REGIME-SWITCHING PRODUCTIVITY GROWTH AND BAYESIAN LEARNING IN REAL BUSINESS CYCLES

2019 ◽  
pp. 1-27
Author(s):  
Sami Alpanda
Keyword(s):  
Incomplete Information ◽  
Productivity Growth ◽  
Regime Switching ◽  
Bayesian Learning ◽  
Projection Methods ◽  
Real Business Cycles ◽  
Productivity Slowdown ◽  
Tfp Growth ◽  
Maximum Likelihood Methods ◽  
The Usa

Growth in total factor productivity (TFP) in the USA has slowed down significantly since the mid-2000s, reminiscent of the productivity slowdown of the 1970s. This paper investigates the implications of a productivity slowdown on macroeconomic variables using a standard real business cycle (RBC) model, extended with regime-switching in trend productivity growth and Bayesian learning regarding the growth regime. I estimate the Markov-switching parameters using US data and maximum-likelihood methods, and compute the model solution using global projection methods. Simulations reveal that, while adding a regime-switching component to the standard RBC setup increases the volatility in the system, further incorporating incomplete information and learning significantly dampens this effect. The dampening is mainly due to the responses of investment and labor in response to a switch in the trend component of TFP growth, which are weaker in the incomplete information case as agents mistakenly place some probability that the observed decline in TFP growth is due to the transient component and not due to a regime switch. The model offers an objective way to infer slowdowns in trend productivity, and suggests that macroeconomic aggregates in the USA are currently close to their potential levels given observed productivity, while counterfactual simulations indicate that the cost of the productivity slowdown to US welfare has been significant.

Productivity growth of Indonesian rice production: sources and efforts to improve performance

2018 ◽  
Vol 67 (9) ◽  
pp. 1792-1815 ◽  
Author(s):  
Joko Mariyono
Keyword(s):  
Technological Change ◽  
Technical Efficiency ◽  
Production Technology ◽  
Productivity Growth ◽  
Scale Effects ◽  
Allocative Efficiency ◽  
Rice Production ◽  
Content Type ◽  
Tfp Growth ◽  
Efficiency Effects

PurposeThe purpose of this paper is to investigate the productivity of rice production by decomposing the growth of total factor productivity (TFP) into four components: technological change, scale effects, technical and allocative efficiencies.Design/methodology/approachThis study employed an econometric approach to decompose TFP growth into four components: technological change, technical efficiency, allocative efficiency and scale effect. Unbalanced panel data used in this study were surveyed in 1994, 2004 and 2014 from 360 rice farming operations. The model used the stochastic frontier transcendental logarithm production technology to estimate the technology parameters.FindingsThe results indicate that the primary sources of TFP growth were technological change and allocative efficiency effects. The contribution of technical efficiency was low because it grew sluggishly.Research limitations/implicationsThis study has several shortcomings, such as very lowR2and the insignificant elasticity of labour presented in the findings. Another limitation is the limited time period panel covering long interval, which resulted in unbalanced data.Practical implicationsThe government should improve productivity growth by allocating more areas for rice production, which enhances the scale and efficiency effects and adjusting the use of capital and material inputs. Extension services should be strengthened to provide farmers with training on improved agronomic technologies. This action will enhance technical efficiency performance and lead to technological progress.Social implicationsAs Indonesian population is still growing at a significant rate and the fact that rice is the primary staple food for Indonesian people, the productivity of rice production should increase continually to ensure social security at a national level.Originality/valueThe productivity growth is decomposed into four components using the transcendental logarithm production technology based on farm-level data. The measure has not been conducted previously in Indonesia, even in rice-producing countries.

Productivity growth, catching-up and technology innovation in microfinance institutions in India: evidence using a bootstrap Malmquist Index approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Asif Khan ◽  
Rachita Gulati
Keyword(s):  
Productivity Growth ◽  
Productivity Index ◽  
Productivity Change ◽  
Microfinance Institutions ◽  
Truncated Regression ◽  
Content Type ◽  
Tfp Growth ◽  
Catching Up ◽  
Double Bootstrap ◽  
Size Groups

PurposeThis paper aims to examine the total factor productivity (TFP) change and its components: efficiency change and technical change in microfinance institutions (MFIs) in India operating from 2005 to 2018. The study also scrutinizes the variations in productivity levels across the distinct organizational form and size groups of MFIs. In addition to this, the authors identify the contextual factors that determine TFP growth, catching-up and technology innovation in MFIs.Design/methodology/approachThe study employs a smooth homogeneous bootstrap estimation procedure of Simar and Wilson (1999) for obtaining reliable estimates of Malmquist indices –productivity and its components – in a data envelopment analysis (DEA) framework for individual MFIs. In order to identify the determinants of productivity change and its components, the study follows Simar and Wilson's (2007) guidelines and applies a bootstrap truncated regression model. The double bootstrap procedure performs well, both in terms of allowing correct estimation of bias and deriving statistically consistent productivity estimates in the first and root mean square errors in the second stage of the analysis.FindingsThe empirical results reveal that the MFIs have shown average productivity growth of 6.70% during the entire study period. The observed productivity gains are primarily contributed by a larger efficiency increase at the rate of 4.80%, while technical progress occurs at 2.3%. Nonbanking financial companies (NBFC)-MFIs outperformed non-NBFC-MFIs. Small MFIs show the highest TFP growth in terms of size groups, followed by the large MFIs and medium MFIs. The bootstrap truncated regression results suggest that the credit portfolio, size and age of MFIs matter in achieving higher productivity levels.Practical implicationsThe practical implication drawn from the study is that the Indian MFI industry might adopt the latest technology and innovations in the products, risk assessment and credit delivery to improve their productivity levels. The industry must focus on enhancing the managerial skill of its employees to achieve a high productivity level.Originality/valueThis study is perhaps the initial attempt to explain the productivity behavior of MFIs in India by deploying a statistically robust double bootstrap procedure in the DEA-based Malmquist Productivity Index (MPI) framework. The authors estimate the bias-adjusted productivity index and its decompositions, which represent more reliable and statistically consistent estimates. For contextual factors responsible for driving productivity change, the study deploys a bootstrap truncated regression approach.

Agricultural Growth Accounting and Total Factor Productivity in Jordan

2020 ◽  
pp. 1709-1723
Author(s):  
Samia Nadeem Akroush ◽  
Boubaker Dhehibi ◽  
Aden Aw-Hassan
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Agricultural Research ◽  
Agricultural Productivity ◽  
Primary Source ◽  
Factor Productivity ◽  
Factors Affecting ◽  
Tfp Growth ◽  
Food And Agriculture ◽  
Food And Agriculture Organization

This article develops new estimates of historical agricultural productivity growth in Jordan. It investigates how public policies such as agricultural research, investment in irrigation capital, and water pricing have contributed to agricultural productivity growth. The Food and Agriculture Organization (FAO) annual time series from 1961 to 2011 of all crops and livestock productions are the primary source for agricultural outputs and inputs used to construct the Törnqvist Index for the case of Jordan. The log-linear form of regression equation was used to examine the relationship between Total Factor Productivity (TFP) growth and different factors affecting TFP growth. The results showed that human capital has positive and direct significant impact on TFP implying that people with longer life expectancy has a significant impact on TFP growth. This article concludes that despite some recent improvement, agricultural productivity growth in Jordan continues to lag behind just about every other region of the world.

scholarly journals The Decline of British Manufacturing, 1973–2012: The Role of Total Factor Productivity

2019 ◽  
Vol 247 ◽  
pp. R19-R31 ◽  
Author(s):  
Richard Harris ◽  
John Moffat
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Intangible Assets ◽  
Factor Productivity ◽  
Tfp Growth ◽  
Manufacturing Productivity ◽  
Plant Level

This paper uses plant-level estimates of total factor productivity covering 40 years to examine what role, if any, productivity has played in the decline of output share and employment in British manufacturing. The results show that TFP growth in British manufacturing was negative between 1973 and 1982, marginally positive between 1982 and 1994 and strongly positive between 1994 and 2012. Poor TFP performance therefore does not appear to be the main cause of the decline of UK manufacturing. Productivity growth decompositions show that, in the latter period, the largest contributions to TFP growth come from foreign-owned plants, industries that are heavily involved in trade, and industries with high levels of intangible assets.

scholarly journals The Procyclical Behavior of Total Factor Productivity in the United States, 1890–2004

2010 ◽  
Vol 70 (2) ◽  
pp. 326-350 ◽  
Author(s):  
Alexander J. Field
Keyword(s):  
United States ◽  
Total Factor Productivity ◽  
Productivity Growth ◽  
The United States ◽  
Real Business Cycle ◽  
Factor Productivity ◽  
Tfp Growth ◽  
Capital Services ◽  
Labor Hoarding ◽  
Statistical Artifact

Between 1890 and 2004 total factor productivity (TFP) growth in the United States has been strongly procyclical, while labor productivity growth has been mildly so. This article argues that these results are not simply a statistical artifact, as Mathew Shapiro and others have argued. Procyclicality resulted principally from demand shocks interacting with capital services which are relatively invariant over the cycle. This account contrasts with explanations emphasizing labor hoarding as well as those offered by the real business cycle (RBC) program, in which TFP shocks (deviations from trend) are themselves the cause of cycles.

scholarly journals Investor Sentiment in an Artificial Limit Order Market

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Lijian Wei ◽  
Lei Shi
Keyword(s):  
Regime Switching ◽  
Trading Volume ◽  
Bayesian Learning ◽  
Investor Sentiment ◽  
Agent Based Modeling ◽  
Order Book ◽  
Limit Order ◽  
Agent Based ◽  
Bid Ask Spread ◽  
Limit Order Market

This paper examines the under/overreaction effect driven by sentiment belief in an artificial limit order market when agents are risk averse and arrive in the market with different time horizons. We employ agent-based modeling to build up an artificial stock market with order book and model a type of sentiment belief display over/underreaction by following a Bayesian learning scheme with a Markov regime switching between conservative bias and representative bias. Simulations show that when compared with classic noise belief without learning, sentiment belief gives rise to short-term intraday return predictability. In particular, under/overreaction trading strategies are profitable under sentiment beliefs, but not under noise belief. Moreover, we find that sentiment belief leads to significantly lower volatility, lower bid-ask spread, and larger order book depth near the best quotes but lower trading volume when compared with noise belief.

scholarly journals Estimating eigenvalues of dynamical systems from time series with applications to predicting cardiac alternans

2012 ◽  
Vol 468 (2147) ◽  
pp. 3649-3666 ◽  
Author(s):  
Adam Petrie ◽  
Xiaopeng Zhao
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Data Driven ◽  
Standard Errors ◽  
Statistical Techniques ◽  
Likelihood Methods ◽  
Maximum Likelihood Methods ◽  
The Usa ◽  
Cardiac Alternans ◽  
The Stability

The stability of a dynamical system can be indicated by eigenvalues of its underlying mathematical model. However, eigenvalue analysis of a complicated system (e.g. the heart) may be extremely difficult because full models may be intractable or unavailable. We develop data-driven statistical techniques, which are independent of any underlying dynamical model, that use principal components and maximum-likelihood methods to estimate the dominant eigenvalues and their standard errors from the time series of one or a few measurable quantities, e.g. transmembrane voltages in cardiac experiments. The techniques are applied to predicting cardiac alternans that is characterized by an eigenvalue approaching −1. Cardiac alternans signals a vulnerability to ventricular fibrillation, the leading cause of death in the USA.

scholarly journals Declining Dynamism, Allocative Efficiency, and the Productivity Slowdown

2017 ◽  
Vol 107 (5) ◽  
pp. 322-326 ◽  
Author(s):  
Ryan A. Decker ◽  
John Haltiwanger ◽  
Ron S. Jarmin ◽  
Javier Miranda
Keyword(s):  
Productivity Growth ◽  
Allocative Efficiency ◽  
High Growth ◽  
Firm Level ◽  
Productivity Slowdown ◽  
Level Data ◽  
Firm Level Data ◽  
Job Reallocation ◽  
Firm Activity ◽  
Growth Distribution

A large literature documents declining measures of business dynamism including high-growth young firm activity and job reallocation. A distinct literature describes a slowdown in the pace of aggregate labor productivity growth. We relate these patterns by studying changes in productivity growth from the late 1990s to the mid 2000s using firm-level data. We find that diminished allocative efficiency gains can account for the productivity slowdown in a manner that interacts with the within-firm productivity growth distribution. The evidence suggests that the decline in dynamism is reason for concern and sheds light on debates about the causes of slowing productivity growth.

China's Growth Slowdown and Prospects for Becoming a High-Income Developed Economy

2017 ◽  
Vol 16 (1) ◽  
pp. 89-113 ◽  
Author(s):  
Ding Lu
Keyword(s):  
Labor Productivity ◽  
Productivity Growth ◽  
Analytical Framework ◽  
Early Observation ◽  
High Income ◽  
Growth Potential ◽  
Labor Productivity Growth ◽  
Tfp Growth ◽  
Moderate Growth ◽  
China’S Growth

After decades of hyper growth, China's economy has slowed significantly in recent years, causing widespread anxiety both within and outside the country. Although economists have not reached a consensus about China's growth potential, it is undeniable that the country has switched gears toward a “new normal” of moderate growth amidst ongoing structural change. To assess China's growth performance and prospects, this study modifies Masahiko Aoki's analytical framework of a unified growth theory into a multi-sector model and applies it to identify the sources of China's per capita income growth in recent decades. The analysis confirms Aoki's early observation that China entered the so-called “Kuznets phase” of development in the 1980s, which then became overlapped by the H-phase, in which human capital–based growth is characterized by high labor productivity growth. This study provides evidence that China's labor productivity growth has been predominantly driven by fixed capital formation. It also reveals that the Kuznets effect (with its labor reallocation effect) has now passed its peak and is fading away. The most alarming finding is that net total factor productivity (TFP) growth in the latest period has slowed to a near halt. This trend is particularly worrisome given that China has exhausted its past demographic dividend and its industrial structure has evolved to the end of industrialization stage. Meanwhile, demographic projections clearly indicate that China has entered what Aoki defined as the development phase of “post demographic transition.” Whether China can reverse the downward trend of TFP growth will determine how soon it can achieve the goal of becoming a high-income developed economy.

Factors Influencing Recent Productivity Growth—Report On a Survey of Companies

1982 ◽  
Vol 101 ◽  
pp. 57-66 ◽  
Author(s):  
G.C. Wenban-Smith
Keyword(s):  
Productivity Growth ◽  
Manufacturing Industries ◽  
Productivity Slowdown ◽  
Factors Influencing ◽  
Half Decade

Last year was marked by what appears to have been an unprecedented improvement in the productivity of manufacturing industries. The previous half-decade was distinguished by a productivity slowdown. This article considers recent movements in productivity at Industrial Order level, and reports the results of a survey, carried out at the end of 1981, on the factors which had been important in determining companies' productivity growth through the seventies.

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