Which Sectors Hold the Key to India\u27s Future Economic Growth?

This paper explores sectors that hold the key to India\u27s future economic growth. The forecasts of economic growth from an autoregressive distributed lag model with stock market sectoral indices are analyzed against the benchmark autoregressive model forecasts across several forecast horizons. Results show that the information from sectoral indices improves forecasts of economic growth. However, the forecast superiority is not uniform across sectors and horizons. Auto, consumers\u27 spending, material, metal, oil and gas, and realty sectors provide the most forecasting gains. In contrast, bankex, capital goods, finance, and industrial sectors provide superior forecasts only at horizons above one year. FMCG and health-care provide the least incremental information in predicting economic growth. The highest forecasting gains at a short, medium and long horizons are found in the case of energy (30\%), consumer spending (16\%), and auto (10\%) sector, respectively

High-frequency impact of monetary policy and macroeconomic surprises on US MSAs and aggregate US housing returns and volatility : a GJR-GARCH approach

This paper explores the impact of monetary policy and macroeconomic surprises on the U.S housing market returns and volatility at the Metropolitan Statistical Area (MSA) and aggregate level using a Glosten–Jagannathan-Runkle generalized autoregressive conditional heteroscedasticity (GJR-GARCH) model. Using daily data and sampling periods which cover both the conventional and unconventional monetary policy periods, empirical results show that monetary policy surprises have a greater impact on the volatility of housing market returns across time with particularly pronounced effect during the conventional monetary policy period. We also show that macroeconomic surprises do not have a significant impact on housing returns for most MSAs for the full sample, conventional and unconventional monetary policy periods.http://journal.asia.edu.tw/ADShj2019Economic

Predicting housing market sentiment : the role of financial, macroeconomic and real estate uncertainties

Sentiment indicators have long been closely monitored by economic forecasters, notably to predict short-term moves in consumption and investment. Recently, housing sentiment indices have been developed to forecast housing market developments. Sentiment indices partly reflect economic determinants, but also more subjective factors, thereby adding information, particularly in periods of uncertainty, when economic relations are less stable than usual. While many studies have investigated the relevance of sentiment indicators for forecasting, few have looked at the factors which shape sentiment. In this paper, we investigate the role of different types of uncertainty in predicting housing sentiment, controlling for a wide set of economic and financial factors. We use a dynamic model averaging/selection (DMA/DMS) approach to assess the relevance of uncertainty and other factors in forecasting housing sentiment at different points in time. We find that housing sentiment forecast errors from models incorporating uncertainty measures are up to 40% lower at a two-year horizon, compared with models ignoring uncertainty. We also show, by examining DMS posterior inclusion probabilities, that uncertainty has become more relevant since the 2008 global financial crisis, especially at longer forecast horizons.https://www.tandfonline.com/loi/hbhf20hj2022Economic

Information spillover across international real estate investment trusts : evidence from an entropy-based network analysis

In this study, we unveil information spillover between international real estate markets using an entropy-based network approach for real estate investment trusts (REIT). Our novel approach is simple and yet flexible enough to accommodate the nature and extent of information spillover among several components of the global housing network. For a network of nine leading industrial economies, we unveil static and time-varying information spillover of REIT returns using total transfer entropy, pairwise net transfer entropy and directional (“From”, “To”) transfer entropy. Evidence suggests that the greatest pairwise transfer entropy is from the US to Australia, whereas France, the Netherlands, New Zealand and Singapore are the largest information recipients in the network. The time-varying evolution of total transfer entropy also exhibits a declining trend for the integration of global housing market during our sample period.The first author acknowledged the supports from the National Natural Science Foundation of China under Grant No. 71774152, No. 91546109 and Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant: Y7X0231505).http://www.elsevier.com/locate/ecofin2019-11-01hj2018Economic

125 ​Years of time-varying effects of fiscal policy on financial markets

This paper examines the effect of fiscal policy on financial markets over a long span of 125 years. Unlike existing studies that mainly focus on monetary policy shocks and model-based identification of fiscal policy shocks, we use a time-varying parameter model to study the effect of fiscal policy with much cleaner and direct identification of fiscal policy shocks. In addition, we extend our analysis by measuring the response volatility in these markets and separately study the effects of good and bad components of volatility. We find significant time-variation in the response of stock and bond market returns and volatility. The overall response of the stock market exceeds that of bond markets, with more pronounced effects in the pre-1950 period than in the last six decades. Fiscal consolidation generates long-term benefits that positively affect financial markets in the latter part of the 20th century, thus providing new insights into the dynamic role of fiscal policy.http://www.elsevier.com/locate/iref2021-11-01hj2020Economic

The non-linear response of US state-level tradable and non-tradable inflation to oil shocks : the role of oil-dependence

This paper investigates the effects of oil supply, oil-specific consumption demand, oil inventory demand shocks, and global economic activity shocks on state-level tradable and non-tradable inflation in the US. We use oil shock data following the work of Baumeister and Hamilton (2019) and estimate both linear and non-linear impulse responses using a lag-augmented local projections model in a panel context. Our results from a linear model show that both supply and demand-side oil shocks have a statistically significant impact on both types of inflation. While supply, global economic activity, and demand shocks have a greater impact on tradable inflation, non-tradable inflation responds more strongly to inventory shocks. Further, the non-linear model results provide evidence of heterogeneity in the magnitude and persistence of impact between high- and low-oil dependence regimes. Non-tradable inflation is more sensitive to nearly all components of oil price shocks in the high-oil dependence regime.The National Natural Science Foundation of China.https://www.elsevier.com/locate/ribafhj2023Economic

Machine learning predictions of housing market synchronization across US States : the role of uncertainty

We analyze the role of macroeconomic uncertainty in predicting synchronization in housing price movements across all the United States (US) states plus District of Columbia (DC). We first use a Bayesian dynamic factor model to decompose the house price movements into a national, four regional (Northeast, South, Midwest, and West), and state-specific factors. We then study the ability of macroeconomic uncertainty in forecasting the comovements in housing prices, by controlling for a wide-array of predictors, such as factors derived from a large macroeconomic dataset, oil shocks, and financial market-related uncertainties. To accommodate for multiple predictors and nonlinearities, we take a machine learning approach of random forests. Our results provide strong evidence of forecastability of the national house price factor based on the information content of macroeconomic uncertainties over and above the other predictors. This result also carries over, albeit by a varying degree, to the factors associated with the four census regions, and the overall house price growth of the US economy. Moreover, macroeconomic uncertainty is found to have predictive content for (stochastic) volatility of the national factor and aggregate US house price. Our results have important implications for policymakers and investors.The German Science Foundation.http://link.springer.com/journal/11146hj2022Economic

Is the future really observable? A practical approach to model monetary policy rules

We take a practical approach to model the forward-looking monetary policy rule. Unlike existing studies, we recognize that the forward-looking components—future inflation and output growth—are intrinsically unobserved at the time policy formulation. Using the unobserved components framework, we extract the latent components of the policy rule from the short-term and long-term Greenbook forecasts, both individually and in combination, and jointly estimate the policy parameters. We also consider correlations between different components and combine the forecasts from the survey of professional forecasters and the inflation index bonds market. Evidence suggests that the Federal Reserve follows an inflation-tilted policy rule and the long-term state of economy gets a higher weight than the short term. Also, the policy reaction is more aggressive when interconnections between different components of the policy rule are considered

Modeling House Price Synchronization across the U.S. States and their Time-Varying Macroeconomic Linkages

This paper analyzes the time-varying impact of macroeconomic forces on the synchronization in housing movements across all the U.S. states. Using a Bayesian modeling approach, the house price movements are decomposed into national, regional and state-specific factors. We then analyze the time-varying impact of macroeconomic forces on these national and regional factors. Evidence suggests that in several Western and Eastern states the house price variations are dominated by the national factor, whereas the regional factor dominates the Southern and Midwestern markets. These factors are found to have a time-varying relationship with most macroeconomic indicators with particularly pronounced time-variation caused by national house prices, inflation rate and consumer sentiments