THAI BUSINESS CYCLE FROM MACROECONOMIC MODEL USING BVAR AND MS-BVAR METHODS

This study aims to determine the impact of important components of Thai business cycle during prosperity and depression phases. The BVAR and MS-BVAR models are used to analyze the relationship of each variable. The variables consist of population, GDP, inflation, balance of payments, government cash balance, interest rate, and exchange rate. The data correlated in this study are secondary data during 1979 to 2014 obtained from various sources including World Bank World Development Indicators and the Global Development Finance database, World Resources Institutes (WRI), and Bank of Thailand (BOT). The results of this study indicate that each variable in this model has statistical significant relationship. From the analysis, each variable has different impact on Thai business cycle during prosperity and depression phases.&nbsp

Modelling and Forecasting Tourist Arrivals to Cambodia: An Application of ARIMA-GARCH Approach

The paper aims at estimating and forecasting international tourist arrivals to Cambodia during the time interval of 2000m1 to 2017m7, covering 209 of monthly observations. To find out factors affecting tourist arrivals, simple OLS and 2SLS with instrument variable regression are applied, on the one hand. On the other hand, several time series models of ARIMA (p, d, q), GARCH (s, r) and the hybrid of ARIMA(p, d, q)-GARCH(s, r) are employed to forecast tourist arrivals in line with AIC and BIC in selecting the best modified models. The empirical results primarily reveal that tourist arrivals are affected by exogenous factor, say exchange rate, dummy factors such as the AEC, global finical crisis, national election and Cambodia’s e-Visa. With regard to forecasting stage, the result indicates that tourist arrivals are shocked by time trend in the past period, say time (t-1). The trend is furthermore reduced due to the time lags, say time (t-2, t-3) as shown in the parameter coefficients of AR. GARCH (1, 1) model suggests that the short run persistence of shocks lies in the gap of 0.04 whereas the long run persistence lies in the gap of 0.94. Additionally, AIC and BIC propose that ARIMA(3, 1, 4) and the hybrid of ARIMA(3, 1, 4)-GARCH (1, 1) are the best model to predict the future value of tourist arrivals. The RMSE and U index obtained from measurement predictive accuracy reveal that long run 1-step ahead forecasting of 2013m12 to 2017m7 is produced the smallest predictive error amongst the others. Thus, it has more predictive power to apply long term ex-ante forecasting

Modelling and Forecasting Tourist Arrivals to Cambodia: An Application of ARIMA-GARCH Approach

The paper aims at estimating and forecasting international tourist arrivals to Cambodia during the time interval of 2000m1 to 2017m7, covering 209 of monthly observations. To find out factors affecting tourist arrivals, simple OLS and 2SLS with instrument variable regression are applied, on the one hand. On the other hand, several time series models of ARIMA (p, d, q), GARCH (s, r) and the hybrid of ARIMA(p, d, q)-GARCH(s, r) are employed to forecast tourist arrivals in line with AIC and BIC in selecting the best modified models. The empirical results primarily reveal that tourist arrivals are affected by exogenous factor, say exchange rate, dummy factors such as the AEC, global finical crisis, national election and Cambodia’s e-Visa. With regard to forecasting stage, the result indicates that tourist arrivals are shocked by time trend in the past period, say time (t-1). The trend is furthermore reduced due to the time lags, say time (t-2, t-3) as shown in the parameter coefficients of AR. GARCH (1, 1) model suggests that the short run persistence of shocks lies in the gap of 0.04 whereas the long run persistence lies in the gap of 0.94. Additionally, AIC and BIC propose that ARIMA(3, 1, 4) and the hybrid of ARIMA(3, 1, 4)-GARCH (1, 1) are the best model to predict the future value of tourist arrivals. The RMSE and U index obtained from measurement predictive accuracy reveal that long run 1-step ahead forecasting of 2013m12 to 2017m7 is produced the smallest predictive error amongst the others. Thus, it has more predictive power to apply long term ex-ante forecasting

THE LONG MEMORY PROPERTY OF HUNGARIAN MARKET PIG PRICES: A COMPARISON OF THREE DIFFERENT METHODS

The present study investigates the long memory property of market pig prices. Simply knowing that these time series have long term dependence could have strong significance when forecasting prices. The presence of long memory is crucial information in making business decisions and creating portfolios. Long memory can be measured by calculating the so-called Hurst exponent. In our article, we studied and described three different methods (Rescaled range, Detrended Fluctuation Analysis, Autoregressive Fractionally Integrated Moving Average). Data consist of four time series (piglet, young pig, sow, slaughter pig) between 1991 and 2011. Before conducting the econometric analysis, all the series were seasonally adjusted using the TRAMO/SEATS method. Data preparation was followed by differencing the time series and testing their normality and stationarity. In the next step, we divided the analysed period into four parts and determined the Hurst exponent for each sub-period, using all three methods. In summary, results showed that slaughter pig prices are random; pig and piglet prices developed similarly and have long memory, while sow price changes definitely have short memory. Among the methods of pinpointing long term memory, ARFIMA was used for making the forecast. The forecasting ability of the method was compared to the traditional ARIMA model, with ARFIMA proving to be the better of the two

Relationships between Effective and Expected Interest Rates as a Transmission Mechanism for Monetary Policy: Evidence on the Brazilian Economy Using MS-models and a Bayesian VAR

AbstractThis work applies Markov-switching models and a Bayesian VAR in order to verify empirical relationships between expected and effective short term interest rates in Brazil. The main results corroborate the theoretical idea according to which the Central Bank can smooth adjustments of effective short term interest rates, given that these last ones have effects on expected short term rates, thereby influencing long term interest rates, which are fundamental for controlling output activity and price changes. Besides, the MS-models show that these empirical relationships are more significant under a “higher response regime”. At last, the BVAR test yields impulse-response functions showing that shocks in expected rates have more persistent impacts on effective rates than what is observed from the opposite direction. This evidence gives support for the idea of a transparent and predictable monetary policy in Brazil

Performing a Bayesian VAR to Analyze how Monetary Policy's Credibility is Affected and Affects Over Time: The Brazilian Experience

AbstractThis article measures and analyzes how the monetary policy's credibility is dynamically related to macroeconomic performance in Brazil. Performing a Bayesian VAR with Litterman/Minnesota priors, we obtain results highlighting that monetary policy's credibility gains (and losses) are affected by inflation rate shocks, while the higher such credibility the easier the control of inflationary expectations and thereby taming effective inflation rates becomes a natural result over time. Furthermore, we verified other important new-keynesian predictions for Brazil, such as the pass-through effect, the output-inflation relation (Phillips curve), the interest rate-output one (IS curve), as well as the reaction of such a rate to inflation shocks (Taylor rule). At last, the monetary policy's credibility is negatively affected by an undervaluated domestic currency

Extreme Value Estimators for Stock Indices in ASEAN Economics Community

The paper examines the extremeValue-at-Risk(VaR) model with daily stock indices of selected South EastAsian countries consisting of SET index (Thailand), KLSEindex (Malaysia), FTSI index (Singapore), and JKSE index(Indonesia). Additionally, the experiment based on extremevalue theory (EVT) was conducted to generate extremeVaRestimates at the 99 percent confidence intervals. The paperis tested utilizing Generalized Extreme Value Distribution(GEV) was computed by using negative maximum naturallog of weekly returns with block maxima method on AECmarketindices. And Generalized Pareto Distribution (GPD)estimated by using natural log exceeding value of dailyreturns of stock indices which set threshold limit flooringvalue as specified and computed with threshold method.According to calculated weekly returns of GEV andcalculated natural log of daily returns of GPD on AECmarket indices. The output results indicated that KLSEextremeVaR in Malaysia was the AEC attractive equitymarket when investors invest in these markets