Impulse Response Estimation Research Articles

Random Subspace Local Projections

Abstract We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over regressions estimated over different combinations of subsets of these controls. We document three key results: (i) Our approach can successfully recover the impulse response functions across Monte Carlo experiments representative of different macroeconomic settings and identification schemes. (ii) Our results suggest that random subspace methods are more accurate than other dimension reduction methods if the underlying large dataset has a factor structure similar to typical macroeconomic datasets such as FRED-MD. (iii) Our approach leads to differences in the estimated impulse response functions relative to benchmark methods when applied to two widely studied empirical applications.

The short-run displacement of EU cohesion funds in Italy: Has reprogramming a positive impact on regional growth?

The European cohesion policy aims at fostering long-run growth. This paper, for the first time, empirically evaluates the impacts of Structural Funds re-programming on regional growth, for the Italian case during 2007–20 period. The re-programming was due to the redirection of allocated amounts facing urgent needs related to the Great Recession, Covid-19 and natural disasters. We estimate impulse response functions through Jordà local projections. We find a robust negative effect of re-programming on regional growth starting three years after the planned shock and sectoral heterogeneity. Our empirical results discourage the Funds’ redirection towards a different use from original goals.

Oil jump tail risk as a driver of inflation dynamics

In this paper, we look at the role of various oil jump tail risk measures as drivers of both U.S. headline and core inflation. Those measures are first computed from high-frequency oil future prices and are then introduced into standard regression models in order to (i) assess in-sample determinants of inflation, (ii) assess overtime the evolution of inflation drivers, (iii) estimate impulse response functions and (iv) forecast inflation out-of-sample for various horizons. Empirical results suggest that oil jump tail risk measures contain useful information to describe inflation dynamics, generally leading to upward inflationary pressures. Even after controlling from standard variables involved in a Phillips curve, goodness-of-fit measures show evidence of a gain, in particular for headline inflation. Overall, we observe that oil jump tail risk measures are contributing more to inflation dynamics since the Covid-19 crisis.

EyeGesener: Eye Gesture Listener for Smart Glasses Interaction Using Acoustic Sensing

The smart glasses market has witnessed significant growth in recent years. The interaction of commercial smart glasses mostly relies on the hand, which is unsuitable for scenarios where both hands are occupied. In this paper, we propose EyeGesener, an eye gesture listener for smart glasses interaction using acoustic sensing. To mitigate the Midas touch problem, we meticulously design eye gestures for interaction as two intentional consecutive saccades in a specific direction without visual dwell. The proposed system is a glass-mounted acoustic sensing system with two pairs of commercial speakers and microphones to sense eye gestures. To capture the subtle movements of the eyelid and surrounding skin induced by eye gestures, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel impulse response (CIR) estimation schema that allows two speakers to transmit at the same time and in the same frequency band without collision. We implement eye gesture filtering and adversarial-based eye gesture recognition to identify eye gestures for interaction, filtering out daily eye movements. To address the differences in eye size and facial structure among different users, we employ adversarial training to achieve user-independent eye gesture recognition. We evaluate the performance of our system through experiments on data collected from 16 subjects. The experimental result shows that our system can recognize eight eye gestures with an average F1-score of 0.93, and the false alarm rate of our system is 0.03. We develop an interactive real-time audio-video player based on EyeGesener and then conduct a user study. The result demonstrates the high usability of the proposed system.

Macrofinancial linkages and income distribution in the South African economy

ABSTRACT This study examines the impact of financial factors and income distribution on aggregate demand and growth in South Africa, using data spanning 1980–2019. We applied the autoregressive-distributed-lag bounds-testing procedure to uncover the effects over the short-and-long-run horizons. Additionally, we estimated impulse response functions to reveal further dynamic interactions. Our findings indicate that the key financial factors affecting the South African real economy are credit growth, interest rates, house prices and exchange rates. House prices play an important role, via the collateral channel, in driving consumption demand. Meanwhile, credit growth stifles investment demand in the long run. Interest rates constrain demand and growth, while the strengthening of exchange rates improves investment for firms. However, exchange rates impact negatively on GDP, presumably, through the trade-channel. We also found that income inequality suppresses consumption and investment demand, as well as GDP. This suppression appeared to be relatively pronounced in the short run for investment and growth, while for consumption, the suppressive impact was persistent. Therefore, we recommend that any policy framework aiming to galvanise growth should not overlook the problem of inequality. Otherwise, any headway made on growth will be back-pedalled by the demand-reducing impact of high-income inequality.

Towards Smartphone-based 3D Hand Pose Reconstruction Using Acoustic Signals

Accurately reconstructing 3D hand poses is a pivotal element for numerous Human-Computer Interaction applications. In this work, we propose SonicHand, the first Smartphone-based 3D Hand Pose Reconstruction system using purely inaudible acoustic signals. SonicHand incorporates signal processing techniques and a deep learning framework to address a series of challenges. Firstly, it encodes the topological information of the hand skeleton as prior knowledge and utilizes a deep learning model to realistically and smoothly reconstruct the hand poses. Secondly, the system employs adversarial training to enhance the generalization ability of our system to be deployed in a new environment or for a new user. Thirdly, we adopt a hand tracking method based on channel impulse response (CIR) estimation. It enables our system to handle the scenario where the hand performs gestures while moving arbitrarily as a whole. We conduct extensive experiments on a smartphone testbed to demonstrate the effectiveness and robustness of our system from various dimensions. The experiments involve 10 subjects performing up to 12 different hand gestures in 3 distinctive environments. When the phone is held in one of the user’s hand, the proposed system can track joints with an average error of 18.64 mm.

On the robustness of the relationship between tax progressivity, growth, and inequality in the US

We investigate the impact of a shock in tax progressivity on GDP growth and income inequality in the US between 1969 and 2016. Employing various robustness tests, including different imputation methods for the progressivity index, as well as utilizing Vector Autoregressive and Linear Projection models for estimating impulse response functions, we find that both GDP growth and inequality decline in response to more tax progressivity. Nevertheless, only the effect on inequality remains statistically significant across our battery of robustness exercises, while the negative effect on GDP growth is robustly significant only prior to the Great Recession in 2008.

Channel Estimation for Underwater Acoustic Communications in Impulsive Noise Environments: A Sparse, Robust, and Efficient Alternating Direction Method of Multipliers-Based Approach

Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an l1−l1 optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method.

Bayesian Flexible Local Projections

Abstract We develop a methodology to estimate impulse response functions via Bayesian techniques with the goal of providing a bridge between a linear vector autoregressive specification and a high-order polynomial local projection, namely flexible local projection. We label this methodology Bayesian Flexible Local Projection (BFLP). We assess the properties of BFLP in a Monte Carlo framework considering both linear and non-linear models as data generating processes. We also empirically illustrate how BFLP can be used with standard identification strategies. In particular, we show how to use external instruments to identify the effects of the monetary policy shock in the United States. Furthermore, exploiting the time-varying nature of the impulse response functions based on BFLP, we assess the zero lower bound irrelevance hypothesis and find no strong evidence that monetary policy was less effective in influencing output and inflation during the recent ZLB period.

Averaging impulse responses using prediction pools

Macroeconomists construct impulse responses using many competing time series models and different statistical paradigms (Bayesian or frequentist). We adapt optimal linear prediction pools to efficiently combine impulse response estimators for the effects of the same economic shock from this vast class of possible models. We thus alleviate the need to choose one specific model, obtaining weights that are typically positive for more than one model. Our Monte Carlo simulations and empirical applications illustrate how the weights leverage the strengths of each model by (i) trading off properties of each model depending on variable, horizon, and application and (ii) accounting for the full predictive distribution rather than being restricted to specific moments.11MATLAB replication code is available on Matthes’ GitHub repository.

US fiscal policy shocks: Proxy‐SVAR overidentification via GMM

SummaryUsing external instruments, one can recover the effects of individual shocks without fully identifying a vector autoregression (VAR). We show that fully or almost fully instrumenting a VAR—that is, using an instrument for each shock—allows one to overidentify the model by incorporating the condition that the structural shocks are uncorrelated, via the generalized method of moments (GMM). We apply our approach to a fiscal VAR for the United States over 1948–2019, where the overidentifying restrictions are not rejected. The overidentified structural vector autoregression (SVAR) yields (a) greater precision in estimating impulse response functions and multipliers and (b) measures of the effects of nonfiscal shocks even when there is no instrument for them.

Local projections in unstable environments

This paper develops a local projection estimator for estimating impulse responses in the presence of time variation. Importantly, we allow local instabilities in both slope coefficients and variances. Monte Carlo simulations illustrate that the method performs well in practice. Using our proposed estimator, we shed new light on the effects of fiscal policy shocks and the size of government spending multipliers. Our analysis uncovers the existence of instabilities that were unaccounted for in previous studies, and links time variation in the multipliers to the size of government debt.

Consistency of averaged impulse response estimators in vector autoregressive models

We show root‐T consistency of the smoothed AIC and smoothed BIC model averaging estimators (sAIC, sBIC) of impulse response coefficients in stationary vector autoregressive models of finite lag order. We also show that there is not one unique way to define the sAIC and sBIC estimators, but that instead there is a whole class of each of these defined by a weight scaling factor that allows the averaging estimator to become more similar to either its model selection counterpart or the equal weights averaging estimator. We also show asymptotic validity of a bootstrap method for estimating the averaging estimators' distributions. Simulations illustrate the benefits of using sAIC instead of AIC estimators.

When cannot regularization improve the least squares estimate in the kernel-based regularized system identification

In the last decade, kernel-based regularization methods (KRMs) have been widely used for stable impulse response estimation in system identification. Its favorable performance over classic maximum likelihood/prediction error methods (ML/PEM) has been verified by extensive simulations. Recently, we noticed a surprising observation: for some data sets and kernels, no matter how the hyper-parameters are tuned, the regularized least square estimate cannot have higher model fit than the least square (LS) estimate, which implies that for such cases, the regularization cannot improve the LS estimate. Therefore, this paper focuses on how to understand this observation. To this purpose, we first introduce the squared error (SE) criterion, and the corresponding oracle hyper-parameter estimator in the sense of minimizing the SE criterion. Then we find the necessary and sufficient conditions under which the regularization cannot improve the LS estimate, and we show that the probability that this happens is greater than zero. The theoretical findings are demonstrated through numerical simulations, and simultaneously the anomalous simulation outcome wherein the probability is nearly zero is elucidated, and due to the ill-conditioned nature of either the kernel matrix, the Gram matrix, or both.

Impact of Supply Side Shocks on Kenyan Exports to COMESA

The study sought to determine the impact of supply-side shocks on Kenyan exports to COMESA region. This was motivated by the increasing number of shocks that have hit the country, the region and the global economy in the recent past. Using secondary data covering the period from 1990 to 2021, the study applied the Vector Auto Regression model and estimated impulse response functions to show the effect of shocks on each of Kenya's exports to the region. The study established that Kenya's exports to COMESA as a region are impacted by oil price shocks, variations in investment level in Kenya and exchange rate. Erratic weather patterns characterized by droughts do not affect the country's exports to the region. Therefore, the paper recommends that for Kenya to boost its exports to the region and subsequently boost Intra-COMESA trade, the government of Kenya should promote investment and ensure that the country gets enough crude oil supplies even when global prices are rising as this will boost the country's exports to the region.

Regularized Finite Impulse Response Models versus Laguerre Models: A Comparison

While in recent years, the estimation of finite impulse response (FIR) models has been improved by introducing new regularization schemes, also other orthonormal basis function (OBF) models are now becoming more prominent again. Although Laguerre models show very similar properties to the regularized FIR models, they are only rarely used for system identification. Therefore, in this paper, regularized FIR models and Laguerre models will be compared. This work focuses on the model structure and its similarities and differences, as well as the hyperparameter optimization utilizing the generalized cross-validation (GCV) error. Finally, the two model types are investigated using three different processes and the model performance is evaluated. Both model types show significant improvements compared to standard (unregularized) FIR models.

Efficient Implementation of Kernel Regularization Based on ADMM and Its Application to Room Impulse Response Estimation

Efficient Implementation of Kernel Regularization Based on ADMM and Its Application to Room Impulse Response Estimation

Peculiarities of a Rarely Used Method of Measuring the Speech Transmission Index in Premises

Evaluation of speech transmission index (STI) in premises allows for determining the speech intelligibility, and therefore the suitability of premises for speech communication. STI measurements using the speech transmission index for telecommunication systems (STITEL) method are rarely performed in rooms, possibly due to insufficient information on the accuracy of this method. In this paper, computer simulations were used to estimate the STI estimation errors by the STITEL method under conditions of noise and reverberation. The pink noise model and the room impulse response estimate of a real room with a reverberation time T60=0.8 s were used for the research. The duration of the test signals varied between 4, 8, 16, 32, and 64 seconds, and the signal-to-noise ratio varied from minus 28 dB to plus 28 dB. The dependences of the bias, standard deviation, and total error of the STI estimate on the duration of the test signal and the signal-to-noise ratio are obtained. It is shown that the total error of the STI estimation is close to 0.03 when the duration of the test signal is 8 s. Under conditions of noise action, this error decreases with a further increase in the duration of the test signal. Under conditions of joint action of noise and reverberation, such a decrease was not observed, while the total error is within 0.03-0.04.

Tourism Demand in the Face of Geopolitical Risk: Insights From a Cross-Country Analysis

This paper develops a novel Bayesian heterogeneous panel vector autoregressive model (B-HP-VAR) that quantifies the impact of geopolitical risk shocks on the tourism industry of 14 emerging market and developing economies (EMDE). We find that increasing geopolitical tensions have a persistent negative effect on tourism demand in most of these countries, as shown by our impulse response estimates. Furthermore, evidence from forecast error variance decomposition reveals that geopolitical risk shocks in many EMDE economies constitute the main driver of tourism demand. Analysis from historical decompositions demonstrates that geopolitical tensions have been particularly influential in driving tourism demand in Ukraine, Russia, Turkey, China, Indonesia, Thailand, Colombia, and Mexico. Our main findings are robust to several perturbations to the benchmark specification. Our results have several important implications for policymakers in their efforts to strengthen the ability of the tourism industry to absorb shocks from geopolitical tensions.

Monetary policy models: lessons from the Eurozone crisis

This paper theoretically and empirically investigates the puzzling decade-long concurrence of expansionary monetary and fiscal policies, decreasing credit flows, fall in price levels, and sluggish real activity observed in the Euro area from the outset of the 2007–2008 financial crisis. To this end, we propose a monetary general equilibrium model that clarifies the transmission mechanisms, debt–deflation channels, and the paramount role of financial leverage decisions underlying these peculiarities. On this basis, a vector error correction model is specified which confirms the theoretical predictions and provides insights into the elements specific to the long-term relations. In addition, the estimated impulse response functions document the associated short-term dynamics outlining the debt–deflation mechanism.