Understanding the effects of technology shocks

The research led by Gali (AER 1999) and Basu et al. (AER 2006) raises two important questions regarding the validity of the RBC theory: (i) How important are technology shocks in explaining the business cycle? (ii) Do impulse responses to technology shocks found in the data reject the assumption of flexible prices? Using an RBC model, this paper argues that the conditional impulse responses of the U.S. economy to technology shocks are not grounds to reject the notion that technology shocks are the main driving force of the business cycle and the assumption of flexible prices, in contrast with the conclusions reached by the literature.     

Avoiding pitfalls in using structural VARs to estimate economic models

Structural Vector Autoregressions with a differenced specification of hours (DSVAR) suggest that productivity shocks identified using long-run restrictions lead to a persistent and significant decline in hours worked. Economists have interpreted this evidence as showing that standard business cycle models in which a positive technology shock leads to a rise in hours are inconsistent with the data. In this paper we argue that such a conclusion is unwarranted because model's data and actual data are not treated symmetrically. To illustrate this problem, we estimate and test a flexible-price DSGE model with non-stationary hours using Indirect Inference on impulse responses of hours and output after technology and non-technology shocks. We find that, once augmented with a moderate amount of real frictions, the model can mimic well impulse responses obtained from a DSVAR on actual data. Using this model as a data generating process, we show that our estimation method is less subject to bias than a method that would directly compare theoretical responses with responses from the DSVAR.     

Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs

We investigate the ability of small- and medium-scale Bayesian VARs (BVARs) to produce accurate macroeconomic (output and inflation) and credit (loans and lending rate) out-of-sample forecasts during the latest Greek crisis. We implement recently proposed Bayesian shrinkage techniques based on Bayesian hierarchical modeling, and we evaluate the information content of forty-two (42) monthly macroeconomic and financial variables in terms of point and density forecasting. Alternative competing models employed in the study include Bayesian autoregressions (BARs) and time-varying parameter VARs with stochastic volatility, among others. The empirical results reveal that, overall, medium-scale BVARs enriched with economy-wide variables can considerably and consistently improve short-term inflation forecasts. The information content of financial variables, on the other hand, proves to be beneficial for the lending rate density forecasts across forecasting horizons. Both of the above-mentioned results are robust to alternative specification choices, while for the rest of the variables smaller-scale BVARs, or even univariate BARs, produce superior forecasts. Finally, we find that the popular, data-driven, shrinkage methods produce, on average, inferior forecasts compared to the theoretically grounded method considered here.     

Maximum-likelihood estimation of misspecified models

Misspecified models occur frequently in econometric practice. It is therefore important to study the sampling distribution of maximum-likelihood estimators of parameters of misspecified models. This note exhibits the asymptotic covariance matrix of the ML estimator of a misspecified model. It points out that the expression for this matrix given by White is incorrect except for the very special case, rarely occuring in econometrics, that each observation is independent and identically distributed. An illustration using the standard linear regression model is provided.     

Employment responses to aggregate and sectoral technology shocks

This paper investigates the responses of sectoral employment in US manufacturing to a technology shock by its type: aggregate or sectoral. In order to distinguish between aggregate and sectoral shocks, we construct independent VAR models for identifying each shock separately: a factor-augmented vector autoregression (FAVAR) for aggregate shocks and a sectoral SVAR for sectoral shocks. Our aggregate model in particular extends the conventional small-scale VAR to the FAVAR framework of Bernanke et al. (2005) so that it can address the potential bias from omitted variables. The main findings are as follows: most industries exhibit negative employment responses to an aggregate technology shock while exhibiting positive responses to a sectoral technology shock. By comparing our FAVAR framework with Chang and Hong’s (2006) small-scale VAR, we show that applying the FAVAR results in significant differences in the estimated responses to an aggregate technology shock. Real rigidities (such as slow diffusion of new technology or frictional labor reallocation), rather than nominal rigidities (such as sticky prices), are crucial in accounting for the cross-industry difference in employment responses. In particular, the slow diffusion of new technology is closely related to the sluggish response of sectoral employment.     

Price stickiness and the contractionary effect of technology shocks

We derive a measure of technological change from a dynamic cost minimization model that controls for imperfect competition, increasing returns and unobserved factor utilization. We estimate this measure using highly detailed panel data of a representative sample of Italian manufacturing firms for the period 1984-1997. Our key finding is that technological improvements result in a contraction of labor input on impact. In principle, this result can be reconciled with the transmission mechanism of flexible-price models by resorting to reorganization and reallocation effects. On the other hand, however, it is consistent with the predictions of a sticky-price model. Using survey information on the frequency of price revisions, we corroborate the latter interpretation, by showing that the contractionary effect of technology shocks is much stronger for firms with stickier prices.     

Identifying uncertainty shocks using world diffusion index

ABSTRACT

In this study, a world diffusion index is developed to measure how uncertainty shocks have diffused among 179 economies and caused contractions in the world growth cycles. This index is simply defined as the percentage share of the number of expanding countries. It identifies four uncertainty shocks: the oil crisis of 1973; the bursting of the information technology bubble in 2000; the credit crunch of 2007; and the European debt crisis of 2010. To overcome the problem of data unavailability in emerging market economies, the annual GDP values of 179 economies are transformed through temporal disaggregation, and the dating of quarterly growth cycles is implemented as per the OECD method. The empirical findings indicate that each of the uncertainty shocks caused severe contractions in the advanced economies but that the emerging market economies experienced such contractions only during the credit crunch of 2007. Policy implications are also discussed.     

Capacity utilization and technology shocks in the US manufacturing sector

The ability of real business cycle models to generate reasonable aggregate fluctuations depends on the time series properties of technology shocks measured by the change of total factor productivity. Three specifications of a non‐parametric productivity analysis which correct to different degrees for variations of capacity utilization are compared in this article using data for three‐ and four‐digit US manufacturing industries during the years 1958–1996. The results show that correcting for utilization generally leads to substantially smaller technology shocks that are less strongly correlated with growth of output and hours. Moreover, the probability of technological regress is considerably lower after the correction.     

The long-term consequences of external debt: Revisiting the evidence and inspecting the mechanism using panel VARs

We estimate a panel vector autoregression model to examine the relationship between external debt and economic growth. We use a large dataset based on 123 countries, classified according to income levels over the period 1990–2015. While total external debt appears to have a negative effect on growth rate overall, it is positively associated with income growth in the lower- and upper-middle income countries. Further disaggregating external debt into its components reveals that public external debt negatively affects economic growth across all income categories of countries, whereas the impact of private external debt is not statistically significant. We do not detect a common threshold level in the relationship between public debt and economic growth across countries. Savings and investment are the primary channels through which external debt impacts economic growth. These results are robust to various model specifications, additional controls, and identifying restrictions.     

Identifiability of the misspecified split hazard models

Unlike standard models, a split population hazard model allows the exit probability to be less than one. Although conceptually attractive, split models are prone to identification problems. In the reduced form estimation of the hazard function, the influence of split may not be distinguishable from that of neglected heterogeneity. For illustration, I use Monte Carlo simulations to highlight the problem of interpreting the structural parameters of the split Weibull and the Weibull-gamma models.     

On the welfare costs of misspecified monetary policy objectives

This paper quantifies the effects on welfare of misspecified monetary policy objectives in a stylized DSGE model. We show that using inappropriate objectives generates relatively large welfare costs. When expressed in terms of ‘consumption equivalent’ units, these costs correspond to permanent decreases in steady-state consumption of up to two percent. The latter are generated by both the inappropriate choice of weights and the omission of variables. In particular, it is costly to assume an interest-rate smoothing incentive for central bankers when it is not socially optimal to do so. Finally, a parameter uncertainty decomposition indicates that uncertainty about the properties of markup shocks gives rise to the largest welfare costs.     

Risk premium,macroeconomic shocks,and information technology: an empirical analysis

This study empirically identifies the impact of various macroeconomic factors on the default risk premium. Using monthly data for the period 1970–2010 for the US, our estimations indicate that the monetary policy aggregates, risk-free interest rate, term structure of interest rates, inflation, and the state of the business cycle influence the risk premium. The results also provide some evidence in support of the hypothesis that the development of information technology has had a decreasing impact on the risk premium. As expected, various financial crises have had substantial and long-lasting effects on the premium. The results suggest that the direct impact of the subprime crisis and Lehman’s collapse on the risk premium was as large as two and a half percentage-points for a sustainable period. Foreign financial crises, in turn, have lowered the risk premium in the US market, suggesting a flight-to-safety phenomenon.     

Are shocks to the terms of trade shocks to productivity?

International trade is frequently thought of as a production technology in which the inputs are exports and the outputs are imports. Exports are transformed into imports at the rate of the price of exports relative to the price of imports: the reciprocal of the terms of trade. Cast this way, a change in the terms of trade acts as a productivity shock. Or does it? In this paper, we show that this line of reasoning cannot work in standard models. Starting with a simple model and then generalizing, we show that changes in the terms of trade have no first-order effect on productivity when output is measured as chain-weighted real GDP. The terms of trade do affect real income and consumption in a country, and we show how measures of real income change with the terms of trade at business cycle frequencies and during financial crises.     

Demand shocks,supply shocks and the cyclicality of real wages

The behaviour of real wages over the business cycle has received increasiing attention in recent years. The cyclicality of real wages constitues an important aspect of recent models of the business cycle. However,empirical studies undertaken to determine whether real wages are procyclical or countercyclical have reported conflicting findings. In thiis paper we use vector-autoregressions to analyse the cyclicality of real wages. We find that the source of the disturbance plays a decisive role in the cyclical behaviour of real wages. In particular, we demonstrate that a supply shock generates procyclical real wages,whereas a demand shock yields countercyclicality.     

Time varying structural VARs with sign restrictions: The case of Taiwan

I apply a Bayesian approach to a time-varying structural vector autoregression model with stochastic volatility (TVP-SVAR-SV) to study the time-varying nature of the Taiwanese economy. In particular, the structural parameters are identified via the sign information in a three-variable VAR system. The estimated results show that TVP-SVAR-SV model has the best fit to the data, compared to the time-varying parameters VAR model with constant volatility and a classical VAR model with constant parameters and volatilities. Moreover, I find the time-varying contemporaneous relationship between the output growth and inflation rates, particularly significant before the year 2000. Lastly, the impulse responses and the volatilities of all the variables are found to be time-varying.     

Identifying terms of trade shocks in a developing country using a sign restrictions approach

Using annual data for Botswana from 1960 to 2012, we examine the responses of macroeconomic variables to four generalized positive terms of trade shocks – global demand, globalizing, sector-specific and global supply. A sign-restricted structural vector autoregression model with a penalty function is estimated to identify the four possible shocks. While positive global demand and globalization shocks are both expansionary, they have opposite effects on inflation. A positive commodity market specific shock dampens real GDP growth and is inflationary, suggesting a possible Dutch disease response. A negative global supply shock suppresses both output growth and inflation. All but the last shock leads to a significant declining interest rate. Monetary policy contraction is recommended for the first shock and expansion for the others.