Recoverability and Expectations-Driven Fluctuations

2021 ◽  
Author(s):  
Ryan Chahrour ◽  
Kyle Jurado
Keyword(s):  
Economic Theory ◽  
Time Series ◽  
Business Cycles ◽  
Restrictive Condition ◽  
Vector Autoregressive ◽  
Future Technology ◽  
Shifting Attention

Abstract Time series methods for identifying structural economic disturbances often require disturbances to satisfy technical conditions that can be inconsistent with economic theory. We propose replacing these conditions with a less restrictive condition called recoverability, which only requires that the disturbances can be inferred from the observable variables. As an application, we show how shifting attention to recoverability makes it possible to construct new identifying restrictions for technological and expectational disturbances. In a vector autoregressive example using postwar U.S. data, these restrictions imply that independent disturbances to expectations about future technology are a major driver of business cycles.

scholarly journals Wage Posting and Business Cycles

2016 ◽  
Vol 106 (5) ◽  
pp. 208-213 ◽  
Author(s):  
Giuseppe Moscarini ◽  
Fabien Postel-Vinay
Keyword(s):  
Time Series ◽  
Business Cycles ◽  
Unemployment Rate ◽  
Job Search ◽  
Wage Growth ◽  
Program Participation ◽  
Average Wage ◽  
Micro Data ◽  
Natural Connection ◽  
Monetary Authorities

The canonical model of job search and wage posting (Burdett and Mortensen, 1998) establishes a natural connection between the average wage growth in the economy and the pace of Employer-to-Employer (EE) transitions, predicting wage growth to be positively related to the pace of EE reallocation for all workers, but especially for stayers. We verify this empirically both with aggregate time series and with longitudinal micro data from the Survey of Income and Program Participation (SIPP). We argue that monetary authorities concerned with inflationary wage pressure should pay more attention directly to EE reallocation and less to the unemployment rate.

scholarly journals Business Cycles in Algerian Economy: A Bayesian DSGE Approach

2018 ◽  
Vol 2 (1) ◽  
pp. 72-100
Author(s):  
Abdelsalam BOUKHEROUFA
Keyword(s):  
Time Series ◽  
Business Cycles ◽  
Business Cycle ◽  
Equilibrium Model ◽  
Government Spending ◽  
Aggregate Demand ◽  
Dynamic Stochastic General Equilibrium ◽  
Business Cycle Fluctuations ◽  
Output Fluctuations ◽  
Dynamic Stochastic

The main objective of this paper is to highlight the most important shocks that drives the business cycles in the Algerian economy. Using Bayesian estimation techniques, we estimate a dynamic stochastic general equilibrium model (DSGE) using four time series of the Algerian macroeconomics. Through this estimated model, which succeeded in capturing the dynamics of the Algerian economy data, we found three main results: First, the main causes of business cycle fluctuations in the Algerian economy are aggregate demand shocks. Second, the of government spending shock play the most important role in output fluctuations. Third, empirical results show evidences of procyclical in government spending policies.

scholarly journals On the Ideality of Filtering Techniques in the Business Cycle Analysis Under Conditions of European Economy

2015 ◽  
Vol 63 (3) ◽  
pp. 915-926 ◽  
Author(s):  
Ladislava Issever Grochová ◽  
Petr Rozmahel
Keyword(s):  
Time Series ◽  
Business Cycles ◽  
Gain Function ◽  
Monthly Data ◽  
Band Pass ◽  
Ideal Filter ◽  
Production Indices ◽  
The Business Cycle ◽  
High Pass ◽  
Filtering Techniques

The paper deals with the applications and comparison of various filtering techniques, which is an integral part of the business cycles identification and their further analysis. In particular, the paper examines four high-pass and band-pass filters and compares them to an ideal filter. The gain function is estimated and shown by the periodogram to assess the filters’ efficiency in the extraction of undesired periods in the time series. The monthly data of industrial production indices of chosen EU countries are used to analyse the filtering techniques. The results show the Butterworth filter as a high pass and the Christiano-Fitzgerald filter as a band-pass to be closer to an ideal filter than the Hodrick-Prescott and Baxter-King filters.

scholarly journals VECTOR AUTOREGRESSIVE APPROACH AFTER FIRST DIFFERENCING: A TIME SERIES ANALYSIS OF INFLATION AND ITS DETERMINANTS IN TANZANIA

2021 ◽  
pp. 43-56
Author(s):  
Rachel R. Cheti ◽  
Bahati Ilembo
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Gross Domestic Product ◽  
Money Supply ◽  
Inflation Rate ◽  
Negative Impact ◽  
Causal Effect ◽  
Domestic Product ◽  
Series Data ◽  
Vector Autoregressive

The objective of the study was to examine the trend of inflation and its key determinants in Tanzania. We used secondary time series data observed annually from January 1970 to 2020 which are inflation rate, GDP, Exchange rate and money supply. The vector autoregressive (VAR) model was employed for modeling. Augmented Dickey-Fuller test (ADF) found that inflation rate, Gross Domestic Product (GDP), exchange rate and Money supply (M3) were initially non-stationary but they became stationary after first differencing so as to proceed with the analysis. Preliminary tests before obtaining vector auto regressive model were carried out before determining the relationship between the variables. Diagnostic test such as serial correlation, heteroscedasticity, stability and normality were also important to evaluate the model assumptions and investigate whether or not there are observations with a large, undue influence on the analysis. We used Granger causality test (GCT) to determine causal- effect relationship between the variables. The results show that, there is a long run relationship between the variables, also the results showed that exchange rate and money supply (M3) both have a positive impact on inflation rate while gross domestic product (GDP) revealed a negative impact on inflation rate. Finally, the forecast of inflation rate for 15 years ahead was performed. The study recommends that the government should pursue both contractionary monetary policy and fiscal policy in order to control inflation in the country.

scholarly journals A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

2021 ◽  
Vol 12 (5) ◽  
pp. 309-316
Author(s):  
Jae-Hyun Kim, Chang-Ho An
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Prediction Model ◽  
Interest Rates ◽  
Time Series Data ◽  
Information Criteria ◽  
Series Data ◽  
Call Rate ◽  
Causality Test ◽  
Vector Autoregressive

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop.

scholarly journals The time series regression analysis in evaluating the economic impact of COVID-19 cases in Indonesia

2021 ◽  
Vol 16 (3) ◽  
pp. 197-210
Author(s):  
Utriweni Mukhaiyar ◽  
Devina Widyanti ◽  
Sandy Vantika
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Transfer Function Model ◽  
Function Model ◽  
Time Series Regression ◽  
Vector Autoregressive ◽  
Daily Data ◽  
The Impact

This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.

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