A Guide to Autoregressive Distributed Lag Models for Impulse Response Estimations*

Baek, ChaeWon; Lee, Byoungchan

doi:10.1111/obes.12494

Cited by 4 publications

(4 citation statements)

References 76 publications

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“…where Debt i,t is an indicator of whether firm i has debt in time t and Dollar i,t is the share firm i's debt in US dollar at time t. The specification also includes firm, month and year fixed-effects, and we cluster the standard errors at the firm level. In terms of the specification, Baek and Lee (2021) suggest that the amount of lags should be as long as the shock lasts.…”

Section: Exposure To External Debtmentioning

confidence: 99%

The Transmission of International Monetary Policy Shocks on Firms' Expectations

Frache

Lluberas

Pedemonte

et al. 2023

Working Paper (Federal Reserve Bank of Cleveland)

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Motivated by the dominant role of the US dollar, we explore how monetary policy (MP) shocks in the US can affect a small open economy through the expectation channel. We combine data from a panel survey of firms' expectations in Uruguay with granular information about firms' debt position and total imports on a monthly basis. We show that a contractionary MP shock in the US reduces firms' inflation and cost expectations in Uruguay. This result contrasts with the inflationary effect of this shock on the Uruguayan economy, suggesting uncertainty about the policy regime. We discuss the issues and challenges of this expectation channel.

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Section: Exposure To External Debtmentioning

confidence: 99%

The Transmission of International Monetary Policy Shocks on Firms' Expectations

Frache

Lluberas

Pedemonte

et al. 2023

Working Paper (Federal Reserve Bank of Cleveland)

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show abstract

“…More generally, Ω h ≡ h =0 β represents the impulse response function (IRF) of y with respect to a weather shock, dw, at time 0 (Baek & Lee (2021)).…”

Section: Panel Distributed Lag Modelmentioning

confidence: 99%

“…The properties of the DL model and its associated IRFs have been studied recently by Baek & Lee (2021) and Plagborg-Møller & Wolf (2021), who demonstrate that the DL model yields consistent estimates of the true IRF provided that the model includes a sufficient number of lags relative to the horizon of interest. As a result, our long lag structure should yield consistent estimates.…”

Section: Econometric Concernsmentioning

confidence: 99%

Climate Change and the Geography of the U.S. Economy

Leduc¹,

Wilson²

2023

ERWP

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This paper examines how the spatial distribution of people and jobs in the United States has been and will be impacted by climate change. Using novel county-level weather data from 1951 to 2020, we estimate the longer-run effects of weather on local population, employment, wages, and house prices using a panel distributed lag model. The historical results point to long-lasting negative effects of extreme temperatures on each of these outcomes. We highlight that a long lag structure is necessary to appropriately capture the longer-run effects of climate change, as short-run effects are often small and imprecisely estimated. Using county-level weather projections based on alternative greenhouse gas emissions scenarios, we use the estimated models to project the spatial distribution of these local economic outcomes out to 2050. The results point to substantial reallocations of people and jobs across the country over the next three decades, with mobility increasing by between 33 and 100 percent depending on the scenario. Population and employment are projected to shift away from the Sunbelt and toward the North and Mountain West. We document that this would, in fact, be a continuation of a historical pattern: Over the past four decades the relationship between population growth and hot climates across the United States has turned from strongly positive to slightly negative. We present a spatial equilibrium model to interpret the results, highlighting the impacts of climate change on amenities and productivity, and find significant roles for both channels in accounting for our empirical findings.

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“…Typical methods such as Bayesian model averaging are unavailable when one of the estimators considered is based on LPs as LPs are not 'generative models', that is, a set of LPs for different horizons do not form a consistent data-generating process. Besides the aforementioned LPs and VARs, dynamic equilibrium models (Smets and Wouters, 2007), dynamic factor models (Stock and Watson, 2016), or single equation methods (Baek and Lee, 2022) can be used in our framework. In addition, our method provides horizon-and variable-specific averages, thus exploiting each method's strength as much as possible.…”

Section: Introductionmentioning

confidence: 99%

Averaging Impulse Responses Using Prediction Pools

Ho¹,

Lubik²,

Matthes³

2023

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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. Three Monte Carlo simulations and two monetary shock 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.

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A Guide to Autoregressive Distributed Lag Models for Impulse Response Estimations*

Cited by 4 publications

References 76 publications

The Transmission of International Monetary Policy Shocks on Firms' Expectations

The Transmission of International Monetary Policy Shocks on Firms' Expectations

Climate Change and the Geography of the U.S. Economy

Averaging Impulse Responses Using Prediction Pools

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