Investment-specific Technology Research Articles

Investment-specific technology shocks and business cycle: evidence from a sign restriction approach

Investment-specific technology shocks and business cycle: evidence from a sign restriction approach

Estimation of industry-level basic digital capital services in China: A variable depreciation rate estimation method based on DSGE

Accurately measuring the input of digital capital is of great significance for understanding the development pattern of China's digital economy, nurturing new quality productivity, and achieving high-quality development. However, existing representative studies often assume a constant depreciation rate for capital goods, which does not align with reality. In this paper, we address this limitation by introducing dynamic stochastic general equilibrium (DSGE) model that estimates the variable depreciation rate for each capital good. By setting the depreciation rate as a function of capital maintenance expenditures and utilization rates, we provide a more realistic approach. The empirical results obtained through Monte Carlo simulation demonstrate that the trend of depreciation rates for all types of capital goods aligns with China's economic development. Additionally, we find that investment-specific technology shock plays a significant role in affecting changes in the depreciation rates of capital goods. This shock leads to an increase in capital utilization rates and a decrease in capital maintenance expenditures, resulting in higher depreciation rates. Notably, the depreciation rate of basic digital capital is more sensitive to exogenous shock compared to non-digital capital. Furthermore, the paper estimates the basic digital capital service of 19 industries in China from 1981 to 2020. The results indicate a steady increase in the services provided by basic digital capital, with particularly rapid growth observed in industries such as information transmission, software and information technology services, and the financial sector. When comparing our estimation results with those of representative literature, we find that the national-level productive capital stock and the basic digital capital services estimated using our methodology closely align with existing literature. This validation confirms the effectiveness of our methodology and the reliability of the estimated industry-level basic digital capital services.

Output measurement and technological shocks in business cycles

ABSTRACT Is the importance of technology shocks in accounting for the fluctuations in output and hours sensitive to the measure of output? Using a vector autoregression, we empirically investigate the contribution of technological shocks to economic fluctuations when output is defined in consumption units, as commonly used in many macroeconomic models, and when output is tabulated according to the Divisia index in the U.S. National Income and Product Accounts (NIPAs). Based on a standard neoclassical growth framework that allows for a mapping of theory into any desired measure of output, we establish that the same restrictions may be used to identify technology shocks in a vector autoregression model regardless of the measure of output. However, our estimation reveals that while the combination of both investment-specific technology shocks and neutral technology shocks accounts for a large portion of the business cycle variability of hours and output, the choice of the measure of output via the use of the associated deflator greatly affects the amplification of the shocks in the responses and the variability of the responses in output and hours.

Are all economic fluctuations bad for consumers?

Are business cycles always costly? This paper sheds new light on this question in the context of a two-sector neoclassical business cycle model by focusing on the roles of the origin of shocks and the degree of real frictions that restrict factor reallocation both inter-temporally (investment adjustment cost) and intra-temporally (inter-sectoral factor immobilities). We find that under the benchmark parameterization, investment-specific technology shocks are welfare-improving while consumption-specific technology shocks are welfare-detrimental, regardless of the degree of real frictions. While aggregate TFP shocks can be both depending on the degree of real frictions, welfare-improving business cycles are not supported by empirical evidence.

News shocks to investment-specific technology in business cycles

Recent research in DSGE and VAR models has indicated that the most important drivers of business cycles are IST shocks, not traditional TFP and other shocks. Research in VAR models has also empirically identified IST news shocks as a significant driving force behind the U.S. business cycle. However, recent New-Keynesian DSGE models have found that IST news shocks do not produce comovement of aggregate variables, with the share of the forecast error variance explained by IST news shocks being very small in a flexible-price model and essentially zero in a sticky-price model. This paper uses the Bayesian methods to estimate the parameters and shock processes to study the business cycle effects of IST news shocks in a two-sector sticky-price DSGE model with consumer durables and collateral constraints. Our variance decompositions indicate that IST news shocks are a more relevant source of uncertainty than IST non-news and other shocks.

The Decline of the Labor Share: New Empirical Evidence

We use time series techniques to estimate the importance of four main explanations for the decline of the US labor income share: rising firm markups, falling bargaining power of workers, higher investment-specific technology growth, and more automated production processes. Identification is achieved with restrictions derived from a stylized model of structural change. Our results point to automation as the main driver of the labor share, although rising markups have played an important role in the last 20 years. We also find evidence of capital-labor complementarity, suggesting that capital deepening may have raised the labor share. (JEL D21, D43, E25, J51, L23, O33, O41)

An unobserved components model of total factor productivity and the relative price of investment

AbstractThis paper applies the common stochastic trends representation approach to the time series of total factor productivity (TFP) and the relative price of investment (RPI) to investigate the modeling of neutral technology (NT) and investment-specific technology (IST) and its econometric ramifications on the analysis of general equilibrium model. The permanent and transitory movements in both series are estimated efficiently via Markov chain Monte Carlo methods using band matrix algorithms. The results indicate that TFP and the RPI are, each, well represented by a differenced first-order autoregressive process. In addition, their time series share a common trend component that we interpret as reflecting changes in general purpose technology. These results are consistent with studies that suggest that (1) the traditional view of assuming that NT and IST follow independent processes is not supported by the features of the time series and (2) improper specification of secular trends may distort estimation and inference. Notably, the findings provide some guidance to minimize the effect of idiosyncratic and common trend misspecifications on the analysis of impulse dynamics and propagation mechanisms in macroeconomic models.

LEARNING ABOUT REGIME CHANGE

AbstractTotal factor productivity (TFP) and investment specific technology (IST) growth both exhibit regime‐switching behavior, but the regime at any given time is difficult to infer. We build a rational expectations real business cycle model where the underlying TFP and IST regimes are unobserved. We develop a general perturbation solution algorithm for a wide class of models with unobserved regime‐switching. Using our method, we show learning about regime‐switching fits the data, affect the responses to regime shifts and intraregime shocks, increase asymmetries in the responses, generate forecast error bias even with rational agents, and raise the welfare cost of fluctuations.

Business cycle dynamics when neutral and investment-specific technology shocks are imperfectly observable

We develop a general equilibrium business cycle model with imperfectly observed neutral and investment-specific technology shocks, which agents learn from noisy signals. Estimated using US data, the model implies that neutral technology shocks generate more volatile responses than in an environment with perfect information, whereas investment-specific shocks have more persistent impact. Noise accounts for substantial variation in investment, and alters agents’ behavior persistently through its influence on beliefs — even when the underlying fundamentals are unchanged and the noise itself is not persistent. Further implications for business cycle analysis are explored.

A dissection of Indian growth using a DSGE filter

In order to build a strong and sustainable recovery post the COVID-19 pandemic, we need to draw important observations from the growth experience of the past. In this context, this paper uses a dynamic stochastic general equilibrium (DSGE) model that takes into account persistent growth rate shocks to decompose the Indian GDP into potential output and output gap. Apart from analysing the trajectory of potential output-output gap, it also examines their underlying drivers. The results suggest that a combined deceleration in neutral and investment-specific technology growth post 2016, brought down the potential growth to around 6 per cent in 2020Q1. The output gap also witnessed a persistent decline since 2018Q1, primarily due to weak demand and a rise in investment adjustment costs reflecting heightened stress in the investment and financial sectors. A forecasting exercise is also undertaken which shows that the estimates of output gap from the model possess competing inflation forecasting ability compared to HP filtered output gap.

Are neutral and investment-specific technology shocks correlated?

The joint behavior of Total Factor Productivity (TFP) and the Relative Price of Investment (RPI) in the data lead several authors to conclude that neutral technology shocks are positively correlated with investment-specific technology shocks, challenging the specification of standard macroeconomic models. This paper rejects the correlated-shocks hypothesis using both parametric and non-parametric methods and controlling for structural breaks. The data suggests moderately negative long-run covariation between the RPI and TFP constructed from chain-linked output, but the RPI is orthogonal to TFP in consumption units. These results are consistent with a simple two-sector model in which neutral technology shocks and investment-specific technology shocks are uncorrelated, while models with correlated shocks cannot account for the second result. I conclude that it is not necessary to adapt macro models to allow for correlated technology processes.

Technology shocks and sectoral labour market spill-overs

In a multi-sector model estimated on U.S. data, we apply standard long-run restrictions to investigate the labour market consequences of investment-specific and neutral technology shocks. We set up a global VAR model where aggregate technology shocks feed onto sector-specific dynamics and then propagate back through the network structure of the economy. At the aggregate level, our results are consistent with the wealth of existing empirical studies where investment-specific technology shocks trigger favourable employment responses, in contrast to neutral technology shocks. At a disaggregate level, however, we uncover the significant contribution of sectoral spill-overs in response to technology shocks, particularly in driving the adjustment towards long-term equilibrium.

The Dynamic Effect of Micro-Structural Shocks on Private Investment Behavior

This paper estimates the dynamic effect of three micro-structural shocks, namely, investment-specific technology, markup and technology shocks to key components of the Iranian economy: private investment behaviour. The identification strategy positions the structural shocks to private investment behaviour in the Dynamics Stochastic General Equilibrium (DSGE) framework with a three-accuracy test estimation: The Brooks and Gelman test, the Metropolis-Hastings jumping distribution acceptance ratio, and the distribution of the deep posteriors parameters are asymptotically normal. The quarter economic data of Iran economy from July 1988 until March 2015 is applied. The findings illustrate that the structural shocks about technology have a similar impact and cause a growth in private investment, and the only difference is, in investment-specific technology shock due to a temporary decrease in the capital expenses installation (Tobin's Q), an investment boom is driven. In contrast, the price markup shock due to inflation causes a decline in all investment indices.

The Impact of Investment-Specific Technology Shocks on Macroeconomics Variables

The Impact of Investment-Specific Technology Shocks on Macroeconomics Variables

An Unobserved Components Model of Total Factor Productivity and the Relative Price of Investment

This paper applies the common stochastic trends representation approach to the time series of total factor productivity and the relative price of investment to investigate the relationship between neutral technology and investment-specific technology. The permanent and transitory movements in both series are estimated efficiently via MCMC methods using band matrix algorithms. The results indicate that total factor productivity and the relative price of investment are, each, well-represented by an integrated process of order one. In addition, their time series share a common trend component that we interpret as reflecting changes in General Purpose Technology. These results suggest that (1) the traditional view of assuming that neutral technology and investment-specific technology follow independent processes is not supported by the features of the time series and (2) advances in information and communication technologies are general purpose technological progress that drive the trend in aggregate TFP in the United States.

Optimal Taxation and Investment‑ Specific Technological Change

In this paper, we look at the relationship between Investment Specific Technological Change (ISTC) and optimal level of labor income progressivity. We develop an incomplete markets overlapping generations model that matches relevant features of the US economy and find that the observed drop in the relative price of investment since the 1980’s leads optimal progressivity to increase. This result hinges on ISTC increasing the wage premium through an increase in the variance of the permanent component of labor income. This result is supported by recent findings in the literature that highlight the increasing role of the permanent component of labor income in the observed increase in income inequality.

From Macroeconomic Shocks to Credit Spreads

We identify the response of corporate bond credit spreads to three exogenous macroeconomic shocks: oil supply, investment-specific technology, and government spending. The response is large, significant, and close to a mirror image of the response of macroeconomic activity. The counter-cyclicality of credit spreads is mostly driven by time-varying credit risk premia, which translates into significant predictability in corporate bond returns. Standard proxies for equity risk premia exhibit similar responses, providing external validity to this argument. As causal evidence linking large, infrequent macro-shocks to credit risk premia is scarce and recent work mostly focuses on the real effects of credit market fluctuations, our findings contribute to understanding the joint dynamics of credit markets and the macroeconomy.

Learning about Regime Change

Total factor productivity (TFP) and investment specific technology (IST) growth both exhibit regime switching behavior, but the regime at any given time is difficult to infer. We build a rational expectations real business cycle model where the underlying TFP and IST regimes are unobserved. We develop a general perturbation solution algorithm for a wide class of models with unobserved regime-switching. Using our method, we show learning about regime-switching fits the data, affects the responses to regime shifts and intra-regime shocks, increases asymmetries in the responses, generates forecast error bias even with rational agents, and raises the welfare cost of fluctuations.

Observed expectations, news shocks, and the business cycle

This paper exploits information from the term structure of survey expectations to identify news shocks in a DSGE model with rational expectations.We estimate a structural business-cycle model with price and wage stickiness. We allow for both unanticipated and anticipated components (“news”) in each structural disturbance: neutral and investment-specific technology shocks, government spending shocks, risk premium, price and wage markup shocks, and monetary policy shocks.We show that the estimation of a standard DSGE model with realized data obfuscates the identification of news shocks and yields weakly or non-identified parameters pertaining to such shocks. The identification of news shocks greatly improves when we re-estimate the model using data on observed expectations regarding future output, consumption, investment, government spending, inflation, and interest rates - at horizons ranging from one-period to five-periods ahead.The news series thus obtained largely differ from their counterparts that are estimated using only data on realized variables. Moreover, the results suggest that the identified news shocks explain a sizable portion of aggregate fluctuations. News about investment-specific technology and risk premium shocks play the largest role, followed by news about labor supply (wage markup) and monetary policy.

The Effects and Origins of House Price Uncertainty Shocks

This paper investigates the effects of house price uncertainty shocks on economic activity, and traces the origins of the shocks. A Markov-switching vector autoregression (MS-VAR) model shows that house price uncertainty shocks in expansionary regimes increase residential investment, housing prices, and mortgage debt, while they have the opposite effects in recessionary regimes. These empirical results are investigated in an estimated New-Keynesian dynamic stochastic general equilibrium (DSGE) model with a housing sector that allows for multiple structural uncertainty shocks. We show that uncertainty shocks to housing preference and the inflation target are the main sources of house price uncertainty shocks. Uncertainty shocks to investment-specific technology and the inflation target can reproduce the empirical impulse responses in recessionary regimes from the MS-VAR. By contrast, the responses to housing preference uncertainty shocks are consistent with the empirical impulse responses in expansionary regimes. House price uncertainty generated by these structural uncertainty shocks affects the housing market via both housing demand and real-options channels.