Browsing by Author "Wagala, A."

Now showing 1 - 5 of 5

Evaluating the Predictive Ability of Seasonal Autoregressive Integrated Moving Average (SARIMA) Models When Applied to Food and Beverages Price Index in Kenya
(2022) Wanjuki, T. M.; Muriithi, D. K.; Wagala, A.
Price instability has been a major concern in most economies. Kenya's commodity markets have been characterized by high price volatility affecting investment and consumer behaviour due to uncertainty on future prices. Therefore, precise forecasting models can help consumers plan for their expenditure and government policymakers formulate price control measures. Due to the seasonality of Kenya's food and beverage price indices, the current study postulates that the Seasonal Autoregressive Integrated Moving Average (SARIMA) model can best be the best fit model for the data. The study used secondary data on Kenya's monthly food and beverage prices index from January 1991 to February 2020 to examine the predictive ability of the possible SARIMA models based on the minimization of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The cross- validation test results of 6, 12, 18, 24, 30, and 36 step-ahead forecasts demonstrated that SARIMA models are unstable for use in forecasting over a long-time period with a tendency of increasing prediction errors with an increase in the forecast period. It is anticipated that the findings of the current study will provide necessary valuable information to the policymakers and stakeholders to understand future trends in commodity price.
Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models.
(2021) Wanjuki, T. M.; Muriithi, D. K.; Wagala, A.
Price stability is the primary monetary policy objective in any economy since it protects the interests of both consumers and producers. As a result, forecasting is a common practice and a vital aspect of monetary policymaking. Future predictions guide monetary and fiscal policy tools that that be used to stabilize commodity prices. As a result, developing an accurate and precise forecasting model is critical. The current study fitted and forecasted the food and beverages price index (FBPI) in Kenya using seasonal autoregressive integrated moving average (SARIMA) models. Unlike other ARIMA models like the autoregressive (AR), Moving Average (MA), and non-seasonal ARMA models, the SARIMA model accounts for the seasonal component in a given time series data better forecasts. The study relied on secondary data obtained from the KNBS website on monthly food and beverage price index in Kenya from January 1991 to February 2020. R-statistical software was used to analyze the data. The parameter estimation was done using the Maximum Likelihood Estimation method. Competing SARIMA models were compared using the Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE),.and Mean Absolute Percentage Error (MAPE). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The forecasting ability evaluation statistics MAE = 2.00%, MAPE = 1.62% and MASE= 0.87%. The 24-step ahead forecasts showed that the FPBI is unstable with an overall increasing trend. Therefore, the monetary policy committee ought to control inflation through monetary or fiscal policy, strengthening food security and trade liberalization.
Foreign Exchange Rate Volatility and Its Effect on International Trade in Kenya
(2022) Titus, O. M.; Muriithi, D. K.; Wagala, A.
Exchange rate volatility has received much attention in economic research especially with the advent of floating exchange regimes. The volatile nature of exchange rate is generally perceived as having negative affect on international trade. However, the theoretical and empirical perspective are mixed on the nature of the relationship. This study aimed at examining analyzing the moderating effect of foreign exchange reserves on the relationship between foreign exchange rate volatility and international trade in Kenya. The study used error correction model in the analysis of the time series data for the study period which spanned between 1966-2018. Results show that controlling for inflation rate, interest rate and gross domestic product, foreign exchange reserves had a positive and statistically significant moderating effect at 5% significant level on the relationship between foreign exchange rate volatility and international trade with R2 of 0.9557. The study recommends maintaining enough stock of foreign exchange reserves to cushion the economy from adverse effects of exchange rate volatility. The findings of the study will provide relevant information in the formulation of and implementation of an effective monetary policy that will promote exchange rate stability and improve the country’s performance in international trade.
Singular Spectrum Analysis: An Application to Kenya’s Industrial Inputs Price Index
(EJ-MATH, European Journal of Mathematics and Statistics., 2022) Emmanuel, K. K.; Wagala, A.; Muriithi, D. K.
Time series modeling and forecasting techniques serve as gauging tools to understand the time-related properties of a given time series and its future course. Most financial and economic time series data do not meet the restrictive assumptions of normality, linearity, and stationarity of the observed data, limiting the application of classical models without data transformation. As non-parametric methods, Singular Spectrum Analysis (SSA) is data- adaptive; hence do not necessarily consider these restrictive assumptions as in classical methods. The current study employed a longitudinal research design to evaluate how SSA fist Kenya’s monthly industrial inputs price index from January 1992 to April 2022. Since 2018, reducing the costs of industrial inputs has been one of Kenya’s manufacturing agendas to level the playing field and foster Kenya’s manufacturing sector. It was expected that Kenya’s Manufacturing Value Added hit a tune of 22% by 2022. The study results showed that the SSA (L = 12, r =7) (MAPE = 0.707%) provides more reliable forecasts. The 24-period forecasts showed that the industrial inputs price index remains high above the index in 2017 before the post-industrial agenda targeting a reduction in the cost of industrial inputs. Thus, the industrial input prices should be reduced to a sustainable level.
Vector Error Correction Model: Prediction of Bi-Directional Causality between Gross Domestic Product and Wage Growth in Kenya
(European Journal of Mathematics and Statistics, 2022) Njoroge, M. S.; Njoroge, G. G.; Wagala, A.
Economic growth and wage growth are very prominent macroeconomic variables in all countries in the World. These two variables are the main signposts signaling the current trends in an economy. To determine the recent behavior of the economy, the government must study and analyze these major variables. The increase in aggregate production in the Kenyan economy has been deteriorating due to the steady rise in the wage bill, especially since the year 2012, in conjunction with the devolved government. An increase in wage rate motivates workers and, in turn, increases the production capacity of a country hence economic growth. An increase in recurrent expenditure implies that the development expenditure will be condensed, which will alter the growth of the economy. The primary goal of this research was to fit vector error correction model on gross domestic product and wage growth data so as to identify the bidirectional causality effects between the two variables. VEC model is superior since it distinguishes between long run and short run relationship among underlying variables in a large sample size. The linear Granger causality test was used to evaluate the causal relationship between the system's variables; hence a causal research design was adopted in this study. This research employed secondary data, which was analyzed using Eviews and STATA statistical software. Data on these target variables was acquired from the World Bank and Central Bank of Kenya. Lastly, this study used yearly time series data for the period 1979 to 2019. It was found that wage growth and GDP granger causes each other and also have a long run relationship since their respective p values were less than 5% significance level. VECM1 (effects of wage growth on GDP) had AIC of -0.2953, RMSE of 1.0039 while the R-squared was 0.7241. Subsequently, effects of GDP on wage growth (VECM2) was found to have an R-squared of 0.7452, AIC of -8.2270 and RMSE of 0.08363. Based on the foregoing findings, it was determined that GDP has more influence on wage growth both in the short and long run. This study thus recommends that the government should keep inflation under control, increase development expenditure to finance projects and fostering a favorable business environment for Small and Medium-sized Enterprises (SMEs) to upsurge total output (productivity) which will in turn, lead to a rise in wage growth, thus a high standard of living for the millions of unemployed Kenyans. Finally, the findings of the current study are expected to be of significance to academicians and also provide appropriate policy options that will help in harmonizing the wage rates, thus managing recurrent expenditure in Kenya.