The time-varying and volatile macroeconomic effects of immigration

2021 ◽  
pp. 1-21
Author(s):  
Huachen Li
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Time Varying ◽  
Vector Autoregressions ◽  
Hours Worked ◽  
Supply Shock ◽  
The Us ◽  
Forecast Error Variance ◽  
The Business Cycle ◽  
The Impact

Abstract This paper studies the impact of immigration on the US macroeconomy. I identify structural vector autoregressions (SVARs) with time-varying parameters (TVPs) and stochastic volatility (SV) using a novel set of restrictions. The TVP-SV-SVARs are estimated on a quarterly sample including average labor productivity (ALP), hours worked, immigration, consumption, and term spread from 1953 to 2017. An immigration supply shock increases domestic ALP and hours worked over the business cycle horizons. Movements in immigration are explained by its own shock and to a lesser extent by the productivity and news shocks. IRFs driven by these shocks vary over the sample, especially around changes in immigration policy such as the Immigration Act of 1990. In contrast, the forecast error variance decompositions exhibit little change over the sample. Immigration plays an important role in the US macroeconomy.

scholarly journals Covered Interest Parity Deviations Between India And The US

2011 ◽  
Vol 11 (1) ◽  
pp. 83 ◽  
Author(s):  
Vadhindran K. Rao
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
The United States ◽  
Country Risk ◽  
Forward Premium ◽  
Permanent Shocks ◽  
Forecast Error Variance ◽  
Covered Interest Parity ◽  
Interest Parity ◽  
The Impact

Prior studies have tested Covered Interest Parity (CIP) between India and the United States and found substantial deviations. The main objective of the current study is to econometrically model and explain deviations from CIP. Further, the study contributes to the literature by proposing an approach to testing CIP after allowing for country risk. A preliminary analysis suggests that there are two types of shocks that impact the CIP deviation, also referred to as the Covered Interest Differential (CID): permanent shocks and temporary shocks. The permanent shocks may be interpreted as reflecting a change in the country risk premium and the temporary shocks as reflecting transient effects and disequilibrium. The paper uses a bivariate Vector Autoregression (VAR) approach to model the joint dynamics of the CID and the forward premium, and applies the methodology of Blanchard and Quah (1989) to separate the impact of the two types of shocks. Impulse-Response analysis shows that a one standard deviation permanent shock has an immediate, substantial impact on the CID. However, forecast error variance decomposition reveals that less than 30% of the variability in the CID is caused by such permanent shocks. Further, permanent shocks account for less than 5% of the forecast error variance of the forward premium, which suggests that covered interest arbitrage activity has limited influence on the forward premium. Temporary shocks appear to be related to transient volatility in the forward premium, and such shocks initially affect both the forward premium and the CID to approximately the same extent. The manner in which the CID responds to a temporary shock suggests considerable impediments to arbitrage. However, the fact that the CID recovers at a slightly faster rate than the forward premium, especially in the initial periods, suggests that capital restrictions are not completely binding.

scholarly journals Asymmetric and nonlinear inter-relations of US stock indices

2018 ◽  
Vol 14 (1) ◽  
pp. 78-129 ◽  
Author(s):  
Dimitrios Vortelinos ◽  
Konstantinos Gkillas (Gillas) ◽  
Costas Syriopoulos ◽  
Argyro Svingou
Keyword(s):  
Debt Crisis ◽  
Forecast Error ◽  
Error Variance ◽  
Asset Markets ◽  
Correction Model ◽  
Content Type ◽  
Stock Indices ◽  
The Us ◽  
Forecast Error Variance ◽  
Causality Model

Purpose The purpose of this paper is to examine the inter-relations among the US stock indices. Design/methodology/approach Data of nine US stock indices spanning a period of sixteen years (2000-2015) are employed for this purpose. Asymmetries are examined via an error correction model. Non-linear inter-relations are researched via Breitung’s nonlinear cointegration, a M-G nonlinear causality model, shocks to the forecast error variance, a shock spillover index and an asymmetric VAR-GARCH (VAR-ABEKK) approach. Findings The inter-relations are significant. The results are robust across all types of inter-relations. They are highest in the Lehman Brothers sub-period. Higher stability after the EU debt crisis, enhances independence and growth for the US stock indices. Originality/value To the best of the knowledge, this is the first study to examine the inter-relations of US stock indices. Most studies on inter-relations concentrate on the portfolio analysis to reveal diversification benefits among various asset markets internationally. Hence this study contributes to this literature on the inter-relations of a specific asset market (stock), and in a specific nation (USA). The evident inter-relations support the notion of diversification benefits in the US stock markets.

scholarly journals Inflation in Malaysia

2010 ◽  
Author(s):  
Wong Hock Tsen
Keyword(s):  
Forecast Error ◽  
Price Change ◽  
Impulse Response Function ◽  
Import Price ◽  
Error Variance ◽  
Variance Decomposition ◽  
Oil Price ◽  
Forecast Error Variance ◽  
The Impact

This study examines the determination of inflation in Malaysia. The results of the generalised forecast error variance decomposition show that real import price change is the most important factor in the determination of inflation. The impact of real oil price change on inflation is marginal. An increase in real oil price has a more significant impact on inflation than a decrease in real oil price. The results of the generalised impulse response function show the impact of variables examined on inflation is relatively short. There is evidence that real oil price change Granger causes inflation.  

scholarly journals THE OPENNESS AND ITS IMPACT TO INDONESIAN ECONOMY: A STRUCTURAL VAR APPROACH

2008 ◽  
Vol 10 (3) ◽  
Author(s):  
Iskandar Simorangkir
Keyword(s):  
Economic Performance ◽  
Forecast Error ◽  
Trade Openness ◽  
Error Variance ◽  
Structural Var ◽  
Financial Openness ◽  
Cross Sectional ◽  
Forecast Error Variance ◽  
The Impact ◽  
The Relationship

There have been long running disputes on the relationship between the degree of openness and economic performance. Based on cross-country analyses, a number of studies found that the relationship between openness and economic performance is quite mixed. Some studies discovered a positive relationship, while others found a negative or simply neutral relationship.Unlike previous studies using cross-sectional data, this study uses structural vector auto-regression (SVAR) to explore the impact of trade openness and financial openness on the Indonesian economy. The findings shows that trade openness and financial openness have negative impacts on output. The results of trade openness are quite robust; since a lack of preparation to anticipate trade openness weakens the competitiveness of Indonesian products relative to foreign products and, finally, lower output. The findings of financial openness are also robust because greater financial openness leaves the Indonesian economy more vulnerable to capital reversal, which endangers economic performance.Keywords: Openness, SVAR, forecast error variance decomposition, impulse response function.JEL Classification: F41, F43

scholarly journals Identification and inference with ranking restrictions

2021 ◽  
Vol 12 (1) ◽  
pp. 1-39
Author(s):  
Pooyan Amir-Ahmadi ◽  
Thorsten Drautzburg
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Vector Autoregressions ◽  
News Shocks ◽  
Sign Restrictions ◽  
Forecast Error Variance ◽  
Variance Decompositions ◽  
One Year ◽  
The One ◽  
Fully Bayesian

We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications.

scholarly journals Eurozone or national inflation projections: Which has greater impact on consumer expectations?

2020 ◽  
pp. 14-14
Author(s):  
Magdalena Szyszko ◽  
Karolina Tura-Gawron
Keyword(s):  
Central Bank ◽  
Forecast Error ◽  
Probabilistic Method ◽  
Error Variance ◽  
Response Analysis ◽  
Inflation Expectations ◽  
Causality Test ◽  
Consumer Expectations ◽  
Forecast Error Variance ◽  
The Impact

We compare the dependence of consumer inflation expectations on European Central Bank (ECB) inflation projections with that on national central bank (NCB) projections in four economies: Austria, Belgium, Finland, and Germany. We aim to assess whether the information published by central banks affects consumers, and whether inflation projections published by NCBs are more relevant to consumers than those published for the entire Eurozone. Inflation expectations were obtained from the Business and Consumer Surveys conducted by the Directorate General for Economic and Financial Affairs of the European Commission and quantified using the probabilistic method. The methodology covers: (1) forecast encompassing tests, (2) the Granger causality test, and (3) impulse response analysis complemented by (4) forecast error variance decomposition. The results suggest that the ECB outlook constitutes a more important factor in expectation formation. This article adds to the existing literature by comparing the impact of common and national projections on consumer expectations.

scholarly journals Tfp, News, and “Sentiments”: the International Transmission of Business Cycles

2018 ◽  
Vol 18 (1) ◽  
pp. 302-341 ◽  
Author(s):  
Andrei A Levchenko ◽  
Nitya Pandalai-Nayar
Keyword(s):  
United States ◽  
Business Cycle ◽  
Forecast Error ◽  
Error Variance ◽  
The United States ◽  
International Transmission ◽  
The Us ◽  
Forecast Error Variance ◽  
Short Run ◽  
Positive Shock

Abstract We propose a novel identification scheme for a nontechnology business cycle shock, which we label “sentiment”. This is a shock orthogonal to identified surprise and news TFP shocks that maximize the short-run forecast error variance of an expectational variable, alternatively a GDP forecast or a consumer confidence index. We then estimate the international transmission of three identified shocks—surprise TFP, news of future TFP, and sentiment—from the United States to Canada. The US sentiment shock produces a business cycle in the United States, with output, hours, and consumption rising following a positive shock, and accounts for the bulk of the US short-run business cycle fluctuations. The sentiment shock also has a significant impact on Canadian macroaggregates. In the short run, it is more important than either the surprise or the news TFP shocks in generating business cycle comovement between the United States and Canada, accounting for over 40% of the forecast error variance of Canadian GDP and over one-third of Canadian hours, imports, and exports. The news shock is responsible for some comovement at 5–10 years, and surprise TFP innovations do not generate synchronization. We provide a simple theoretical framework to illustrate how the US sentiment shocks can transmit to Canada.

scholarly journals Relationship between Technological Advancement and Agricultural Production: Evidence from India

2020 ◽  
Vol 8 (6) ◽  
pp. 2088-2094
Keyword(s):  
Agricultural Production ◽  
Forecast Error ◽  
Error Variance ◽  
Technological Advancement ◽  
Food Grains ◽  
Long Run ◽  
Forecast Error Variance ◽  
Short Run ◽  
Impact Of Technology ◽  
The Impact

Indian economy has more than 60% of the work force engaged in it and with the sectoral contribution of 17- 18% to country's GDP in 2018-19. Despite such heavy dependence and high significance of the agricultural sector, per capita productivity in agriculture over the past few decades is less in comparison to the productivity in other sectors. Available statistics shows that agricultural production has rose marginally during the period of green revolution (starting in 1960s) which was driven by the technology revolution. Technology revolution here means- ‘seed-fertiliser-water technology’ or modern technology. In the present study, a detailed time series analysis for a time period of 36 years (1981-2017) is made to study the impact of technology in production in both the short run and long run. Firstly, the present status of technology use is studied and secondly a crop-output model is considered depicting the role of technology in production of India. Here, the impact of technology is measured using variables such as gross irrigated area, Pesticide use, Synthetic nitrogen fertilizer (NPK) uses, use of improved seed varieties (HYVs) etc. and their impact upon agricultural production (food grains as well as non-food grains) is tested using various econometric tests such as Johansen Co-integration test, regression estimates etc. A composite index has been constructed using PCA method as a proxy to technology. To examine the linkage between technological advancement and agricultural production in India, we employed the Vector auto regression (VAR) model proposed by Sims. To draw inferences on the results of VAR, we also used forecast error variance decomposition (FEVD) which gives both short-run impact and long-run impact of each variable in explaining the forecast error variance of the dependent variable.

scholarly journals Examining the Relationship between Diffrent Modes of Infrastructure Financing and Economic Growth in India

2019 ◽  
Vol 8 (4) ◽  
pp. 8677-8684
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Forecast Error ◽  
Error Variance ◽  
Public And Private ◽  
Vector Error Correction ◽  
Dynamic Relationship ◽  
Vector Error ◽  
Forecast Error Variance ◽  
The Impact

With an aim to achieve a status of 5 trillion economy, India has to fulfil the criteria of achieving minimum 9%+ growth rate consistently for next five years. But at present, the economic indicators of India reflect a dismal picture to achieve that goal. The economic growth rate of India has gone down to almost five percent in first quarter of financial year 2019-20. Since the opening up of the Indian economy in 1991, the role of private sector in reviving the country’s growth cannot be overstated. Expanding investment in infrastructure is often projected as a weapon which can play a counter cyclical role in the phase of such economic crisis. In an attempt to analyse the impact different modes of investment in infrastructure on economic growth of India, this paper examines the trend of investments by private as well as both public and private (joint) since 1990s. Further, a time series econometric analysis is carried out for a period of twenty-eight years (1990-2018) wherein the nexus between investments (primarily in transportation and energy sector) and economic growth of India (GDP per capita) is examined. To examine the dynamic relationship between the variables, their causality, exogeneity and comparability, the Vector auto regression (VAR) model, along with the Forecast Error Variance Decomposition (FEVD) and Vector Error Correction Model (VECM) is used. The results of VAR and VECM suggests that there is significant impact of investment in infrastructure upon economic growth of India.

scholarly journals Monte Carlo Analysis of Forecast Error Variance Decompositions under Alternative Model Identification Schemes

2018 ◽  
Vol 5 (338) ◽  
pp. 115-131
Author(s):  
Anna Staszewska-Bystrova
Keyword(s):  
Monte Carlo ◽  
Forecast Error ◽  
Model Identification ◽  
Error Variance ◽  
Estimation Errors ◽  
Identification Schemes ◽  
Long Run ◽  
Forecast Error Variance ◽  
Short Run ◽  
The Impact

The goal of the paper is to investigate the estimation precision of forecast error variance decomposition (FEVD) based on stable structural vector autoregressive models identified using short‑run and long‑run restrictions. The analysis is performed by means of Monte Carlo experiments. It is demonstrated that for processes with roots close to one, selected FEVD parameters can be esti­mated more accurately using recursive restrictions on the long‑run multipliers than under recursive restrictions on the impact effects of shocks. This finding contributes to the discussion of pros and cons of using alternative identification schemes by providing counterexamples for the notion that short‑run identifying restrictions lead to smaller estimation errors than long‑run restrictions.

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