayesian sup‐t band with exact finite‐sample simultaneous credibility. In an application to structural VAR impulse response function estimation, the sup‐t band—which has been surprisingly overlooked in this setting—is at least 35% narrower than other off‐the‐shelf simultaneous bands.

The impact of air transportation, railways transportation, and port container traffic on energy demand, customs duty, and economic growth: Evidence from a panel of low-, middle-, and high -income countries

The transportation sector is highly sensitive due to the excessive use of energy, which though generates sufficient amount of income in terms of customs duty that supports country's per capita income; however, its effect largely the energy security issues across the globe. This study examines the impact of air transportation, railways transportation, and container port traffic on energy demand, customs duty, and economic growth in a panel of 40 heterogeneous countries, which comprises 16 low income & lower middle income (LI&LMI) countries and 24 upper middle & high income (UM&HI) countries for the period of 1990–2015. The study employed panel econometric techniques which account for cross-sectional dependence and heterogeneity. The results show that air-railways transportation has a positive and significant relationship with the energy demand (ED) in aggregated panel, whereas air-railways passengers carried positively influenced ED in LI&LMI countries, and railways transported goods (RT) significantly increases ED in UM&HI countries. Air freight (AF) and railways passengers carried (RPC) escalate customs duty (CUD) in aggregated panel, while RPC positively influences CUD in LI&LMI countries, and AF significantly increases CUD in UM&HI countries. Container port traffic (CPT) positively influenced per capita income (GDPPC) across countries. The causality estimates confirmed the bidirectional relationship, unidirectional, reverse causality, and no causal relationships between the studied variables with different transportation modes. The estimates of impulse response function (IRF) suggest that transportation (except RPC) and growth factors will positively influence ED in aggregated panel while differential impacts of transportation and growth factors will affect CUD over a next 30 years time period. The variance decomposition analysis (VDA) shows that GDPPC will largely influence by ED and least influenced by CUD, while CPT will greatly affect by CUD and least influenced by AF, over a next 30 years period. The overall results provoked the need of transportation energy infrastructure that desirable for long-term sustainable growth across countries.

Measuring the Channels of Monetary Policy Transmission: A Factor-Augmented Vector Autoregressive (Favar) Approach

Abstract There is more consensus on the effects of monetary policy than its transmission mechanism. Two channels of transmission mechanisms are the conventional interest rate channel and the credit channel. I investigate the channels of monetary policy transmission in the U.S. using the factor-augmented vector autoregressive (FAVAR) models developed by Bernanke, Boivin & Eliasz (2005). The newly developed FAVAR approach allows the researcher to include all relevant macroeconomic variables in the model and analyze them. Therefore, the FAVAR models span a larger information set and generate better estimates of impulse response functions than the commonly used vector autoregressive (VAR) models that utilize only 4–8 variables. I include 154 monthly U.S. time series variables for the period 1970–2014. The findings support the existence of the credit channel in the U.S. The conclusion remains the same when the non-borrowed reserve operating regime (October 1979–October 1982) is removed from the sample period.

Dynamic Factor Models, Cointegration, and Error Correction Mechanisms

In this paper we study Dynamic Factor Models when the factors Ft are I(1) and singular, i.e. rank(Ft) < dim(Ft). By combining the classic Granger Representation Theorem with recent results by Anderson and Deistler on singular stochastic vectors, we prove that, for generic values of the parameters, Ft has an Error Correction representation with two unusual features: (i) the autoregressive matrix polynomial is finite, (ii) the number of error-terms is equal to the number of transitory shocks plus the dierence between the dimension and the rank of Ft. This result is the basis for the correct specification of an autoregressive model for Ft. Estimation of impulse-response functions is also discussed. Results of an empirical analysis on a US quarterly database support the use of our model.

Prior Selection for Vector Autoregressions

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of-sample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors in order to shrink the richly parameterized unrestricted model toward a parsimonious naıve benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach, theoretically grounded and easy to implement, greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well in terms of both out-of-sample forecasting—as well as factor models—and accuracy in the estimation of impulse response functions.

Probabilidad clásica de sobreajuste con criterios de información: Estimaciones con series macroeconómicas chilenas

This paper provides, via Monte Carlo simulations, estimates of the classical probability of overfitting under an autoregressive environment (AR), using the information criteria (IC) of Akaike, Schwarz and Hannan-Quinn (AIC, BIC and HQ), calibrated with Chilean data of total inflation, core inflation, IMACEC, and monthly return of the nominal exchange rate Chilean peso- American dollar. This probability corresponds to the number of times when a candidate model has a strictly greater number of coefficients than the true model, divided by the total number of searches. The results indicate that the increased risk of overfitting is obtained with the AIC, followed by HQ and finally the BIC. The highest probability of overfitting is achieved with the AIC, reaching 32 and 30% with the exchange rate and IMACEC, respectively, followed by 25 and 22% for total and core inflation. Considering the three IC, it is more likely to obtain an overfitted model by just one coefficient. Also, it is more likely that the overfitting does not exceed 10 coefficients. These results are important as quantifying the extent to which these variables are subject to overfitting risk when represented by AR models. The potential problems carried by overfitting includes: (i) the spurious regression, (ii) distor tthe estimation of impulse response function, and (iii) affect the predictive accuracy of the variable of interest. The latter problem is analyzed in detail.

Structural VAR models for Malaysian monetary policy analysis during the pre- and post-1997 Asian crisis periods

This article conducts an in-depth investigation into building a Structural Vector Autoregression (SVAR) model and analysing the Malaysian monetary policy. Considerable attention is paid to: (i) the selection of foreign, policy and target variables; (ii) establish identifying restrictions and improve the estimates of impulse response functions; (iii) assess the importance of intermediate channels in transmitting monetary policy mechanism; and (iv) the way in which the 1997 Asian financial crisis affected the working of monetary policy. Malaysia is an interesting small open economy to study because, following this crisis, the government imposed capital and exchange rate control measures. The overall results suggest that the crisis and the subsequent major shift in the exchange rate regime have significantly affected the Malaysian ‘Black Box’. In the pre-crisis period, domestic variables appear to be more vulnerable to foreign monetary shocks. Further, the exchange rate played a significant role in transmitting the interest rate shocks, whereas credit and asset prices helped to propagate the money shock. In the post-crisis period however, asset prices play a more domineering role in intensifying the effects of both interest rate and money shocks on output, and the economy was insulated from foreign shocks.